significant predictors of functional status and … · 2016-02-22 · iii developed a clinical and...

Significant Predictors of Functional Status and Complications in

Patients Undergoing Surgery for the Treatment of Cervical Spondylotic

Myelopathy

Lindsay Anne Tetreault

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Institute of Medical Sciences

University of Toronto

© Copyright by Lindsay Tetreault 2015

ii

Significant Predictors of Functional Status and Complications in Patients

Undergoing Surgery for the Treatment of Cervical Spondylotic

Myelopathy

Lindsay Anne Tetreault

Doctor of Philosophy

Institute of Medical Sciences, University of Toronto

2015

Abstract Introduction: Cervical spondylotic myelopathy (CSM) is a degenerative spine disease and the

most common cause of spinal cord dysfunction in adults worldwide. Surgery is increasingly

recommended as the preferred management strategy for these patients as it can effectively

halt neurological progression and improve functional status. In this field, there is an increasing

need to manage patients’ expectations of outcomes and inform them of relative risks and

benefits of their surgical procedure.

Objectives: This thesis aims to identify important predictors of functional outcomes and

perioperative complications in patients undergoing surgery for the treatment of CSM.

Methods: Three systematic reviews of the literature were performed to identify 1) important

clinical predictors of surgical outcome; 2) important imaging predictors of surgical outcome;

and 3) significant clinical and surgical predictors of complications. Two surveys were also

conducted to evaluate what spine professionals believed were the most critical predictors of

functional outcomes and complications. Finally, using prospectively-collected data, we

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developed a clinical and a complications prediction rule to predict functional status at 1-year

follow-up and a patient’s risk of complications.

Results: Patients were more likely to achieve an “optimal outcome” if they were younger; had

milder myelopathy and a shorter duration of symptoms preoperatively; did not smoke; had

fewer and less severe co-morbidities; and did not present with gait dysfunction. Patients were

at a higher risk of perioperative complications if they had a greater number of co-morbidities,

co-existing diabetes, a diagnosis of myelopathy secondary to ossification of a posterior

longitudinal ligament and a longer operative duration.

Conclusions: Our outcomes prediction study provides information that can be used by clinicians

to manage patients’ expectations and counsel concerned patients as to potential treatment

options. Furthermore, the results from this study emphasize the importance of accurately

detecting CSM at a mild disease state and referring these patients for early surgical

consultation. The knowledge gained from our complications study can be used by surgeons to

objectively quantify a patient’s risk of complications and discuss this risk during the surgical

consent process. Furthermore, surgeons should use this information to institute case-specific

preventative plans and to strategize appropriate postoperative care.

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Acknowledgements My experiences in graduate school have been positive, rewarding and humbling. I have

had the opportunity to collaborate with professionals from a wide variety of backgrounds,

present at international neurosurgery and spine conferences and witness evidence-based

medicine first hand.

I have been privileged to work under the supervision of Dr. Michael Fehlings as he is a

leading authority in spinal cord injury and has advanced the field through innovative

translational research. Dr. Fehlings is also an exceptional mentor and was able to lead me on an

appropriate career path, relate to my athletic background and competitiveness and provide the

guidance I needed to achieve my goals. I was always made to feel welcomed and valued in the

lab and encouraged that my work would eventually influence clinical practice. I have been

inspired over the last four years and have developed a new passion for research.

Dr. Fehlings, I am grateful to be a member of your clinical research team. Thank you for

encouraging me to pursue a doctorate degree, for the countless opportunities and for setting

such high standards. I hope to continue this working relationship for years to come.

I would also like to acknowledge several other individuals who have generously shared

their expertise with me and have provided the support I needed to complete this degree. To,

Pierre Côté and Robert Chen for being such positive presences on my Program Advisory Committee and for the valuable input, constructive criticism and methodological guidance you have provided me with over the years. Branko Kopjar for all of the statistical and methodological advice and for challenging me to think creatively. You have taught me the power of collaboration and your suggestions have truly enhanced the quality of my work. Anoushka Singh for giving me several opportunities to expand my knowledge, being my biggest source of support and encouragement and your unconditional friendship. Jeff Wilson for being such a positive role model and for all your helpful advice. Your work is truly remarkable and your modesty, charisma and time management skills are inspirational.

v

Paul Arnold for instilling confidence in me and for your constant motivation, valuable suggestions and collaboration. Marina Englesakis for your help in conducting such brilliant literature searches for my systematic reviews. Andrea Skelly and Joe Dettori for teaching me all I know about systematic reviews and for doing it so patiently. Sherry Peterson, Madeleine O’Higgins and Amy Lem for the administrative support and helping me with my organization. Kevin Beverly for your dedication in preparing and cleaning the datasets for my use. Yuriy Petrenko, Yuliya Petrenko and Natalia Nugaeva for mentoring me during my first six months in the Fehlings’ lab and for your unwavering support and friendship. Anick Nater for being such a positive influence and a constant source of inspiration. Aria Nouri for your commitment, passion for research and collaboration and for providing the data I needed to perform the MRI prediction study. Suhkvinder Kalsi Ryan for providing positive and constructive feedback, for motivating me during my thesis writing and for sharing your clinical expertise. My friends and rowing teammates for giving me balance in my life and for always lifting my spirits. I am also thankful to AOSpine North America and International for their sponsorship and

the co-investigators of the CSM-North America and International studies for conducting such

high quality prospective studies. I am grateful that I was able to use this data for my thesis.

My family has truly done this journey with me. My parents have been exceptionally

supportive of my ambitions and have always taken interest in my work. Thank you both for

teaching me invaluable life lessons, giving me the opportunity to succeed and for your

unconditional love. Jennifer, I have come to realize you are my best friend. You have been such

a positive influence in my life and I know I can always count on you and Brent for advice,

reassurance and motivation.

Lindsay A. Tetreault, 2015

vi

Contributions

Chapter 1

Andrea Skelly conducted the literature search and Han Jo Kim assisted with the writing and

editing of Sections 1.5.3 and 1.5.4

Anoushka Singh assisted in the literature review and writing of Section 1.7

Chapter 3

Marina Englesakis conducted the literature search for systematic review A and C. Joe Detorri

and his team at Spectrum assisted with the literature search, data extraction and rating of the

evidence for systematic review B.

Chapter 5

Co-investigators of the CSM-International and North America study were responsible for

collecting the data used throughout this thesis.

Branko Kopjar and his team cleaned the dataset for analysis.

Chapter 9

Aria Nouri analyzed all magnetic resonance images and collected the data used for the analysis

presented in Chapter 9.

Chapter 11

Gamaliel Tan helped adjudicate each complication as related to surgery, related to myelopathy

or unrelated.

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Table of Contents Abstract ......................................................................................................................................................... ii

Acknowledgements ...................................................................................................................................... iv

Contributions ............................................................................................................................................... vi

List of Tables ............................................................................................................................................... xiv

List of Figures ............................................................................................................................................ xviii

List of Abbreviations ................................................................................................................................... xxi

Preamble ....................................................................................................................................................... 1

Thesis Structure ............................................................................................................................................ 2

Chapter 1: An Overview of Diagnosis, Pathophysiology, Treatment and Assessment Standards for

Cervical Spondylotic Myelopathy.................................................................................................................. 4

1.1 Introduction ........................................................................................................................................ 4

1.2 Anatomy of the Cervical Spine ............................................................................................................ 4

1.3 The Degenerative Process and Pathophysiology ................................................................................ 5

1.4 Prevalence of CSM and Economic Implications of Disease ................................................................ 7

1.5 Current Approaches to CSM Diagnosis ............................................................................................... 7

1.5.1 Clinical Assessment ...................................................................................................................... 7

1.5.2 Imaging Assessment ..................................................................................................................... 8

1.5.3 Electrodiagnosis ........................................................................................................................... 9

1.5.4 Differential Diagnosis ................................................................................................................. 10

1.6 Risk Factors of Disease Diagnosis, Development and Progression ................................................... 15

1.6.1 Clinical Risk Factors .................................................................................................................... 15

1.6.2 MRI Risk Factors ......................................................................................................................... 16

1.6.3 Genetic Risk Factors ................................................................................................................... 20

1.7 Evaluating Functional Status and Quality of Life using Outcome Measures .................................... 20

1.7.1 Validity of Existing Outcome Tools ............................................................................................ 22

1.7.2 Reliability of Existing Outcome Measures ................................................................................. 26

1.7.3 Responsiveness of Existing Outcome Tools ............................................................................... 26

1.8 Management and Treatment Strategies ........................................................................................... 27

1.8.1 Non-operative Management ..................................................................................................... 27

1.8.2 Surgery ....................................................................................................................................... 29

Chapter 2: A Clinical and Complications Prediction Rule in Cervical Spondylotic Myelopathy: Rationale,

Objectives and Specific Aims ...................................................................................................................... 30

viii

2.1 Definition of a Clinical Prediction Rule.............................................................................................. 30

2.2 The APACHE II Score and the Model for End-Stage Liver Disease .................................................... 30

2.3 Clinical Prediction Rules in Traumatic Spinal Cord Injury ................................................................. 31

2.4 Rationale, Objectives and Specific Aims ........................................................................................... 31

2.4.1 Rationale .................................................................................................................................... 31

2.4.2 Knowledge Gaps in the Literature ............................................................................................. 34

2.4.3 Objectives and Specific Aims ..................................................................................................... 35

Chapter 3: Identifying Significant Predictors of Surgical Outcome and Complications: Results from

Systematic Reviews of the Literature ......................................................................................................... 38

3.1 Introduction ...................................................................................................................................... 38

3.2 Overview of Common Methods ........................................................................................................ 40

3.2.1 Eligibility Criteria ........................................................................................................................ 40

3.2.2 Study Characteristics .................................................................................................................. 42

3.2.3 Information Sources ................................................................................................................... 43

3.2.4 Search Strategy .......................................................................................................................... 43

3.2.5 Study Selection ........................................................................................................................... 44

3.2.6 Data Extraction and Synthesis ................................................................................................... 44

3.2.7 Risk of Bias in Individual Studies ................................................................................................ 44

3.2.8 Risk of Bias Across Studies ......................................................................................................... 45

3.2.9 Clinical Recommendations and Consensus Statements ............................................................ 47

3.3 Results Part A: Important Clinical Predictors of Surgical Outcome .................................................. 48

3.3.1 Study Selection ........................................................................................................................... 48


3.3.3 Risk of Bias ................................................................................................................................. 48

3.3.4 Are there clinical factors that can predict surgical outcome? ................................................... 49

3.3.5 Results of studies without multivariate analysis ....................................................................... 58

3.3.6 Evidence Summary ..................................................................................................................... 67

3.3.7 Discussion ................................................................................................................................... 67

3.3.8 Evidence-Based Clinical Recommendations .............................................................................. 71

3.4 Results Part B: Important Imaging Predictors of Surgical Outcome ................................................. 72

3.4.1 Study Selection ........................................................................................................................... 72


3.4.3 Risk of Bias ................................................................................................................................. 73

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3.4.4 Are there anatomic characteristics that can predict outcome? ................................................ 78

3.4.5 Are there cord properties that can predict outcome? .............................................................. 80

3.4.6 Evidence Summary ..................................................................................................................... 84

3.4.7 Discussion ................................................................................................................................... 87

3.4.8 Evidence-Based Clinical Recommendations .............................................................................. 90

3.5 Results Part C: Important Clinical and Surgical Predictors of Complications ................................... 90

3.5.1 Study Selection ........................................................................................................................... 90


3.5.3 Risk of bias ................................................................................................................................. 92

3.5.4 Are there clinical or imaging factors that can predict complications? ...................................... 92

3.5.5 Are there surgical factors that can predict complications? ....................................................... 97

3.5.6 Results of studies without multivariate analysis ....................................................................... 99

3.5.7 Are rates of complications different between surgical interventions or varying techniques? 106

3.5.8 Summary of Evidence .............................................................................................................. 110

3.5.9 Discussion ................................................................................................................................. 122

3.5.10 Evidence-Based Clinical Recommendations .......................................................................... 124

Chapter 4: Surgeons’ Perceptions of Significant Predictors of Surgical Outcome and Complications:

Results from two Surveys of AOSpine International................................................................................. 125

4.1 Introduction .................................................................................................................................... 125

4.2 Overview of Common Methods ...................................................................................................... 126

4.3 Results Part A: Important Clinical and Imaging Predictors of Surgical Outcome ........................... 129

4.3.1 Summary of Respondents ........................................................................................................ 129

4.3.2 Significant Clinical Predictors of Surgical Outcome ................................................................. 130

4.3.3 Significant Imaging Predictors of Surgical Outcome ................................................................ 137

4.3.4 Discussion ................................................................................................................................. 139

4.4 Results Part B: Important Clinical and Surgical Predictors of Complications ................................. 141

4.4.1 Summary of Respondents ........................................................................................................ 141

4.4.2 Complications commonly seen in clinical practice .................................................................. 141

4.4.3 Factors predicting complications ............................................................................................. 143

4.4.4 Significant Clinical Predictors of Complications ....................................................................... 143

4.4.5 Significant Imaging Predictors of Complications ..................................................................... 148

4.4.6 Significant Surgical Predictors of Complications ...................................................................... 149

4.4.7 Discussion ................................................................................................................................. 153

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Chapter 5: An Overview of the AOSpine CSM-North America and International Studies ........................ 159

5.1 Introduction .................................................................................................................................... 159

5.2 Study Design and Inclusion Criteria ................................................................................................ 159

5.3 Surgical Protocol ............................................................................................................................. 161

5.4 Data Collection ................................................................................................................................ 162

5.4.1 Clinical Variables ...................................................................................................................... 162

5.4.2 Imaging Variables ..................................................................................................................... 166

5.4.3 Surgical Variables ..................................................................................................................... 168

5.5 Primary Outcome Measure ............................................................................................................. 169

5.6 Complications .................................................................................................................................. 170

Chapter 6: The Minimal Clinically Important Difference of the modified Japanese Orthopedic Association

Score and Establishing a Cut-Off Point ..................................................................................................... 174

6.1 Introduction .................................................................................................................................... 174

6.2 Methods .......................................................................................................................................... 175

6.2.1 Patient Sample ......................................................................................................................... 175

6.2.2 The MCID of the mJOA ............................................................................................................. 175

6.2.3 MCID translated to a cut-off point ........................................................................................... 177

6.3 Results ............................................................................................................................................. 178

6.3.1 What is the MCID of the mJOA? .............................................................................................. 179

6.3.2 Does the MCID of the mJOA differ based on severity? ........................................................... 180

6.4 MCID translated to a cut-off point .................................................................................................. 183

6.5 Discussion ........................................................................................................................................ 184

6.6 Strengths and Limitations ............................................................................................................... 186

6.7 Conclusion ....................................................................................................................................... 187

Chapter 7: A Clinical Prediction Model to Determine Outcomes in Patients with Cervical Spondylotic

Myelopathy undergoing Surgical Treatment: Data from the Prospective, Multicenter AOSpine North

American Study ......................................................................................................................................... 188

7.1 Introduction .................................................................................................................................... 188

7.2 Methods .......................................................................................................................................... 188

7.2.1 Patient Sample ......................................................................................................................... 188

7.2.2 Statistical Analysis .................................................................................................................... 188

7.2.3 Secondary Analysis ................................................................................................................... 189

7.3 Results ............................................................................................................................................. 189

7.3.1 Patient Sample ......................................................................................................................... 189

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7.3.2 Univariate Analysis ................................................................................................................... 191

7.3.3 Multivariate Analysis ................................................................................................................ 192

7.3.4 Secondary Analysis ................................................................................................................... 194

7.4 Discussion ........................................................................................................................................ 195

7.5 Study Strengths and Limitations ..................................................................................................... 197

7.6 Conclusions ..................................................................................................................................... 198

Chapter 8: A Clinical Prediction Model to Assess Surgical Outcome in Patients with Cervical Spondylotic

Myelopathy: Internal and External Validation using the Prospective Multicenter AOSpine North

American and International Datasets in 743 Patients .............................................................................. 199

8.1 Introduction .................................................................................................................................... 199

8.2 Methods .......................................................................................................................................... 202

8.2.1 Patient Sample ......................................................................................................................... 202


8.3 Results ............................................................................................................................................. 203

8.3.1 Patient Sample ......................................................................................................................... 203

8.3.2 Original Model ......................................................................................................................... 204

8.3.3 Internal Validation ................................................................................................................... 206

8.3.4 External Validation ................................................................................................................... 207

8.4 Discussion ........................................................................................................................................ 210

8.5 Applying the Model ......................................................................................................................... 211

8.5.1 Managing Expectations ............................................................................................................ 212

8.5.2 Counseling Patients .................................................................................................................. 213

8.5.3. Influencing Practice ................................................................................................................. 215

8.5.4 Aligning Surgeon Perceptions with Objective Evidence .......................................................... 216


8.7 Conclusions ..................................................................................................................................... 218

Chapter 9: Does Magnetic Resonance Imaging Improve the Predictive Performance of our Validated

Clinical Prediction Rule ............................................................................................................................. 219

9.1 Introduction .................................................................................................................................... 219

9.2 Methods .......................................................................................................................................... 220

9.2.1 Patient Sample ......................................................................................................................... 220


9.3 Results ............................................................................................................................................. 221

9.4 Discussion ........................................................................................................................................ 225

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9.6 Conclusions ..................................................................................................................................... 228

Chapter 10: A Clinical Prediction Rule for Functional Outcomes in Patients Undergoing Surgery for

Cervical Spondylotic Myelopathy: Analysis of an International AOSpine Prospective Multicentre Dataset

of 757 Subjects .......................................................................................................................................... 229

10.1 Introduction .................................................................................................................................. 229

10.2 Methods ........................................................................................................................................ 230

10.2.1 Patient Sample ....................................................................................................................... 230

10.2.2 Statistical Analysis .................................................................................................................. 230

10.3 Results ........................................................................................................................................... 231

10.3.1 Patient Sample ....................................................................................................................... 231

10.3.2 Predicting a mJOA score ≥16 ................................................................................................. 232

10.4 Discussion...................................................................................................................................... 237

10.5 Conclusions ................................................................................................................................... 240

Chapter 11: Clinical and Surgical Predictors of Complications following Surgery for the Treatment of

Cervical Spondylotic Myelopathy: Results from the Prospective AOSpine International study of 479

Patients ..................................................................................................................................................... 242

11.1 Introduction .................................................................................................................................. 242

11.2 Methods ........................................................................................................................................ 243

11.2.1 Patient Sample ....................................................................................................................... 243

11.2.2 Statistical Analysis .................................................................................................................. 243

11.3 Results ........................................................................................................................................... 244

11.3.1 Patient Sample ....................................................................................................................... 244

11.3.2 Complications ......................................................................................................................... 246

11.3.3 Univariate Analysis ................................................................................................................. 246

11.3.4 Multivariate Analysis .............................................................................................................. 248

11.3.5 Sub-Analyses .......................................................................................................................... 249

11.4 Applying the Model to Two Cases ................................................................................................ 250

11.5 Discussion...................................................................................................................................... 252

11.6 Strengths and Limitations ............................................................................................................. 255

11.7 Conclusions ................................................................................................................................... 255

Chapter 12: Summary of Findings, General Discussion, Thesis Limitations and Future Directions ......... 257

12.1. An Overview: Predicting Surgical Outcome ................................................................................. 257

12.2 An Overview: Predicting Complications ........................................................................................ 264

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12.3 Thesis Limitations .......................................................................................................................... 266

12.4 Future Directions .......................................................................................................................... 268

12.4.1 Standardizing Nomenclature ................................................................................................. 268

12.4.2 The Reliability of the mJOA and MRI Factors ......................................................................... 269

12.4.3 Guidelines for the Management of CSM ............................................................................... 269

12.4.4 Predicting Surgical Outcomes ................................................................................................ 270

12.4.5 Predicting Complications ....................................................................................................... 271

Publications Arising from this Thesis ........................................................................................................ 273

References ................................................................................................................................................ 276

xiv

List of Tables

Table 1-1. Differential Diagnoses of CSM ..................................................................................... 12

Table 1-2. Methods of Differentiating between CSM and other Common Diagnoses ................ 14

Table 1-3 Clinical Risk Factors of CSM Diagnosis .......................................................................... 15

Table 1-4. MRI Risk Factors of CSM Diagnosis .............................................................................. 18

Table 1-5. A Summary of Studies Evaluating the Validity of Various Measurement Tools ..... 23-25

Table 1-6. Reliability of the JOA, 30-meter Walking Test, NDI and SF-36 .................................... 26

Table 1-7 Responsiveness of the SF-36, mJOA, and NDI .............................................................. 27

Table 3-1. Relevant Prognostic Factors for Systematic Reviews A, B and C ................................. 41

Table 3-2. Relevant Outcomes for Systematic Reviews A, B and C .............................................. 42

Table 3-3. Definition of the Different Levels of Evidence for Prognostic Studies ....................... 45

Table 3-4. Definition of the Different Levels of Evidence for Therapeutic Studies ...................... 46

Table 3-5. Overview of Grade: Reasons for Upgrading and Downgrading Level of Evidence ...... 47

Table 3-6. Characteristics of Prognostic Studies with Multivariate Analysis: Systematic Review A

.................................................................................................................................................. 51-54

Table 3-7. Important Clinical Predictors of Surgical Outcomes: Results of Univariate and

Multivariate Analysis................................................................................................................ 59-62

Table 3-8. The Association between Age and Surgical Outcome: Results from Studies without

Multivariate Analysis .................................................................................................................... 63

Table 3-9. The Predictive Value of Duration of Symptoms: Results from Studies without

Multivariate Analysis..................................................................................................................... 64

Table 3-10. The Relationship between Preoperative Myelopathy Severity and Surgical Outcome:

Results from Studies without Multivariate Analysis ..................................................................... 65

Table 3-11: Other Clinical Predictors of Surgical Outcome: Results from Studies without

Multivariate Analysis..................................................................................................................... 66

Table 3-12. Evaluation of Overall Body of Evidence using GRADE: Systematic Review A ....... 68-69

Table 3-13. Characteristics of Prognostic Imaging Studies with Multivariate Analysis: Systematic

Review B ................................................................................................................................... 75-77

Table 3-14. Association of Anatomic MRI Characteristics with Surgical Outcomes ..................... 80

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Table 3-15. Association of MRI Signal Intensity Characteristics with Surgical Outcomes ....... 82-83

Table 3-16. Evaluation of Overall Body of Evidence using GRADE: Systematic Review B ....... 85-86

Table 3-17. Characteristics of Prognostic Complications Studies with Multivariate Analysis:

Systematic Review C ................................................................................................................ 93-94

Table 3-18. Important Clinical, Imaging and Surgical Predictors of Complications: Results of

Univariate and Multivariate Analysis ................................................................................... 100-102

Table 3-19. Clinical, Imaging and Surgical Predictors of Complications: Results of Prognostic

Studies without Multivariate Analysis ................................................................................. 104-106

Table 3-20. Comparative Surgical Studies reporting differences in Complication Rates .... 111-116

Table 3-21. Evaluation of Overall Body of Evidence using GRADE: Systematic Review C ... 117-121

Table 4-1. Survey Questions and Answer Options for Part A .............................................. 126-127

Table 4-2. Survey Questions and Answer Options for Part B .................................................... 127-128

Table 4-3. Important Clinical Predictors of Surgical Outcome: Results for Entire Sample and each

Geographic Region ................................................................................................................................. 132

Table 4-4. Threshold Duration of Symptoms, Age and Baseline Severity Score and Smoking as a

Predictor .................................................................................................................................................. 134

Table 4-5. Important Imaging Predictors of Surgical Outcome: Results for Entire Sample and

each Geographic Region ........................................................................................................................ 138

Table 4-6. Important Clinical Predictors of Postoperative Complications: Results for Entire

Sample and each Geographic Region .................................................................................................. 145

Table 4-7. Complication Rates in Anterior vs. Posterior Surgery ..................................................... 149

Table 4-8. Complications Rates in 1-Stage vs. 2-Stage Surgery ....................................................... 150

Table 4-9. Complication Rates between Laminectomy with Fusion and Laminoplasty ............... 151

Table 4-10. Complication Rates between Fusion and Non-Fusion Surgery ................................... 152

Table 5-1. Inclusion and Exclusion Criteria for Participation in the CSM-North America and CSM-

International Studies .............................................................................................................................. 160

Table 5-2. A Summary of Relevant Clinical Variables collected as part of the CSM-North America

and International Studies ............................................................................................................... 162-166

Table 5-3. A Summary of the Imaging Parameters collected using Quantitative Analysis of

Magnetic Resonance Images from Patients enrolled in the CSM-North America Study ............. 167

xvi

Table 5-4. A Summary of Relevant Surgical Variables collected as part of the AOSpine Studies .................................................................................................................................................................. 168

Table 5-5. The modified Japanese Orthopaedic Association Scale ................................................. 169

Table 5-6. Assessing Outcome in Patients with CSM: Advantages and Disadvantages of the

mJOA, SF-36, NDI, Nurick and 30-meter walking test ................................................................ 171-172

Table 5-7. A List and Description of Anticipated Surgery-Related Complications ........................ 173

Table 6-1. A Summary of Demographics, Baseline Status and Surgical Outcomes of 517 Patients

Enrolled in the AOSpine CSM-North America or CSM-International Multicenter Studies .......... 178

Table 6-2. The mJOA Change Scores in Patients Classified as “Worsened,” “Unchanged,”

“Slightly Improved” and “Markedly Improved” based on the NDI ................................................. 179

Table 6-3. Establishing a Cut-off to Distinguish between Patients with an “Optimal” and

“Suboptimal” Surgical Outcome........................................................................................................... 184

Table 7-1. Patient Baseline Demographic Information and 1-year Functional Outcomes following

Surgery: CSM-North America Study ..................................................................................................... 190

Table 7-2. Univariate Analyses Evaluating the Association between Various Clinical Predictors

and a mJOA Score ≥16 at 1-year following Surgery ........................................................................... 192

Table 7-3: A Clinical Prediction Model to Determine Functional Status and Predict an “Optimal”

Surgical Outcome (mJOA≥16) .............................................................................................................. 194

Table 7-4. Final Linear Regression Model using Postoperative mJOA at 1-year as the Dependent

Variable .................................................................................................................................................... 194

Table 8-1. General Characteristics of the CSM-North America and the CSM-International study .................................................................................................................................................................. 205

Table 8-2. Odds Ratios for Original North American Model and Bootstrap Model ...................... 206

Table 8-3. Calibration of the Original and CSM-International Validation Models ........................ 209

Table 8-4. Refitting the Original Logistic Regression Model on the CSM-International Sample ........................................................................................................................................................... 209-210

Table 9-1. Demographic and MRI Information of a Subset of 99 Patients Enrolled in the CSM-

North America Study.............................................................................................................................. 222

Table 9-2. Predictive Performance of Original Model with the Addition of Various MRI

Parameters .............................................................................................................................................. 223

Table 10-1. Patient Baseline Demographic Information and 1-year Functional Outcomes

following Surgery for CSM ............................................................................................................. 233-234

xvii


and a mJOA Score ≥16 at 1-year following Surgery ........................................................................... 234

Table 10-3. Final Clinical Prediction Model to Determine Functional Status (mJOA≥16) at 1-year

following Surgery .................................................................................................................................... 235


and a mJOA score ≥12 at 1-year following Surgery in Patients with Severe CSM (mJOA<12) .... 236


following Surgery in Patients with Severe CSM (mJOA<12) ............................................................. 237

Table 11-1. General Characteristics, Signs and Symptoms, Co-Morbidities, Diagnosis and Surgical

Summary of CSM patients enrolled in the CSM-International Study ............................................. 245

Table 11-2. Univariate Analysis assessing the Relationship between Various Clinical Factors and

Perioperative Complications ................................................................................................................. 247

Table 11-3. Univariate Analysis assessing the Relationship between Various Surgical Factors and


Table 11-4. Final Complications Prediction Model: Significant Clinical and Surgical Predictors of


Table 11-5. The Association between Important Predictors and Specific Type of Complications .................................................................................................................................................................. 251

Table 11-6. The Relationship between Type of Complications and Surgical Approach, Number of Stages and Posterior Technique ........................................................................................................... 251

xviii

List of Figures

Figure 1-1. A Magnetic Resonance Image of a Patient with Severe Degenerative Changes and

Signal Lesions .................................................................................................................................. 8

Figure 3-1. Search Strategy and Detailed Review Process for Systematic Review A ................... 49

Figure 3-2. Search Strategy and Detailed Review Process for Systematic Review B .................... 72

Figure 3-3. Summary Figure of Anatomic MRI Characteristics ..................................................... 74

Figure 3-4. Summary Figure of Cord Signal Change Properties .................................................... 78

Figure 3-5. Search Strategy and Detailed Review Process for Systematic Review C .................... 91

Figure 4-1. Geographical Distribution of Survey Participants: Part A ........................................ 129

Figure 4-2. Distribution of Responses for each Clinical Factor ................................................... 130

Figure 4-3. Important Co-Morbidities of Outcome Prediction ................................................... 135

Figure 4-4. The Predictive Value of Myelopathic Signs .............................................................. 136

Figure 4-5. The Predictive Value of Myelopathic Symptoms...................................................... 136

Figure 4-6. Distribution of Responses for each Imaging Factor ................................................. 137

Figure 4-7. Geographical Distribution of Survey Participants: Part B ......................................... 142

Figure 4-8. Frequently Seen Surgical Complications across Six Geographic Regions ................. 143

Figure 4-9. Important Clinical Predictors of Postoperative Complications ................................ 144

Figure 4-10. Co-morbidities Professionals agree Increase the Risk of Postoperative

Complications.............................................................................................................................. 146

Figure 4-11. Differences in Specific Complications between Diabetic and Non-diabetic Patients

..................................................................................................................................................... 147

Figure 4-12. Important Imaging Predictors of Postoperative Complications ............................. 148

Figure 4-13. Differences in Specific Complications between Anterior and Posterior Surgery ... 150

Figure 4-14. Differences in Specific Complications between Laminectomy with Fusion and

Laminoplasty ............................................................................................................................... 152

Figure 4-15. Differences in Specific Complications between Fusion and Non-Fusion Surgery .. 153

Figure 5-1. Enrollment Summary of the AOSpine CSM-North America and CSM-International

Studies ......................................................................................................................................... 161

xix

Figure 5-2. Computing Signal Change Ratios .............................................................................. 168

Figure 6-1. ROC Analysis: Difference between Sensitivity and Specificity for All Patients ........ 180

Figure 6-2. Results from a Survey of AOSpine International ...................................................... 181

Figure 6-3. ROC Analysis: Difference between Sensitivity and Specificity for Mild Patients ..... 181

Figure 6-4. ROC Analysis: Difference between Sensitivity and Specificity for Moderate Patients

..................................................................................................................................................... 182

Figure 6-5. ROC Analysis: Difference between Sensitivity and Specificity for Severe Patients .. 183

Figure 6-6. The Distribution of the Number of MCIDs Gained or Lost in two Outcome Groups

(mJOA≥16 and mJOA<16 at 1-year) ............................................................................................ 184

Figure 7-1. Receiver Operating Curve for the Final Clinical Prediction Model ........................... 193

Figure 8-1. Receiver Operating Curves for Original and Bootstrap Models ................................... 206

Figure 8-2. Receiver Operating Curves for the Original North American Model (red) and the

Model Validated on the International Population (blue) ................................................................. 207

Figure 8-3: Calibration Plots. A (top): Original model; B (bottom): Validated Model .................. 208

Figure 8-4. Applying the Clinical Prediction Model in a Surgical Setting: Case 2 .......................... 213



Figure 9-1. An Overview of our Patient Sample derived from the CSM-North America Study ... 220

Figure 9-2. Summary of Functional Outcome at 1-year Post-Surgery ............................................ 221

Figure 9-3. ROC Curves of Original Model + T2 Hyperintensity, T1-Hypointensity or Combined

T1/T2 Signal Change ............................................................................................................................... 223

Figure 9-4. ROC Curves of Original Model + Height or Area of T2 Signal Change ......................... 224

Figure 9-5. ROC Curves of Original Model + Spinal Canal Compromise or Spinal Cord

Compression ........................................................................................................................................... 224

Figure 9-6. ROC Curves of Original Model + Signal Change Ratio ................................................... 225

Figure 10-1. Summary of Participating Subjects and Predictors Evaluated in this Study ............ 231

Figure 11-1. An Overview of the Types of Complications experienced by CSM Patients in the

Perioperative Period .............................................................................................................................. 247

Figure 11-2. Applying the Complications Prediction Model in a Surgical Setting: Case 1 ............ 250

xx

Figure 12-1. A Theoretical Framework of the Prediction Model ..................................................... 261

Figure 12-2. A Summary of Complications seen in Patients undergoing Surgery for CSM .......... 272

xxi

List of Abbreviations ADC – Apparent Diffusion Coefficient ACDF – Anterior Cervical Discectomy and Fusion AHQR – Agency for Healthcare Research and Quality ALS – Amyotrophic Lateral Sclerosis AUC – Area Under the Curve BMI – Body Mass Index BP – Bodily Pain CDH – Cervical Disc Herniation CI – Confidence Interval CMS – Center for Medicare & Medicaid Services CSF – Cerebrospinal Fluid CT – Computed Tomography CSM – Cervical Spondylotic Myelopathy DCM – Degenerative Cervical Myelopathy DTI – Diffusion Tensor Imaging ELAP – Expansive Open-door Laminoplasty EMG – Electromyography FA – Fractional Anisotropy GH – General Health GRADE – Grades of Recommendation Assessment, Development and Evaluation ICD – International Classification of Disease JOA – Japanese Orthopaedic Association KQ – Key Question MCC – Maximum Canal Compromise MELD – Model for End Stage Liver Disease MEP – Motor Evoked Potential MCID – Minimal Clinically Important Difference MCS – Mental Component Score MH – Mental Health

mJOA – modified Japanese Orthopaedic Association MRI – Magnetic Resonance Imaging MS – Multiple Sclerosis MSCC – Maximum Spinal Cord Compression NCS – Nerve Conduction Studies NCSS – Neurological Cervical Spine Scale NDI – Neck Disability Index ntSCI – non-traumatic Spinal Cord Injury OPLL – Ossification of the Posterior Longitudinal Ligament OR – Odds Ratio PCS – Physical Component Score PF – Physical Functioning PIVD – Prolapsed Intervertebral Discs RE – Role Limitations Emotional RP – Role Limitations Physical ROC – Receiver Operating Characteristics SCI – Spinal Cord Injury SCR – Signal Change Ratio SF – Social Functioning SEM – Standard Error of Measurement SEP – Sensory Evoked Potential SF-36 – Short-Form-36 SI – Signal Intensity/Change SNP – Single Nucleotide Polymorphism SSEP – Somatosensory Evoked Potential tSCI – traumatic Spinal Cord Injury VT - Vitality WI – Weighted Image

1

Preamble

Cervical spondylotic myelopathy (CSM) is a progressive, degenerative spine disease and

the most common cause of spinal cord dysfunction in adults worldwide.1, 2 As our population

ages, an increased number of patients will exhibit degenerative changes and suffer from

varying stages of myelopathy.3 It is essential that global health systems develop guidelines for

the management of CSM to ensure adequate patient support, appropriate treatment plans and

optimal outcomes. Surgery is increasingly recommended as the preferred treatment strategy

for patients with CSM as it can effectively halt disease progression and improve neurological

outcomes, functional status and quality of life.4-9 Surgery, however, is not risk free and is

associated with complications in 11-38% of patients.10, 11 The majority of these are transient,

non-neurological and do not require invasive intervention or prolonged hospital stay.

Regardless, surgical complications still taint a patient’s overall perception of surgery and may

often involve postoperative management, additional follow-up visits and increased associated

costs.

Predicting surgical outcome and perioperative complications in these patients are

increasingly important research topics. This information is valuable to clinicians because 1) it

helps manage patients’ expectations which are directly associated with perception of outcome

and satisfaction; 2) it provides decision-making support to surgeons; 3) it allows surgeons to

identify high-risk patients and institute rigorous preventative strategies; and 4) it gives surgeons

a tool to counsel their patients and discuss relative risks and benefits of the procedure.

Furthermore, this information will enable health care providers to better anticipate hospital

utilization costs, allocate sufficient resources and strategize postoperative management. Given

its clinical value, both an outcome and complications prediction model should be incorporated

into the clinical guidelines for CSM management.

It is therefore the objective of this thesis to develop prediction models that can

accurately predict surgical outcome and perioperative complications in patients with CSM.

2

Thesis Structure

This thesis has two key objectives and has been organized according to the “multiple

paper format” using primarily unaltered peer reviewed material. The first objective is to

develop a clinical prediction rule to predict functional outcomes in patients with CSM

undergoing surgery. The second objective is to determine significant clinical and surgical

predictors of perioperative complications and to construct a complications prediction rule that

can help clinicians identify their high risk patients.

This thesis is divided into 12 chapters. Chapter 1 provides an introduction to the topic of

CSM and summarizes the diagnosis, pathophysiology, clinical and imaging assessment, risk

factors and treatment for this disease. Chapter 2 is a modified version of a paper under review

at the “Evidence-Based Spine Journal” and defines the value of clinical prediction rules in and

out of the setting of spinal cord injury (SCI). Section 2.4 provides the rationale, objectives and

specific aims of this thesis. Chapter 3 presents the methods and results of three systematic

reviews conducted to evaluate important clinical and imaging predictors of surgical outcome

and significant clinical and surgical predictors of complications. This chapter is derived from

three separate manuscripts published in the “European Spine Journal,” “SPINE,” and “Journal of

Neurosurgery: Spine.” Chapter 4 outlines the methods and results from two surveys distributed

to members of AOSpine International and compares these findings to the conclusions from our

systematic reviews. The results from these surveys were published in “World Neurosurgery.”

Chapter 5 is a brief section that summarizes the key objectives of the AOSpine studies and

provides an overview of the datasets used in our analyses. Chapter 6 is a modified version of a

paper published by “SPINE” and establishes the minimal clinically important difference (MCID)

of the modified Japanese Orthopaedic Association (mJOA) scale and uses this value to define an

appropriate cut-off between an “optimal” and “suboptimal” outcome. Chapter 7 is a

reformatted version of a paper published in “Journal of Bone and Joint Surgery” and describes

the initial modeling process using data from patients enrolled in the AOSpine CSM-North

America study. Chapter 8 is derived from a manuscript published in “The Spine Journal” and

summarizes the results of external validation using data on patients enrolled in the AOSpine

3

CSM-International study. Chapter 9 evaluates the role of magnetic resonance imaging (MRI) as

a prognostic tool and aims to determine whether specific imaging variables can improve the

predictive performance of our validated prediction model. This paper has been published by

“SPINE.” Our final outcome prediction paper has been accepted for publication by “Journal of

Bone and Joint Surgery” and is presented in Chapter 10. Chapter 11 is a reformatted version of

a paper invited to be published in “Neurosurgery” and presents a preliminary complications

prediction model that can be used to identify high risk surgical patients. Finally, Chapter 12

summarizes the findings and limitations of our studies and suggests future directions.

4

Chapter 1: An Overview of Diagnosis, Pathophysiology, Treatment and

Assessment Standards for Cervical Spondylotic Myelopathy

1.1 Introduction

Spinal cord injury (SCI) is an insult to the spinal cord that results in disturbances to

normal sensory, motor, or autonomic function and ultimately impacts a patient’s physical,

emotional and social well-being.12 A SCI can be caused by a traumatic event such as a motor

vehicle accident or a fall, or may result from non-traumatic etiologies, including tumors,

degenerative changes, loss of blood supply and infection. Cervical spondylotic myelopathy

(CSM) is a progressive spine disease caused by the degeneration of various components of the

spinal axis and is an example of a non-traumatic SCI that can result in severe neurological

impairment and reduced quality of life.13 This thesis will focus on patients with cord

compression secondary to degenerative changes or “degenerative cervical myelopathy (DCM)”.

We will use the terms DCM and CSM synonymously throughout this thesis. The goal of this

chapter is to provide an overview of the pathophysiology, epidemiology, diagnosis, risk factors,

measurements tools and management strategies for CSM.

1.2 Anatomy of the Cervical Spine

The cervical spine consists of seven vertebrae that span from the occipital bone at the

base of the skull to the thoracic vertebrae that articulate with the ribs.14 A normal vertebra

houses the spinal cord and is composed of a rounded body anteriorly and a vertebral arch

posteriorly.14 The vertebral arch consists of a pair of pedicles and laminae and gives rise to one

spinous, two transverse and four articular processes. The spinous and transverse processes

serve as attachment sites for ligaments and muscles whereas the articular processes adjoin two

adjacent vertebrae.

Intervertebral discs lie between each vertebra and consist of a peripheral annulus

fibrosus and a central nucleus pulposus.14 The annulus fibrosus is composed of fibrocartilage

and firmly attaches to adjacent vertebral bodies and the anterior and posterior longitudinal

5

ligaments. The nucleus pulposus is comprised of gelatinous material and, as such, provides

mobility to the vertebrae.

The vertebral bodies of C3 through C7 have lateral hook-shape processes known as the

uncinate processes that articulate with the surface of the above vertebra and form

uncovertebral joints.15 At the junction of the pedicle and lamina there is a bony pillar that forms

the superior (upward projection) and inferior (downward projection) articular facets. The

superior facet of one vertebra adjoins with the inferior facet of the above vertebra to form a

facet joint on each side of the spinal canal.14

The spinal ligaments provide additional support to the spinal column and consist of the

anterior and posterior longitudinal ligaments, the supraspinous ligaments, interspinous

ligaments, intertransverse ligaments and ligamentum flavum.14 The ones relevant to this thesis

are the posterior longitudinal ligaments which run as a continuous band down the posterior

surface of the vertebral bodies and intervertebral discs, and the ligamentum flavum which

connects the laminae of adjacent vertebrae.

1.3 The Degenerative Process and Pathophysiology

CSM is a progressive disease caused by age-related alterations including a) degeneration

of the facet joints, intervertebral discs and/or vertebral bodies; b) hypertrophy of the

ligamentum flavum; and c) ossification of the longitudinal ligament (OPLL).1, 13 As the spine

ages, the discs begin to degenerate and can no longer fulfill their weight bearing and load-

transferring functions.13, 16 As a result, the uncovertebral processes experience increased load

and become flattened. This alters the load-bearing function of the intervertebral joint and puts

increased stress on the articular cartilage endplates. Osteophytes develop to stabilize

hypermobility and to increase the weight bearing surface of the end plates.17 These bony spurs

also protrude outward from the vertebral body to cover the bulging intervertebral disc.2 In

addition, the ligamentum flavum may stiffen and buckle due to loss of disc height and

straightening of cervical lordosis and other spinal ligaments may hypertrophy or ossify.13, 18

6

These age-related degenerative changes ultimately narrow the spinal canal and

encroach on the spinal cord. In addition to static mechanical factors, nerve root and spinal cord

compression can be aggravated by dynamic factors.19 For example, in neck flexion, the spinal

cord can be compressed by ventral osteophytes, and in extension, the cord can be pinched

between the vertebrae body and the lamina or ligamentum flavum.15 Mechanical, chronic

compression of the cord reduces intraparenchymal spinal cord flow, affects the integrity of the

microvasculature and results in spinal cord ischemia.20, 21 This ischemia damages

oligodendrocytes, endothelial cells and neurons and initiates a chronic immune response that

consists of microglia activation and macrophage recruitment to the site of compression.22-24

Persistent compression can result in axonal demyelination, gliosis, scarring, cavitation,

degeneration of the corticospinal tracts, interneuronal loss and atrophy of the anterior horn

cells.16

The prevalence of degenerative changes in the asymptomatic population is not well

documented. In a study by Ernst et al (2005), the prevalence of annular tears and bulging discs

was 36.7% and 73% of asymptomatic volunteers, respectively.25 Disc protrusions were also seen

in 50% and an extrusion was identified in one subject at the C5-C6 level. Thirty-three percent of

volunteers presented with severe degeneration of one or more discs and 13.3% exhibited

image evidence of medullar compression. A second study on 1211 asymptomatic volunteers

from Japan reported significant disc bulging in 87.6% of the sample and evidence of spinal cord

compression in 5.3%.26 Furthermore, 2.3% exhibited high signal intensity lesions on T2-

weighted images (WI) and 3.1% had flattening of the spinal cord. Finally, Matsumoto et al

(1998) evaluated the discs of 497 volunteers and identified grade-1 (dark and/or speckled) and

grade-2 (almost black) disc degeneration in 86% and 89% of subjects over 60 years of age,

respectively.27 Approximately eight percent of volunteers exhibited grade-2 posterior disc

protrusion with spinal cord compression. OPLL was present in 0.4-3.6% of the sample. All three

studies reported an increase in prevalence of degenerative changes with increasing age.25-27 In

addition, Kato et al (2012) demonstrated a decrease in the diameter of the spinal cord, spinal

canal and dural tube and in the area of the dural tube and spinal cord with increasing age.28

7

Asymptomatic patients with evidence of cervical canal stenosis and cord compression

due to spondylosis are at a high risk of developing signs and symptoms of myelopathy.29

According to a systematic review by Wilson et al (2013), approximately 8% of these patients will

deteriorate and exhibit clinical evidence of CSM at 1-year and 23% at a median of 44-months.29

1.4 Prevalence of CSM and Economic Implications of Disease

CSM is the most common cause of spinal cord dysfunction in adults worldwide.

According to the World Health Organization, the proportion of the population over 60 years of

age is projected to double from 11% in 2010 to 22% in 2050. Thus, it is anticipated that

healthcare systems worldwide will be confronted with an increase in patients presenting with

degenerative changes and varying stages of myelopathy.3 In a recent review on the

epidemiology of non-traumatic SCI (ntSCI), New et al (2013) estimated that degenerative spine

disease encompasses 59% of ntSCIs in Japan, 54% in the USA, 31% in Europe, 22% in Australia

and between 4-30% in Africa.30 Furthermore, the regional incidences of ntSCI in North America,

Europe and Australia are 76, 26 and 6 per million, respectively and the prevalence in Canada is

1,120/million. From these numbers, Nouri et al (2015) conservatively estimated the incidence

and prevalence of CSM in North America as 41 and 605/million, respectively.31 Given this

expected increase in disease prevalence, clinicians must design and implement effective

treatment strategies for patients with CSM in order to optimize outcome, improve quality of life

and lessen future cost burden.

1.5 Current Approaches to CSM Diagnosis

1.5.1 Clinical Assessment

Patients with evidence of cervical degeneration may be completely asymptomatic or

simply have localized neck pain.15 If these degenerative changes result in nerve root or cord

compression, patients may experience referred pain into the upper extremities or exhibit motor

dysfunction in the upper and lower limbs, sensory loss or sphincter disturbance.15

CSM is first diagnosed based on patient reported symptoms and a detailed neurologic

examination.32 Common symptoms include numb hands, loss of manual dexterity, bilateral arm

8

paresthesia, impaired gait, lower extremity weakness, l’Hermitte’s phenomena, urge

incontinence and urgency of urination and defecation.15, 16 Relevant signs of myelopathy are

hyperreflexia, clonus, a positive Hoffman sign, upgoing plantar responses, lower limb spasticity,

corticospinal distribution motor deficits, atrophy of intrinsic hand muscles, broad-based

unstable gait and sensory loss.15, 16

1.5.2 Imaging Assessment

Magnetic resonance imaging (MRI) can visualize neural, osseous and soft tissue

structures with high-resolution and is routinely used to confirm the diagnosis of CSM.33 MRI

can evaluate the degree of degeneration and canal stenosis, identify compression of the spinal

cord and detect intramedullary signal changes.34 It is also one of the most valuable tools to

differentiate between CSM and other similar diagnoses as it can visualize anatomical changes of

the spinal axis and parenchymal abnormalities including neoplasms, demyelinating plaques and

syringomyelia.32 Furthermore, MRI plays a role in surgical decision making and may be useful in

predicting postoperative outcomes. Figure 1-1 displays a MRI of a patient with severe

degenerative changes, spinal canal narrowing and a high signal lesion on a T2WI.

Figure 1-1. A Magnetic Resonance Image of a Patient with Severe Degenerative Changes and Signal Lesions

9

Using a MRI, a clinician typically evaluates the anteroposterior diameter, compression

ratio and transverse area of the spinal cord and searches for T1 signal hypointensity, T2 signal

hyperintensity, segmentation of T2 signal change, effacement of cerebrospinal fluid (CSF) and

deformation of the cord.15

Unfortunately, patients cannot be examined by MRI if they have metallic foreign body in

their eye, aneurysm clips, embedded wires, stimulators or batteries, nitroglycerin patches,

pacemakers or severe claustrophobia. Computed tomographic (CT) scans or myelography are

alternative diagnostic modalities for patients with contraindication to MRI.35 This form of

imaging can also be used to visualize bony abnormalities and cord deformation.

Lateral plain X-rays are often used in conjunction with MRI to depict spinal canal

narrowing.15 In addition, lateral X-rays can identify instability, degenerative disc disease,

scoliosis, subluxation and kyphosis.

1.5.3 Electrodiagnosis

Electrodiagnosis is less commonly used to diagnose CSM but can help differentiate

between patients with spondylotic neural compression and those with mimicking diagnoses.15

Common forms of electrodiagnosis include electromyography (EMG), electroneurography or

nerve conduction studies (NCS) and evoked potentials.

EMG assesses the activity of muscle cells by repeatedly stimulating receptors of the

sensory system and measuring resultant cortical activity.36 At rest, insertion activity may be

absent in various neuromuscular disorders, reduced in metabolic diseases and prolonged in

denervated muscle. Rhythmic fibrillation potentials in single muscle fibers and positive sharp

waves are also indicators of denervation. Although fasciculation potentials may be present in

normal muscle, they are also indicative of chronic partial denervations as in amyotrophic lateral

sclerosis (ALS).37 Alterations in motor unit potentials can help diagnose other diseases: double

discharges occurring at the beginning of voluntary contractions can indicate disorders of the

anterior horn cells, roots or peripheral nerves; myokymic discharges can reflect patients with

radiation myelopathy, multiple sclerosis (MS), chronic radiculopathy, entrapment neuropathy

10

or syringomyelia; and neuromyotonic discharges can be present in patients with peripheral

axonal or demyelinated neuropathy.37

During muscular contraction, the amplitude and duration of motor unit action potentials

are affected by the size of the motor unit. In neuropathic disorders, the number of functional

motor units is reduced and the motor unit potentials are longer and sometimes polyphasic.36

Greater amplitude potentials can indicate anterior horn cell involvement.

Electroneurography or NCS can quantify the motor and sensory conduction velocities of

peripheral nerves. This is done by placing two electrodes at different points along a peripheral

nerve; the interval between the stimulus and recorded response is measured and divided by

the distance between the two electrodes.36, 37 When the peripheral nerve is stimulated, one

response will travel antidromically towards the spinal cord, synapse with the ganglionic cells in

the anterior horn and travel back towards the periphery. This is known as an F-wave. NCS are

less important in detecting CSM and pure sensory radiculopathies.15 They are, however,

essential in ruling out alternative diagnoses, including peripheral axonal and demyelinating

neuropathy, and peripheral nerve entrapment such as carpal tunnel syndrome.15

Evoked potentials are important tools for determining the integrity of various functional

systems, including the visual, auditory, somatosensory and motor. In the case of somatosensory

evoked potentials (SSEP), an electrical stimulus applied to the skin will travel through the

peripheral nerve, nerve root, posterior columns, spinothalamic tract, medial lemniscus and

thalamocortical connections.36, 37 Any decrease in velocity may reflect injury along any of these

pathways and can help assess the degree of sensory conduction impairment. Although these

potentials may be useful in the diagnosis of CSM, they are not commonly used in clinical

practice except for electrophysiological monitoring during surgery.15

1.5.4 Differential Diagnosis

There are several neurological conditions that present similarly to CSM, including

intracranial, demyelinating, motor neuron, infectious, inflammatory and metabolic

abnormalities. Table 1-1 provides a summary of the differential diagnoses of CSM.

11

As suggested by two textbooks, Harrison's Internal Medicine and Clark's Cervical Spine,

the four major differential diagnoses of CSM are ALS, MS, peripheral nerve entrapment, and

vitamin B12 deficiency (Table 1-2).38, 39

ALS is a debilitating upper and lower motor neuron disease that manifests in the fourth

to sixth decade of life, similarly to CSM.38 It often presents with symmetrical muscular

weakness of the shoulders, fasciculation and atrophy of the upper limbs, and muscular

spasticity.38, 39 In contrast to CSM, patients with ALS do not have pain or sensory changes and

have tongue fasciculations, normal bladder and bowel function and a unique CSF profile.39 A

recent study of protein contents in the CSF identified three biomarkers that are specific to

patients with ALS. In one study, cystatin C, a proteolytic fragment of VGF, and a third protein

species were found to be significantly lower in patients with ALS than in control subjects.40 In a

second study, Ranganathan et al. (2005) reported that patients with ALS had decreased levels

of cystatin C and transthyretin and increased levels of a carboxy-terminal fragment of the

neuroendocrine protein 7B2.41 The most useful biomarkers for distinguishing ALS from other

neurodegenerative diseases and healthy subjects are erythropoietin (decreased),42 hepatocyte

growth factor (upregulated),43 monocytic chemotactic protein (increased),44 neurofilament light

and heavy subunits (upregulated), cystatin C and transthyretin.45

MS presents similarly to CSM but occurs more commonly in young adults between ages

20 to 40 years. 38 Patients with MS display relapsing symptoms involving the white matter tracts

and may experience L’Hermitte’s phenomena, and motor, sensory, and bladder/bowel

dysfunction.39 Diagnosis is likely not MS if 1) symptoms are localized to the spinal cord; 2)

patients are <15 or >60 years of age; 3) the disease is progressive in nature; and 4) there is a

lack of visual dysfunction.39

12

Table 1-1. Differential Diagnoses of CSM

Compressive Myelopathies

Cervical spondylotic myelopathy o Spondylosis o Disc herniation o Ossification of the Posterior Longitudinal Ligament o Subluxation o Hypertrophy of the Ligamentum Flavum o Congenital stenosis

Cord compression by spinal tumor

Spinal epidural abscess

Spinal epidural hematoma

Hematomyelia

Chiari Malformation

Trauma

Non-Compressive Myelopathies

Spinal cord infarction

Inflammatory and immune myelopathies (myelitis) o Multiple Sclerosis o Rheumatoid arthritis o Neuromyelitis optica o Systemic immune-mediated disorders (Lupus, Sjorgren’s syndrome,

sarcoid myelopathy, vasculitis, perinuclear antineutrophilic cytoplasmic antibodies and primary central nervous system vasculitis)

o Post-infectious myelitis o Acute infections myelitis (viral (Herpes zosters, HIV, rabies), bacterial,

fungal and parasitic)

Radiation myelopathy

Infection (disc space infection or osteomyelitis)

Amyotrophic Lateral Sclerosis

Chronic Myelopathies

Vascular malformations of the cord and dura (dural arteriovenous fistulas)

Retrovirus-associated myelopathies (HTLV-1, HIV)

Syringomyelia

Subacute combined degeneration (vitamin B deficiency)

Hyocupric myelopathy

Tabes dorsalis

Other (congenital anomalies or genetic-linked diseases)

Congenital anomalies of the atlantoaxial joint (Dwarfing conditions, Down syndrome or odontoid hypoplasia)

Malformations of the occipital bone (basilar invaginations)

Klippel-Feil syndrome

Os odontoideum

Familial spastic paraplegia

Adrenomyeloneuropathy

13

Symptoms of CSM are often mistaken as peripheral nerve entrapment such as carpal

tunnel syndrome.46, 47 In carpal tunnel, examination of the hand will reveal thenar wasting but

not concurrent atrophy of the lumbrical muscles as is seen in CSM. Furthermore, patients with

CSM will exhibit other neurological signs of cord compression such as hyperreflexia, lower limb

spasticity, a positive Hoffman’s sign and upgoing plantar responses.48 The MRI of patients with

peripheral nerve entrapment will be normal; however nerve conduction velocty and SSEPs will

be abnormal.15

Vitamin B12 deficiency can also result in symptoms similar to CSM, including sensory

and motor deficiencies and gait ataxia.48 Deep tendon reflexes are usually absent or severely

diminished, while pathologic reflexes (Babinski sign) are present.48 Usually these neurologic

findings are accompanied by dementia and/or other psychiatric symptoms. Patients should be

examined for Vitamin B12 deficiency if they have a history of pernicious anemia or

gastrointestinal abnormalities, symptoms of gait ataxia and motor or sensory deficits.48

Syringomyelia refers to a disorder in which abnormal fluid-filled cavities or cysts form in

the spinal cord.38, 39 The symptoms begin earlier than in CSM, but, like CSM, the onset of

syringomyelia is insidious and progresses irregularly.39 Symptoms include sensory loss, aflexic

weakness, atrophy in the upper limbs, leg spasticity, bladder and bowel dysfunction, and

Horner’s syndrome.39 Due to several overlapping symptoms, syringomyelia should be included

in the differential diagnosis of CSM.

Finally, compressive spinal tumors can also mimick the signs and symptoms of CSM.39

Other important differential diagnoses include any condition that results in cord compression,

including compressive, non-compressive, and chronic myelopathies.38, 39

14

Table 1-2. Methods of Differentiating between CSM and other Common Diagnoses

Diagnosis Common Symptoms Physical Exam Findings

MRI Findings Electrodiagnosis Serologic/ Tissue

CSM -Older age -Gradual onset -Loss of manual dexterity -Balance and coordination problems -Neck pain -Gait dysfunction

-Gait abnormality, hyperreflexia, clonus, spasticity

-Spinal cord compression with or without signal change -Spondylotic changes -Signal intensity change localized to areas of compression

-Normal or signs of radiculopathy: slowing of the peripheral and central portion of motor pathway. -Prolonged central conduction time on MEP

Normal

ALS -Weakness, muscle atrophy, fasciculations, gait difficulty usually due to weakness

-Gait difficulty -Babinski

-Normal -Increased T2 signal intensity on brain MRI in posterior part of internal capsule

-Fibrillation and fasciculations Normal

Peripheral Nerve Entrapment

-Night pain in upper extremities -Positional variation in symptoms

Normal Abnormal Nerve Conduction Velocity and SSEPs

Normal

Vitamin B12 Deficiency

-History of pernicious anemia, GI disorders, malnutrition -Dementia

-Decreased deep tendon reflexes -Babinski

Normal -Can have abnormal Visual Evoked Potentials -Multimodality abnormalities on SSEP

Decreased Vitamin B12

MS - Age 20-40 -More common in females -Vision problems -Gait dysfunction and imbalance

-Gait abnormality -Motor and sensory deficits which wax and wane

-Increased T2 signal intensity on brain MRI

-Abnormal Visual Evoked Potentials -Abnormal Brain Auditory Evoked Potentials -Scalp-recorded SEPs are present in 50-86% -Short-latency N13 from the neck or P14 from the scalp are present in 69-94%

Normal

CSM: cervical spondylotic myelopathy; ALS: amyotrophic lateral sclerosis; MS: multiple sclerosis; GI: gastrointestinal; MRI: magnetic resonance imaging; MEP: motor evoked potentials; SEPs: sensory evoked potentials.

15

1.6 Risk Factors of Disease Diagnosis, Development and Progression

1.6.1 Clinical Risk Factors

Several studies have examined the association between age, gender and CSM diagnosis.

Age was identified as a significant risk factor of CSM by two studies.49, 50 Yue et al (2001)

compared age between CSM patients and controls with neck pain but no evidence of

spondylosis; on average, patients from the myelopathy group were 56.7 years and significantly

older than subjects in the control group (43.3 years). In a study by Takamiya et al (2006),

increased age was also a significant risk factor for cervical myelopathy (OR: 1.07, 95% C.I.: 1.01-

1.14). In contrast, a single study reported no significant relationship between age and CSM

diagnosis.51 (Table 1-3). Based on two studies, gender is not a risk factor of diagnosis.

Table 1-3 Clinical Risk Factors of CSM Diagnosis

Clinical Factor Outcome Author (year) Controlled for Confounders

Associated with Risk?

Age CSM Diagnosis Yue et al (2001) Yes Positively Takamiya et al (2006)

Yes Positively

Chen et al (1994) No No Gender CSM Diagnosis Yue et al (2001) Yes No

Takamiya et al (2006)

Yes No

Disease Development or Progression

Other studies have aimed to determine significant clinical predictors of disease

development or progression in asymptomatic patients or those treated conservatively for

myelopathy. Bednarik et al (2008) evaluated key predictors of early symptomatic CSM in

patients with 1) MRI signs of spondylotic or disc compression of the cervical spinal cord with or

without signal changes on T1/T2-WI, 2) axial pain or clinical signs and/or symptoms of

radiculopathy that could be managed conservatively and 3) no clinical signs and symptoms of

myelopathy.52 In univariate analysis, patients with an abnormal EMG, defined as motor axonal

neuropathy in at least two myotomes, were 2.87 times more likely to progress to symptomatic

CSM. In addition, disease development was predicted by clinically symptomatic radiculopathy

(OR: 4.69, p=0.004), and abnormal motor (MEP) (OR: 2.94, p=0.046) and sensory evoked

16

potentials (OR: 3.97, p=0.011). All of these clinical variables were included in the final

multivariate model except for abnormal EMG as it was highly correlated with clinical

radiculopathy. Gender and age were not significant predictors of disease development or

progression.52, 53 In a study by Barnes and Saunders (1984), however, there were more women

in the myelopathic group that exhibited deterioration following conservative treatment than in

the group that remained stable.54

1.6.2 MRI Risk Factors

Five studies explored the relationship between CSM diagnosis and various imaging

factors (Table 1-4).49, 51, 55-57 In a study by Hukuda et al (1996), several CT measurements were

compared between patients with CSM and a control group of patients with other forms of

spinal lesions (ex. metastatic thoracic tumor, rheumatoid spondylitis, traumatic subluxation).55

In the myelopathy group, patients had vertebral bodies with significantly larger cross-sectional

areas (C3-C6), transverse diameters (C3, C5-C7) and sagittal diameters (C3-C7). The transverse

diameter and sagittal diameter of the spinal canal were significantly smaller in the CSM group

at all vertebral levels. In addition, at C3 and C7, the cross-sectional area of the spinal canal was

significantly smaller in the myelopathy group compared to the control group. CSM patients also

had a smaller sagittal diameter of the spinal cord at all levels and a smaller transverse diameter

at C4, C6 and C7. The space available for the spinal cord in the sagittal (C5-C7) and transverse

planes (C3, C5) was significantly smaller in the myelopathy group than in the control group.

There was no significant difference in the cross-sectional space available for the spinal cord

between the two groups. Finally the ratios between vertebral body: spinal canal and spinal

canal: spinal cord (sagittal: C3-C4, transverse: C5-C7, cross-sectional: C3-C7) were significantly

larger in CSM patients.

Using MRIs, Okada et al (1994) compared the transverse area of the spinal canal and

dural tube and the occupying ratio of the spinal cord between patients with CSM (n=28) and

controls (n=96) without neurological symptoms.56 The canal occupying ratio was computed by

dividing the area of the spinal cord by the area of the spinal canal. These measurements were

made at C3 which was uncompressed in the CSM patients. Patients with CSM still had a

17

significantly smaller transverse area and a higher canal occupying ratio of the spinal cord at C3

than the controls. There was no correlation between transverse area of the dural tube and CSM

diagnosis.

A study by Chen et al (1994) compared the sagittal diameter of the vertebral bodies and

spinal canal in patients (n=100) undergoing surgery for CSM and asymptomatic volunteers

(n=100).51 In the myelopathic group, the sagittal diameter of the vertebral bodies was

significantly larger and the sagittal diameter of the spinal canal was significantly smaller than in

the control group. The Torg-Pavlov ratio, defined as the ratio between the sagittal diameter of

the cervical canal and the cervical vertebra at the same level, was significantly lower in CSM

patients than in the volunteers. This finding was confirmed by Yue et al (2001) who reported a

smaller Torg-Pavolv ratio in myelopathy patients.49

Finally, Golash et al (2001) compared MRI features across three groups: 1) volunteers, 2)

asymptomatic patients with image-evidence of cervical spondylosis, and 3) patients with

symptomatic myelopathy and MRI evidence of spondylosis.57 The cross-sectional areas of the

spinal canal, cord and CSF were significantly smaller in the CSM groups than the other two

groups. However, the cross-sectional area of the CSF was the only independent prognostic

factor of CSM diagnosis. Specifically, patients had a 90% risk of clinical myelopathy if the area of

their CSF space was less than 0.7 cm2.

Disease Development or Progression

The objective of other studies was to assess various imaging predictors of disease

development, progression or failed conservative treatment. In the study by Bednarik et al

(2008), there was no significant univariate association between development of early (<12

months) symptomatic CSM and type of compression (osteophytes vs. other), number of

stenotic levels (1 vs. ≥1), MRI hyperintensity, Pavlov ratio, compression ratio or cross-sectional

area of the spinal cord.52 In multivariate analysis, however, the presence of MRI hyperintensity

decreased the risk of early manifestation of myelopathy.

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Table 1-4. MRI Risk Factors of CSM Diagnosis

Image Factor Outcome Author (year) Controlled for Confounders

Associated with Outcome?

Cross-sectional area of spinal canal

CSM diagnosis Golash et al (2001) Yes No

Hukuda et al (1996) Yes Negatively (C3, C7)

Sagittal diameter of vertebral body

CSM diagnosis Hukuda et al (1996) Yes Positively

Chen et al (1994) No Positively

Transverse diameter of spinal canal

CSM diagnosis Hukuda et al (1996) Yes Negatively

Okada et al (1994) No Negatively

Sagittal diameter of spinal canal


Chen et al (1994) No Negatively

Torg Pavlov ratio CSM diagnosis Yue et al (2001) Yes Negatively

Chen et al (1994) No Negatively

Cross-sectional area for CSF space

CSM diagnosis Golash et al (2001) Yes Negatively

Transverse diameter of spinal cord

CSM diagnosis Hukuda et al (1996) Yes Negatively (C4, C6-C7)

Sagittal diameter of spinal cord


Cross-sectional area of spinal cord


Transverse diameter of vertebral body

CSM diagnosis Hukuda et al (1996) Yes Positively (C3, C5-C7)

Cross-sectional area of vertebral body

CSM diagnosis Hukuda et al (1996) Yes Positively (C3-C6)

Vertebral body: spinal canal ratio (sagittal)


Vertebral body: spinal canal ratio (transverse)


Canal: cord diameter (sagittal)


Canal: cord diameter (transverse)


Canal: cord diameter (cross-sectional)


Sagittal space available for spinal cord

CSM diagnosis Hukuda et al (1996) Yes Negatively (C5-C7)

Transverse space available for spinal cord

CSM diagnosis Hukuda et al (1996) Yes Negatively (C3-C5)

Cross-sectional space available for spinal cord

CSM diagnosis Hukuda et al (1996) Yes No

Dural tube transverse area CSM diagnosis Okada et al (1994) No No

Canal-occupying ratio of the spinal cord

CSM diagnosis Okada et al (1994) No Positively

In a study by Shimomura et al. (2007), 70 patients with mild CSM were enrolled to

evaluate factors associated with successful conservative treatment.53 Out of the 56 patients

19

that completed a final follow-up assessment, 11 showed signs of deterioration from mild to

moderate or severe CSM and were recommended surgery. The most significant and only MRI

predictor was circumferential spinal cord compression on an axial MRI. Patients with

circumferential compression were at a 26.624 (CI: 1.682-421.541, p=0.0199) higher risk of

deterioration on the Japanese Orthopaedic Association (JOA) scale. Ten out of 33 patients

(30.3%) with this type of cord compression deteriorated, nine of whom were subsequently

treated surgically. Partial cord compression on axial MRI and high SI grade on T2-WI (OR: 1.317,

CI: 0.161-10.801, p=0.793, multivariate) were not significantly associated with deterioration.

A second study by Oshima et al. (2012) also examined predictors of outcome in 45 mild

CSM patients treated conservatively.58 Sixteen of these patients deteriorated on the JOA score

and went on to receive surgical treatment. Spinal cord diameter, measured by the ratio at the

narrowest part of the canal to the C1 level, was the only MRI characteristic studied and was not

significantly related to failure of conservative treatment (p=0.09). Cox Proportional Hazard

analysis yielded a hazard ratio of 2.24 for a patient with a diameter <50% of the C1 level

compared to one with a diameter of ≥50%. The confidence interval, however, included 1 (CI:

0.83-6.06), indicating insignificance.

Yoshimatsu et al. (2001) studied 69 patients to determine the limitations of non-surgical

intervention and to analyze important predictors of failed conservative treatment.59 Forty-three

of these patients showed functional deterioration, assessed by a decrease in JOA. MRI

characteristics analyzed in this study included the number of intervertebral discs compressing

the spinal cord and the presence of high signal intensity (SI) on a T2WI; neither of these were

significant predictors of deterioration.

In a study by Barnes and Saunders (1994), patients were classified into three groups

based on whether they improved by at least one functional grade following conservative

treatment, stayed the same or deteriorated.54 Patients who worsened had a greater range of

preoperative neck and head movement than patients who stayed the same. Deterioration was

not predicted by anteroposterior diameter, canal size, subluxation, posterior osteophytosis,

lordosis in extension or kyphosis in extension.

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1.6.3 Genetic Risk Factors

A systematic review by Wilson et al (2013) was conducted to determine whether

individuals with affected relatives are at an increased risk of CSM or OPLL and to summarize

specific genetic polymorphisms associated with these diseases.60

Three studies supported a heritable predisposition for CSM and OPLL. In a study by Patel

et al (2012), first-degree relatives of patients with CSM were 5.1 times (95% C.I.: 2.07-13.1)

more likely to develop CSM.61 The risk ratio of second-degree relatives was non-significant

(p=0.07); however, third degree relatives were at twice the risk of disease development (95%

C.I.: 1.04-3.7). Similar results were reported in two OPLL studies: the risk of OPLL was 5.19 to

7.1 times higher in first-degree relatives than in controls.62, 63

According to Wilson et al (2013), several studies identified specific single nucleotide

polymorphisms (SNPs), haplotypes and gene alleles associated with OPLL and CSM.60 Specific

candidate genes include TGF-β, IL-15 receptor α, NPPS, BMP-2 and 4, RUNX2, VDR, RXRβ, Leptin

Receptor, APOE, COL6A1 and COL11A2. There is low level evidence suggesting that SNPs on the

COLA1 (intron 32(-29), CT) and the COL11A2 (intron 6(-4), AT) genes are associated with an

increased risk of OPLL development. Other SNPs and haplotypes were only examined by single

studies and cannot be deemed significant genetic risk factors. In CSM, disease development

was significantly associated with SNPs in the rs7975232 and rs731236 locations of the VDR

gene, the ε4 allele of APOE and the Trp2 allele of Collagen IX. However, these results were

reported by single studies and therefore are not sufficient to determine whether VDR-SNPs or

alleles of the APOE or Collagen IX are risk factors for CSM.

1.7 Evaluating Functional Status and Quality of Life using Outcome Measures

Quantitative tools are valuable in a clinical setting as they can objectively evaluate

severity at baseline, assess the effectiveness of interventions, predict outcome and provide

decision support to clinicians.64, 65 Singh et al (2005) reported the results of a survey in which

clinicians agreed on the importance of quantifying functional disability in patients with CSM.66

However, respondents also believed that specific assessment tools are underused or not ideal.

In the field of CSM, there is no gold standard for assessing disease severity, predicting outcome

21

or evaluating a patient’s improvement following intervention.67 Clinicians are therefore unable

to establish standard quantitative guidelines for CSM management.

There are several tools designed to evaluate neurological impairment, functional status

and health-related quality of life. In the AOSpine CSM-North America and International studies,

patients were assessed using the modified JOA (mJOA), the Nurick, the 30-meter walking test,

the Neck Disability Index (NDI) and the Short Form-36 (SF-36).

mJOA

The mJOA is an 18-point clinician-administered questionnaire that separately addresses

upper and lower extremity motor function, sensation and micturition. It was modified from the

JOA scale by Benzel et al (1991) to increase its applicability in the Western population.68 Some

of the differences between the JOA and mJOA include that the modified scale is in English and

evaluates a patient’s ability to use western cutlery rather than chopsticks. Factor analysis

demonstrated that the mJOA has two dimensions: 1) motor and sensory function of the

extremities and 2) sphincter dysfunction.69

Nurick

The Nurick score, developed in 1972, is the most commonly cited outcome measure in

existing literature.70, 71 It is a 6-grade ordinal scale that is primarily based on gait impairment

and employment (0=signs or symptoms of root involvement but no evidence of spinal cord

disease, V=chair bound or bedridden).

30-meter walking test

The 30-meter walk test requires a patient to stand up from a stable chair, walk on a

smooth flat surface for 15 meters, turn around, walk back and sit down.72, 73 This scale assesses

a patient’s voluntary movement, balance and coordinated activity and objectively evaluates

walking speed.

Neck Disability Index

The NDI is a 10-item patient questionnaire that evaluates functional activities (personal

care, lifting, reading, work, driving, sleeping, and recreation), pain intensity, concentration and

22

headaches.74-82 For each item, there are six possible answers: 0 is no disability and 5 is complete

disability. The total NDI is out of 100 and is calculated by summating scores from all categories

and multiplying by two.

SF-36 version 2

The SF36v2 is a multipurpose health survey, combining a mental (MCS) and physical

component score (PCS), that measures both functional status and overall quality of life.64, 73, 83,

84 It consists of eight scales including physical functioning (PF), role limitations physical (RP),

bodily pain (BP), general health perceptions (GH), vitality (VT), social functioning (SF), role

limitations emotional (RE) and mental health (MH).

1.7.1 Validity of Existing Outcome Tools

A scale’s validity is how well it measures what it is intended to. Scales are ideally

validated by correlating them with a “gold standard.”73, 85 Given that this has yet to be defined

for CSM, a scale’s validity is evaluated by correlating it with other measures and by assessing

either construct, convergent or divergent, predictive or biological validity. Table 1-5

summarizes the results from studies that have validated the SF-36, 30-meter walking test,

Nurick, JOA/mJOA and NDI.

23

Table 1-5. A Summary of Studies Evaluating the Validity of Various Measurement Tools

Scale Study Correlation with other scales Other forms of validity

SF-36 Baron et al. (2006)86 Guilfoyle et al. (2009)87 King et al. (2002)88 King et al. (2004)89 King et al. (2003)90 Latimer et al. (2002)91 Thakar et al. (2009)92

MDI and PF (-0.76), MDI and PF (-0.56), MDI and MH (-0.39) VAS and BP (-0.57) HADS-A and PF (-0.46), HADS-A and BP (-0.58), HADS-D and MH (-0.63), HADS-D and PF (-0.63), HADS-D and BP (-0.67) Correlations between SF-36 PF and MH were not significant. Spearman’s rank correlation SF-36 PCS: Standard gamble (p=0.119), Time trade-off (p=0.055), Willingness to pay (p=0.394), VAS (p<0.001) SF-36 MCS: Standard gamble (p=0.052), Time trade-off (p=0.181), Willingness to pay (p=0.831), VAS (p=0.046) Lower PCS was correlated with worse myelopathy scores on Nurick, Cooper leg subscale and Harsh scale. SF-36 physical domains are significantly correlated to both the NDI and MDI.

Item-own scale correlations exceeded 0.40. RP, BP, SF, RE demonstrated convergent and discriminant validity (item-own > item-other correlations by 2 standard errors). PF, GH and MH had 1 item that failed to satisfy this criterion. VT had 2 out of 4 items that had identical correlations with other scales. Inter-correlations between scales ranged from 0.20-0.62. Predictive validity: Preoperative SF-36 physical function was predictive of postoperative MDI (ρ=-0.48 to -0.60). Better functioning on the Nurick, Cooper leg subscale and Harsh scale was strongly associated with higher scores on the SF-36 PF, RP, GH (except for Cooper leg subscale), SF and PCS scales (p<0.05) (construct validity). Moderate correlation between the mJOA score and SF-36 (p=0.017-0.171). Strong association between lower limb JOA and SF-36 (p<0.006). Low correlation between Cooper arm subscale scores and SF-36 (p>0.328). Validity is demonstrated by equivalent changes in the VAS, NDI and MDI. Relationship between mobility-related items and Nurick (construct validity). Correlations between scales were fair to moderate except for PF and RE, RP and GH, RE and GH which were poor (discriminant validity). Associations between PF, RP, BP and PCS ranged from 0.67-0.74 (convergent validity). Associations between VT, SF, RE, MH and MCS ranged from 0.67-0.72 (convergent validity).

24

30-m walking test time

Nakashima et al. (2011)93 Singh et al. (1999)72

R2 (Preoperative, Postoperative) JOA (0.19, 0.18), JOACMEQ (0.32, 0.31) Preoperative, Postoperative MDI (0.653, 0.57), Nurick (0.61, 0.69)

30-m steps

Nakashima et al. (2011)93

R2 (Preoperative, Postoperative) JOA (0.20, 0.17), JOACMEQ (0.33, 0.26)

Nurick Revanappa et al. (2011)94 Rajshekhar et al. (2007)95 Vitzthum et al. (2007)96 Singh et al. (2001)73 King et al. (2004)89 King et al. (2005)97

Preoperative, Postoperative: llmJOA (0.901, 0.886), mJOA (0.846, 0.862) Recovery rate (RR): llmJOA RR (0.840), tmJOA RR (0.793) Change: llmJOA change (0.737), tmJOA change (0.679) Recovery rate: patient perceived outcome (whole group, 0.62; good-grade, 0.52; poor-grade 0.79). JOA (p<0.0001), CMS of the LE (p<0.0001), EMS (p<0.0001) , CMS of the UE (p<0.05) Ranawat (0.71, 0.75), JOA (0.59, 0.51), SF-36 (0.38, 0.36) Cuzick’s nonparametric test for trend: VAS (p=0.007), Time trade-off (p<0.001), Willingness to pay (p=0.113), Standard gamble (p=0.108) Not correlated with patient reports of improvement

JOA/mJOA Vitzthum et al. (2007)96 Singh et al. (2001)73 King et al. (2004)89 King et al. (2005)97 Kopjar et al. (2014)69

CMS of the LE (P<0.0001), EMS (p<0.0001), CMS of the UE (p<0.001) Preoperative, Postoperative: SF-36 (0.38, 0.37) Cuzick’s nonparametric test for trend: Standard gamble (p=0.164), Time trade-off (p=0.024), Willingness to pay (p=0.010), VAS (p=0.299) Not correlated with patient reports of improvement mJOA: Nurick score (-0.625), NDI (-0.343), PCS (0.300), MCS (0.245), 30-meter walking test (-0.382)

Not correlated with components of the SF36v2: BP (0.056), GH (0.156), MH (0.210), RE (0.274), SF (0.219) or vitality (0.161) (divergent validity)

NDI Young et al. (2010)81 Riddle et al. (1998)79

MCS (0.47), PCS (0.53)

Poor construct validity Lower scores for NDI in patients whose work status was altered or who were undergoing litigation (construct validity)

25

Cleland et al. (2006)75

Change scores with GROC (0.19) Change scores with NPRS (0.61)

RP: role limitations physical; BP: bodily pain; SF: social functioning; RE: role limitations emotional; PF: physical functioning; GH: general health; MH: mental health; VT: vitality;

PCS: physical component score; MCS: mental component score; SF-36: short form-36; NDI: neck disability index; MDI: myelopathy disability index; VAS: visual analog scale;

(m)JOA: (modified) Japanese orthopaedic association; HADS: hospital anxiety depression scale; EMS: European myelopathy score; JOACMEQ: Japanese orthopaedic association

cervical myelopathy evaluation questionnaire; llmJOA: lower limb mJOA; tmJOA: total mJOA; CMS: Cooper myelopathy scale; LE: lower extremity; UE: upper extremity; GROC:

global rating of change; NPRS: numeric pain rating scale;

26

1.7.2 Reliability of Existing Outcome Measures

Inter-observer reliability is the agreement between two or more raters whereas Intra-

observer reliability is the agreement between two ratings made by a single observer on the

same patient.67 Table 1-6 summarizes what is known about the reliability of the JOA, 30-meter

walking test, NDI and SF-36.

Table 1-6. Reliability of the JOA, 30-meter Walking Test, NDI and SF-36

Measurement Study Interobserver Reliability

Test-retest reliability

JOA (Total score) Motor function Fingers Shoulder and elbow Lower extremity Sensory function Upper extremity Trunk Lower extremity Bladder function

Yonenbou et al. (2001)98

ICC=0.813 κ=0.534 (77.7%) κ=0.305 (82.3%) κ=0.488 (62.3%) κ=0.421 (67.7%) κ=0.579 (78.5%) κ=0.339 (62.3%) κ=0.469 (75.4%)

ICC=0.826 Κ (proportion of agreement) κ=0.678 (73.1%) κ=0.501 (82.9%) κ=0.547 (62.2%) κ=0.510 (64.7%) κ=0.537 (75.7%) κ=0.436 (57.1%) κ=0.643 (75.1%)

30m walking test Nakashima et al. (2011)93 Singh et al. (1999)72

Not reported Differences (SD) between 1st and 2nd measurements Preoperative: 0.95 (1.66) Postoperative: 0.89 (1.33) p=0.995 (low variability between trials)

NDI Young et al. (2010)81

Not reported Test-retest over 4 weeks ICC=0.55

SF-36 Physical functioning Social functioning Role limitations Physical Role limitations Emotional Bodily Pain Mental health Vitality General health perception

Brazier et al. (1992)84

Not reported Test-retest (2 week interval) R=0.81 R=0.60 R=0.69 R=0.63 R=0.78 R=0.75 R=0.80 R=0.80

JOA: Japanese orthopaedic association; ICC: intraclass correlation; K: kappa coefficient of Kraemer; SD: standard

deviation; NDI: neck disability index; P: physical; E: emotional

1.7.3 Responsiveness of Existing Outcome Tools

Responsiveness is a scale’s ability to detect clinically significant changes and distinguish

between disease severities.67 Measures of responsiveness include effect sizes, areas (AUC)

27

under receiver operating curves (ROC) and ceiling or floor effects. 99 Table 1-7 displays the

responsiveness of the SF-36, subscales of the SF-36, the mJOA and the NDI.

Table 1-7 Responsiveness of the SF-36, mJOA, and NDI

Measurement Study Effect Size or AUC Floor, Ceiling Effect

SF-36

Baron et al. (2006)86 Guilfoyle et al. (2009)87 Thakar et al. (2009)92

PF (-0.43), BP (-0.62), SF (-0.52), MH (-0.55), VT (-0.70) Standardised response means: PF (0.86), BP (0.65), MH (0.54) PF (0.78), RP (0.49), BP (0.80), GH (0.39), VT (0.53), SF (0.65), RE (0.62), MH (0.54)

PF (15.4%, NS), RP (62.8%, NS) RE (35.1%, 51.8%) RP (50.9%, 20.4%) SF (NS, 22.9%) RE (29.0, 52.1%) RP (64.3%, NS) BP (30%, NS) SF (17.1%, NS) RE (71.4%, NS)

mJOA Kopjar et al (2014)69 1.00

NDI Bolton et al. (2004)82 Young et al. (2010)81

0.80 AUC: improved and stable patients (0.57) AUC: stable and “smaller” clinically improved (0.61) AUC: stable and “larger” clinically improved (0.74)

SF-36: short form-36; PF: physical functioning; BP; bodily pain; SF: social functioning; MH: mental health; VT:

vitality; RP: role limitations physical; RE: role limitations emotional; GH: general health; mJOA: modified Japanese

Orthopaedic Association; NDI: neck disability index; PSFS: patient specific functional scale; AUC: area under a

receiver operating curve (i.e ability of the scale to distinguish between two groups); NS: not significant

1.8 Management and Treatment Strategies

The management of CSM remains controversial due to disagreement surrounding the

natural history of the disease. In one respect, some patients with radiological spondylosis will

remain clinically stable over time whereas others, once symptomatic, will develop deleterious

myelopathic features. Furthermore, a recent systematic review suggested that, if left

untreated, 20-60% of patients with evidence of symptomatic CSM will deteriorate over time.100

1.8.1 Non-operative Management

Non-operative treatments for CSM include physical therapy, medications (steroids,

NSAIDS, gabapentin/pregabalin), spinal injections, collars and cervical traction.15, 101 In a

systematic review of the literature, Rhee et al (2013) investigated the safety and efficacy of

28

non-operative treatment for CSM.101 This review identified four studies comparing the relative

effectiveness of conservative treatment and surgical intervention.

Kadanka et al (2002, 2011) conducted a randomized control trial to evaluate differences

in outcomes between “mild” (mJOA≥12) patients treated conservatively (intermittent bed rest,

use of collar, anti-inflammatory medication and discouragement of high-risk activities) and

those treated surgically.102, 103 The main conclusions from this study were 1) there was no

statistical difference between the non-operative and operative groups at any time point with

respect to the mJOA; 2) the timed 10-meter walk was significantly faster in the non-operative

group; and 3) activities of daily living were similar in both groups 12-120 months after

treatment initiation. This trial was significantly underpowered and the improvements shown in

the operative cohort were much lower than in other published surgical series.

Two cohort studies also examined treatment outcomes in a surgical and a non-surgical

cohort.59, 104 Sampath et al (2000) reported significant improvements in overall pain and

functional status in patients treated surgically.104 This study also determined that patients

undergoing non-operative intervention (pharmacological therapy with either narcotic or

nonsteroidal drugs, steroids, bed rest, home exercise, cervical traction, neck bracing, or spinal

injections) experienced a significant worsening in their ability to perform daily activities. In a

second study by Yoshimatsu et al (2001), 78% of surgical patients improved their JOA score,

whereas only 23% of the non-operative group (cervical continuous traction, immobilisation

using a cervical orthosis, drug therapy, exercise, orthosis therapy, thermal therapy)

demonstrated gains in functional status.59 Although both studies did not directly compare

outcomes between treatment groups, the recorded differences in improvements suggest that

surgery may be more effective for managing these patients.101

Based on this evidence, Rhee et al (2013) developed a clinical recommendation:

“because myelopathy is known to be a typically progressive disorder and there is little evidence

that non-operative treatment halts or reverses its progression, we recommend not routinely

prescribing non-operative treatment as the primary modality in patients with moderate to

severe myelopathy.”101

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1.8.2 Surgery

Traditionally, surgery was used to halt disease progression and prevent further

neurological deterioration. However, recent results from the prospective AOSpine CSM-North

America study indicated that cervical decompression not only arrests progression but also

improves neurological outcomes, functional status and quality of life in patients with mild,

moderate and severe disease.4 Surgery is therefore increasingly recommended as the standard

treatment for CSM and, as such, we can expect the rate of surgical intervention to rise with the

aging of the population. In fact, according to Lad et al (2009), there was a 7-fold increase in the

number of spinal fusions for CSM between 1993 and 2002 in the United States.105

Surgery can be performed anteriorly or posteriorly; however, the main objective of both

approaches is to remove compressive forces, decompress the cord and provide adequate space

for the spinal cord.106 According to Fehlings et al (2013), patients treated anteriorly have more

focal pathology, are younger and have less severe myelopathy than those treated

posteriorly.107 When adjusting for these baseline characteristics, there are no significant

differences in functional and quality of life outcomes between these two approach groups.

Common anterior surgeries include discectomy and/or corpectomy and fusion and posterior

techniques include laminectomy with or without fusion and laminoplasty. Patients with more

complex degenerative pathology may also undergo a two-stage circumferential procedure.

30

Chapter 2: A Clinical and Complications Prediction Rule in Cervical

Spondylotic Myelopathy: Rationale, Objectives and Specific Aims

2.1 Definition of a Clinical Prediction Rule

A clinical prediction rule combines signs and symptoms, demographics and other

relevant factors to determine the likelihood of developing a disease, achieving a particular

treatment outcome or experiencing a complication.108 These models are valuable in a clinical

setting because 1) they estimate a patient’s probability of disease development, enabling

clinicians to identify high-risk patients, take necessary precautions and educate patients to

recognize future relevant symptoms; 2) quantifying a patient’s likely outcome provides

clinicians with the information they require to discuss the relative risks and benefits of each

treatment and to manage patients’ expectations; 3) they help align both clinician and patient

perceptions of outcome with more objective evidence; and 4) they influence practice by

providing an evidence-based decision aid to clinicians.

2.2 The APACHE II Score and the Model for End-Stage Liver Disease

The APACHE II score and the model for end-stage liver disease (MELD) are two examples

of clinical prediction rules that are valuable in a clinical setting, especially to identify high-risk

patients. The APACHE II score is an intensive care unit scoring system that incorporates 12

physiological measurements, patient age and medical history to assess disease severity and risk

of mortality.109 This score is used to identify life-threatening physiologic problems and to guide

implementation of suitable treatment strategies. Furthermore, this knowledge can inform

concerned family members about the risk of subsequent hospital death. Another example is the

MELD score which uses a patient’s bilirubin, creatinine levels and international normalized ratio

from prothrombin time to evaluate severity of disease, estimate chance of survival and

prioritize recipients of liver transplants.110 These two examples demonstrate that clinical

prediction rules can be used to identify high-risk patients, assess disease severity and provide

prognostic information to patients and their families.

31

2.3 Clinical Prediction Rules in Traumatic Spinal Cord Injury

In the field of spinal cord injury, two models demonstrate how clinical prediction rules

can direct personalized treatment programs, manage patients’ expectations and ultimately

influence clinical practice.111, 112 Both studies were conducted in patients suffering from

traumatic spinal cord injury (tSCI) and were developed to predict long-term functional

outcomes and probability of ambulation. TSCI can impact a patient’s physical, psychological and

social well-being as well as impose substantial financial burden on health care systems.

Therefore, predicting a patient’s functional independence and ambulation following injury can

help educate these patients as to their likely outcome, design optimal rehabilitation strategies

and anticipate future resource utilization.111, 112

A second study by Wilson et al (2012) designed a model to predict the occurrence of

acute complications in patients with tSCI.113 These complications often result in prolongation of

hospital stay, higher rates of mortality, reduced functional recovery and increased management

costs. Clinicians should therefore anticipate these complications, institute aggressive

preventative treatments and strategize appropriate postoperative management. Furthermore,

health care providers should predict future hospital utilization costs for each patient and

allocate resources accordingly. A complications prediction model can provide valuable

information that allows both clinicians and health care providers to identify high risk patients,

develop effective management strategies and lessen future cost burden.

2.4 Rationale, Objectives and Specific Aims

2.4.1 Rationale

Given these benefits in acute SCI, we hypothesized that prediction models would also be

valuable in a surgical CSM setting to predict functional recovery and risk of complications.

Specifically, a model predicting surgical outcomes can be used by clinicians to counsel patients

and their families as to potential treatment options, manage patients’ expectations and identify

ways to optimize results. A complications prediction model, on the other hand, can help

surgeons detect their high risk patients, accurately discuss the risks and benefits of the

32

procedure with their patients and design case-specific preventative strategies. Furthermore,

knowledge of a patient’s likely outcome and risk of complications can allow health care

providers to anticipate future costs, allocate resources accordingly and strategize cost-effective

postoperative care.

Predicting surgical outcome is an increasingly important research topic as surgery is

recommended as the preferred management strategy for patients with CSM. Davidson et al

(2010) suggested that patient satisfaction is closely linked to his/her expectation of outcome.114

Patients who achieve their expected outcome are more likely to be satisfied with the results of

their treatment than those who had unrealistic expectations. It is therefore essential that

clinicians are able to objectively predict a patient’s likely outcome and accurately convey this

information during the surgical consent discussion. In some cases of CSM, the histological

damage to the spinal cord is irreversible and will persist even after surgical decompression.

These patients should be informed that, although they are likely to benefit from surgery, they

will still exhibit some residual neurologic impairment and may continue to require assistance in

daily living.

According to Davidson et al (2010), there is also substantial variability between clinicians

in terms of the information they convey to their SCI patients.114 In this study, a questionnaire

was distributed to spine surgeons across North America who specialized in the management of

tSCI. This survey was designed to evaluate the type of prognostic information surgeons provide

to their injured patients and to assess the variability of responses across practices. The

questionnaire presented various case vignettes and asked questions such as “how long will it

take this patient to return to work?” “what are the chances this patient will be free of back pain

and stiffness 1-year after surgery?” and “what do you tell your patient are the chances for

functional recovery in his/her lower extremities?” Ideally, all surgeons would be well-informed

of existing prognostic literature and provide similar responses to these questions. This, however

was not the case; the results indicated substantial variability in the information surgeons

provide to their patients about how they are expected to fare following intervention. Similarly,

in CSM, surgeons often have different perceptions as to their patients’ surgical prognosis. This

is likely due to the controversy in the literature surrounding the most important clinical and

33

imaging predictors of surgical outcome. Furthermore, there is no quantitative tool that can be

used across centers to objectively quantify a patient’s likely outcome. Such tool would ensure

that consistent and accurate information is being conveyed to these patients and aid in the

alignment of surgeons’ perceptions with more objective evidence.

There is also an increasing need to effectively predict intraoperative and postoperative

complications in CSM patients. Surgery, although proven highly effective, is not risk free and is

associated with complications in 11 to 38% of patients.10, 11 The majority of these are transient,

non-neurological and do not require invasive intervention or prolonged hospital stay.115

Regardless, surgical complications still taint a patient’s overall perception of surgery and often

involve more rigorous postoperative management, additional follow-up visits and increased

costs. Therefore, surgeons should better anticipate these complications, institute preventative

strategies, and closely monitor their patients in the perioperative period. A complication

prediction rule can be used by surgeons to identify their high risk patients, educate their

patients as to the relative risks and benefits of the procedure and strategize intraoperative and

postoperative care. At the Annual Meeting of the Cervical Spine Research Society (2014), Dr.

Hecht, in his discussion of surgical complications, said “an informed patient is a lot more

understanding.” This means that a patient who is aware that he/she is at a higher risk of a

experiencing a complication is less likely to be surprised or dissatisfied if that complication

occurs. It is therefore essential that surgeons objectively quantify risk of complications and

discuss these estimates with their patients during the consent process as a means to manage

expectations. Furthermore, this information will enable health care providers to anticipate

hospital utilization costs, allocate sufficient resources and implement effective postoperative

management strategies.

There is, however, little consensus as to what patient characteristics, imaging

parameters and surgical factors are important predictors of perioperative complications.

Furthermore, there are few studies comparing complications rates between anterior and

posterior surgery, laminoplasty and laminectomy with fusion and various laminoplasty

techniques. These knowledge gaps must be addressed as a first step to developing a

34

complications model that could be used by clinicians to predict a patient’s risk of intraoperative

or perioperative complications.

Along with the clinical benefits of these two models, it is also important for researchers

to be familiar with important predictors of outcome and complications before designing and

conducting therapeutic clinical trials, decision analysis projects and cost-effectiveness studies.

In order to appropriately divide patients into more homogenous subgroups for analysis,

researchers require an understanding of how certain clinical, imaging and surgical factors may

impact a patient’s outcome in the long term or risk of complications.

2.4.2 Knowledge Gaps in the Literature

As will become evident in Chapters 3 and 4, there is little consensus in the literature and

among professionals as to what are the most important predictors of functional status and

complications. Limitations in the methodology of previous studies prevent the formation of

strong evidence-based recommendations and the incorporation of prediction into guidelines

for CSM management. These include:

1) The definition of CSM varies from study to study; some include patients with all forms of

degenerative myelopathy, whereas others only include those with myelopathy

secondary to spondylosis or OPLL. The nomenclature for CSM needs to be

internationally unified and perhaps should be expanded to encompass all degenerative

forms of cervical myelopathy, including OPLL.3

2) There is a paucity of information in the form of high quality, multicenter prospective

studies. The majority of published prediction studies are retrospective. Furthermore,

the sample sizes of previous analyses are typically fewer than 100 patients.

3) Many of the measurement tools used to evaluate outcome have not been validated,

including the Cooper scale, neurosurgical cervical spine scale, neurological assessments,

questionnaires and evaluation of symptom improvement. Future studies should aim to

develop more sensitive and specific outcome measures to assess neurologic and

functional status in CSM patients. However, until this is done, the Nurick score and the

recently validated mJOA are good alternatives.

35

4) The definitions of complications have not been standardized across centers, leading to

both underreporting and over-reporting of rates. For example, some surgeons believe

that every patient treated anteriorly will suffer “dysphagia” whereas others do not

classify “trouble swallowing” as a complication but rather a normal event after an

anterior operation. Future studies should focus on developing guidelines to better

define complications and to distinguish between surgery-related and unrelated adverse

events.

In order to fill these knowledge gaps and add to the current body of literature, we wish

to evaluate key predictors of functional outcomes and perioperative complications using

prospectively-collected data from several spine centers around the world.

2.4.3 Objectives and Specific Aims

This thesis has two primary objectives. The first is to develop and validate a clinical

prediction rule used to determine functional outcomes in patients with CSM undergoing

surgery. This model will be constructed using data collected from two prospective, multicenter

studies funded by AOSpine North America and AOSpine International. However, before

analyzing this data, we will first design a theoretical framework using evidence from the

literature as well as professional opinion obtained from a survey. The following are specific

aims for this objective to ensure the model is both valid and generalizable in future

populations:

Specific Aim #1: To conduct a systematic review of the literature to determine the most

important clinical and imaging predictors of surgical outcome (Chapter 3).

Specific Aim #2: To survey members of AOSpine International to see what spine professionals

view as the most significant predictors of surgical outcome (Chapter 4).

Specific Aim #3: To establish the minimal clinically important difference (MCID) of the mJOA

and determine an appropriate cut-off between an “optimal” and “suboptimal” surgical

outcome (Chapter 6).

36

Specific Aim #4: To develop a clinical prediction rule to predict functional status at 1-year

following surgery using data on 278 patients enrolled in the multicenter, prospective AOSpine

CSM-North America study at 12 North American sites (Chapter 7).

Specific Aim #5: To externally validate this prediction model using data on 479 patients enrolled

in the prospective multicenter AOSpine CSM-International study at 16 global sites (Chapter 8).

Specific Aim #6: To evaluate whether certain MRI parameters contribute to the predictive

performance of our validated North American prediction model (Chapter 9).

Specific Aim #7: To identify limitations in our North American model and address these using

combined data from the North American and International studies (Chapter 10).

By achieving aims 1-7, we can develop a valid and globally relevant prediction model

that can be implemented into clinical practice to guide decisions, manage expectations and

counsel patients as to potential treatment options.

The second objective of this thesis is to determine significant clinical and surgical

predictors of perioperative complications and to develop a complications prediction rule that

clinicians can use to objectively quantify risk. Similar to our first objective, this model will first

be conceptually designed using results from a systematic review and a survey and then

formulated using data from the AOSpine CSM-International study. The following are specific

aims of our second objective:

Specific Aim #8: To perform a systematic review of the literature to identify key clinical and

surgical predictors of perioperative complications and to evaluate whether rates of

complications differ across treatment groups (ex. Anterior versus posterior surgery) (Chapter

3).

Specific Aim #9: To survey members of AOSpine International to determine what spine

professionals believe are the most important predictors of complications.

Specific Aim #10: To analyze data from the multicenter, prospective CSM-International study

and construct a complications prediction rule.

37

By achieving aims 8-10, we can develop a preliminary complications prediction model

that can be used by clinicians to identify their high risk patients. This knowledge should

encourage surgeons to institute rigorous case-specific preventative strategies, inform patients

as to the relative risks and benefits of their operation and strategize postoperative care. This

model must be validated externally before it is implemented into clinical practice.

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Chapter 3: Identifying Significant Predictors of Surgical Outcome and

Complications: Results from Systematic Reviews of the Literature

3.1 Introduction

This chapter presents the results from three systematic reviews conducted to answer

the following clinical questions:

Part A: What are the most important clinical predictors of surgical outcome?

Part B: Are there MRI characteristics that can predict surgical outcome?

Part C: Key question (KQ) 1: Are there clinical, imaging or surgical factors associated with

intraoperative or postoperative complications? KQ 2: Do rates of complications differ across

surgical interventions?

The results of these systematic reviews will be used to conceptually design our two

prediction models and to justify the inclusion of certain variables regardless of their statistical

significance. Part A was designed to determine the most significant clinical predictors of

outcome in patients undergoing surgery for CSM. Holly et al (2009) performed a similar

systematic review to examine the predictive value of various clinical factors, including motor

and sensory evoked potentials, age, duration of symptoms and preoperative neurological

severity.116 This study used the Cochrane database, the National Library of Medicine database

and the reference lists of specific articles to locate relevant literature published between 1966

and 2007. This review summarized the findings from 14 studies; the main conclusions were 1)

normal preoperative median nerve potentials and/or normalization of these potentials in early

surgery predict favorable outcomes, 2) there is insufficient evidence that motor evoked

potentials predict surgical outcome and 3) class III evidence (low) suggests that age and

duration of symptoms are significant predictors of outcome.

Holly et al. (2009) identified several limitations of their review including 1) there was an

inadequate number of prospective studies, 2) too many studies assessed outcome using un-

validated measures and 3) it was challenging to combine and interpret results across studies

39

due to the use of different scales.116 Our systematic review provides an up-to-date summary of

the literature and attempts to address these limitations by increasing the pool of articles

analyzed. In addition, the mJOA has since been validated, improving the level of evidence of

each study that used this scale to evaluate outcome. Our review will also investigate the

predictive value of other clinical factors, including co-morbidities, smoking status, signs and

symptoms.

Our second systematic review (part B) was performed to identify significant imaging

predictors of surgical outcome and to evaluate the predictive value of the MRI. A previous

review by Karpova et al (2013) summarized the results from 30 studies that assessed the

relationship between surgical outcome and various imaging parameters.117 The main

conclusions from this review were that both transverse area and signal change characteristics

are predictive of outcome. This review identified several limitations in the literature, including

poorly-controlled statistical analyses, selection and measurement bias and potential

overestimation of significance. Although our review cannot address these study limitations, we

only consolidated evidence from the highest quality studies.

Our final systematic review (part C) aimed to identify important clinical and surgical

predictors of intra- and post-operative complications and to evaluate differences in

complication rates across surgical interventions. It is unclear whether patient characteristics

(age and myelopathy severity), imaging parameters (transverse area and signal change) and

surgical factors (number of decompressed levels and estimated blood loss) are important

predictors of complications. Knowledge of these key predictors will allow clinicians to identify

high-risk patients and institute rigorous preoperative and intraoperative preventative

measures. In addition, we lack comparative measures of complication rates between anterior

and posterior surgery, laminoplasty and laminectomy with fusion, anterior decompression and

laminoplasty, and various laminoplasty techniques. This knowledge gap is important and needs

to be filled to help surgeons balance the risks and benefits of each procedure. This study is the

first systematic review exploring significant predictors of surgical complications in patients with

CSM.

40

3.2 Overview of Common Methods

The systematic reviews were formatted based on the Preferred Reporting Items for

Systematic Reviews and Meta-Analyses statement. Accordingly, the materials and methods

section for each review consisted of eligibility criteria, study characteristics, information

sources, search strategy, study selection, data extraction and synthesis, risk of bias in individual

studies and risk of bias across studies.

3.2.1 Eligibility Criteria

A “PPO” table was constructed for each review to outline the target patient population,

prognostic variables of interest and outcome.

Patient Population All of our reviews targeted studies of adult patients (>18 year) with cervical myelopathy

secondary to spondylosis, disc herniation, OPLL, congenital stenosis and/or subluxation. All

patients were treated surgically and followed up postoperatively. Studies were excluded if they

included patients with traumatic spinal cord injury, thoracic or lumbar myelopathy, tumor,

infection, radiculopathy or other non-degenerative forms of myelopathy.

Prognostic Variables (Table 3-1) For part A, we were interested in studies that assessed the predictive value of various

clinical factors such as age, duration of symptoms, baseline severity score or gender.

For part B, we focused on studies designed to evaluate the association between

preoperative MRI factors and surgical outcome. We included cohort studies that assessed the

predictive value of either MRI-specific characteristics (e.g., signal intensity) or anatomical

characteristics assessed by MRI (e.g., spinal canal diameter or number of compressed

intervertebral discs).

41

For part C, we sought studies that evaluated various predictors of complications,

including clinical (ex. age, co-morbidities and body mass index (BMI)), imaging (ex. cervical

alignment, transverse area and signal change) and surgical (ex. approach, operative duration

and estimated blood loss) factors. We were also interested in studies that compared

complication rates between different surgical approaches or techniques. Studies were excluded

if their focus was on the predictive value of neuro-monitoring or surgical materials.

Table 3-1. Relevant Prognostic Factors for Systematic Reviews A, B and C

Inclusion Exclusion

Prognostic

factors: A

Age, duration of symptoms, baseline severity score, neurological signs, patient-reported symptoms, co-morbidities, smoking status, gender, race, disease progression, onset of disease.

Imaging factors

Surgical factors

Prognostic

factors: B

Intramedullary signal changes (T1 & T2-weighted MRI): absence/presence; high/low; ratio

Anatomical factors: transverse area, anteroposterior diameter, number of prolapsed intervertebral discs/number of compressed segments, level of maximum compression, rate of flattening on cord, compression ratio, circumferential maximum compression

Shape of OPLL lesion

Clinical factors

Surgical factors

Factors from other diagnostic modalities (ex. Anteroposterior diameter determined by a radiograph)

Prognostic

factors: C

Clinical factors: age, duration of symptoms, baseline severity score, signs, symptoms, co-morbidities, smoking status, gender, BMI, diagnosis

Imaging factors: signal changes on T1- or T2-weighted MRIs, transverse area, anteroposterior diameter, number of compressed segments, level of maximum compression, compression ratio, shape of OPLL lesion, cervical alignment, range of motion

Surgical factors: approach, technique, number of operated segments, number of stages, fusion, operative time, estimated blood loss

Different types of surgical materials (ex. drills, grafting material)

Neuromonitoring

OPLL: ossification of the posterior longitudinal ligament; BMI: body mass index; MRI: magnetic resonance image

Outcomes (Table 3-2)

Outcome for part A and B was evaluated using functional measures, including the mJOA,

Nurick score and 30-meter timed walking test as well as health related quality of life

questionnaires such as the NDI and the SF-36. In part B, studies were excluded if the primary

42

outcome was radiographic, a non-union, a subjective neurological assessment or a

complication.

To be eligible for part C, the study had to report on either overall postoperative

complication rates or rates of specific complications such as C5 nerve root palsy, dysphagia,

infection or instability. Studies that focused on predictors of heterotopic ossification,

progression of myelopathy or adjacent segment degeneration were excluded as these can be

considered three separate, stand-alone topics. Studies that defined axial pain and

postoperative kyphosis as surgical complications were included. However, those that discussed

axial pain and sagittal alignment as measures of pain and radiographic outcomes, respectively,

were excluded.

Table 3-2. Relevant Outcomes for Systematic Reviews A, B and C

Inclusion Exclusion

Outcome: A Functional outcomes

Clinical outcomes

Health-related quality of life

Studies were not excluded based on assessment tool

Outcome: B Functional outcomes using a validated measure (ex. mJOA/Nurick score/30-m walking test)

Patient reported outcomes (SF-36 or NDI)

Radiographic outcomes

Nonunions

Subjective neurological assessments (ex. Status was “improved,” “unchanged,” or “worse.”

Complications such as infection, pseudoarthrosis, dysphagia, etc.

Outcome: C Postoperative Complications: nerve root palsy, axial pain, surgical, dysphagia, infection, instability, systemic

Adjacent segment degeneration

Heterotopic ossification

Progression of myelopathy

mJOA: modified Japanese Orthopaedic Association; SF-36: short form-36; NDI: neck disability index

3.2.2 Study Characteristics

Part A: We focused on studies that used multivariate analyses to evaluate the

association between various clinical factors and surgical outcome. Results from all relevant

studies were still summarized in this review but were not included when rating the overall body

of evidence.

43

Part B: We limited study selection to those that assessed the predictive value of various

MRI parameters using multivariate analyses that controlled for at least two of the following

three covariates: age, duration of symptoms and severity of myelopathy.

Part C: For KQ1, we sought cohort studies designed to evaluate the association between

complications and clinical, imaging and surgical factors using a multivariate analysis. For the

sake of thoroughness, studies that did not conduct a multivariate analysis were still

summarized but were not included when rating the overall body of evidence. For KQ2, we

focused on cohort or case-control studies that measured the association between different

surgical interventions and complications, while controlling for important confounders. Studies

were excluded if they reported the incidence of complications following a specific surgical

technique but did not compare this to another procedure. For both questions, studies had to

report p-values to be included.

The following types of studies were excluded from each reveiw: review articles, letters,

editorials, commentaries, meeting abstracts and books; those with less than 10-15 patients;

and animal or biomechanical studies.

3.2.3 Information Sources

A systematic search of MEDLINE, MEDLINE in Process, EMBASE and/or Cochrane Central

Register of Controlled Trials was conducted to identify relevant studies.

3.2.4 Search Strategy

We developed a search strategy with a librarian who specializes in neuroscience

research. The strategy was first developed in MEDLINE and then appropriately modified for the

other 2-3 databases. It was reviewed thoroughly by two researchers to ensure accuracy and to

confirm that all disease-related synonyms were included. We used the following search terms

to search all databases:

Part A: Cervical Spondylotic Myelopathy AND Surgery or Postoperative AND

Prediction/Prognosis AND observational studies.

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Part B: Cervical Spondylotic Myelopathy OR Ossification of the Posterior Longitudinal Ligament

AND Magnetic Resonance Imaging AND comparative study, randomized control trial, clinical

trial.

Part C: Cervical Spondylotic Myelopathy OR Ossification of the Posterior Longitudinal Ligament

AND Prediction or Risk of Complications arising from Surgery or Neurosurgery.

Only studies on humans and written in English were considered for inclusion, with no

other limits applied.

3.2.5 Study Selection

All abstracts and titles were reviewed independently in an unblinded, standardized

manner by two independent reviewers. The abstracts were sorted by our pre-defined inclusion

criteria and were classified as relevant, possibly relevant or irrelevant. Full text investigation of

the “possibly relevant” studies was done for further clarification. Disagreement between

reviewers was resolved through discussion.

3.2.6 Data Extraction and Synthesis

The following data was extracted from each included article: study design; patient

sample and characteristics, including diagnosis and treatment administered; clinical, imaging

and/or surgical prognostic factors evaluated; outcome measure or primary complication

outcome; and results of association, including odds ratios, confidence intervals and p-values.

3.2.7 Risk of Bias in Individual Studies

The class of evidence for each article was rated (Class I, II, III, IV) independently by two

reviewers using criteria outlined by the Journal of Bone and Joint Surgery for prognostic and

therapeutic studies and modified to encompass both methodological quality and risk of bias.118

Prognostic studies included in each review were all cohort studies and were assessed based on

whether patients were at a similar time point in their disease/treatment, the rate of follow-up

and whether the analysis controlled for important confounders (Table 3-3).

45

Table 3-3. Definition of the Different Levels of Evidence for Prognostic Studies

Class Bias Risk Study design Criteria

I Low risk: Study adheres to commonly held tenets of high quality design, execution and avoidance of bias

Good quality cohort Prospective design

Patients at similar point in the course of their disease or treatment

Follow up rate of 80%

Patients followed long enough for outcomes to occur

Accounting for other prognostic factors

II Moderately low risk: Study has potential for some bias; does not meet all criteria for class I but deficiencies not likely to invalidate results or introduce significant bias

Moderate quality cohort Prospective design, with violation of one of the other criteria for good quality cohort study

Retrospective design, meeting all the rest of the criteria in class I

III Moderately high risk: Study has flaws in design and/or execution that increase potential for bias that may invalidate study results

Poor quality cohort Prospective design with violation of 2 or more criteria for good quality cohort

Good quality case-control or cross-sectional study

Retrospective design with violation of 1 or more criteria for good quality cohort

A good case-control study

A good cross-sectional study

IV High risk: Study has significant potential for bias; does not include design features geared toward minimizing bias and/or does not have a comparison group

Poor quality case-control or cross-sectional

Other than a good case-control study

Other than a good cross-sectional study

Case series Any case series design

Therapeutic studies included in systematic review C (KQ2) were rated based on study

design; patient selection; if the analysis included intention to treat; blinded assessment;

whether cointerventions were applied equally; rates of follow-up; statistical power; and control

for confounders. The level of evidence for therapeutic studies in review C was judged based on

how the study was designed to compare and analyze rates of complications across surgical

cohorts (Table 3-4).

3.2.8 Risk of Bias Across Studies

The overall body of evidence was then assessed using a scoring system developed by the

Grades of Recommendation Assessment, Development and Evaluation (GRADE) working group

with recommendations from the Agency for Healthcare Research and Quality (AHQR).119, 120

46

Table 3-4. Definition of the Different Levels of Evidence for Therapeutic Studies

Class Bias Risk Study design Criteria

I Low risk: Study adheres to commonly held tenets of high quality design, execution and avoidance of bias

Good quality RCT Random sequence generation

Concealment

Intent to treat analysis

Blind or independent assessment for important outcomes

Co-interventions applied equally


Adequate sample size

II Moderately low risk: Study has potential for some bias; study does not meet all criteria for class I, but deficiencies not likely to invalidate results or introduce significant bias

Moderate or poor quality RCT

Violation of one of the criteria for good quality RCT

Good quality cohort Blind or independent assessment in a prospective study, or use of reliable data in a retrospective study

Co-interventions applied equally


Adequate sample size

Controlling for possible confounding

III Moderately high risk: Study has significant flaws in design and/or execution that increase potential for bias that may invalidate study results

Moderate or poor quality cohort

Violation of any of the criteria for good quality cohort

Case-control Any case-control design

IV High risk: Study has significant potential for bias; lack of comparison group precludes direct assessment of important outcomes

Case series Any case series design

The initial strength of the overall body of evidence was considered HIGH if the majority

of the studies were Class I or II and LOW if the majority of the studies were Class III or IV. The

body of evidence may be downgraded one or two levels based on the following criteria: 1)

inconsistency of results, 2) indirectness of evidence, 3) imprecision of the effect estimates (e.g.,

wide confidence intervals) or 4) non-a priori statement of subgroup analyses. The body of

evidence may be upgraded one or two levels based on the following criteria: 1) large magnitude

of effect or 2) dose-response gradient. The final overall strength of the body of literature

expresses our confidence in the estimate of effect and the impact that further research may

have on the results. An overall strength of “HIGH” means we have high confidence that the

47

evidence reflects the true effect. Further research is very unlikely to change our confidence in

the estimate of effect. An overall strength of “MODERATE” means we have moderate

confidence that the evidence reflects the true effect. Further research may change our

confidence in the estimate of effect and may change the estimate. A grade of “LOW” means we

have low confidence that the evidence reflects the true effect. Further research is likely to

change our confidence in the estimate of effect and likely to change the estimate. Finally, a

grade of “INSUFFICIENT” means that evidence either is unavailable or does not permit a

conclusion (Table 3-5).

Table 3-5. Overview of Grade: Reasons for Upgrading and Downgrading Level of Evidence

Initial Strength Reasons for Downgrade Reasons for Upgrade

High: if majority of studies are Class I or Class II. Low: if the majority of studies are Class III or IV

Inconsistent results: If effect sizes indicate the same direction of effect and estimates are similar across studies, the body of evidence is judged to be consistent. Single study conclusions are classified as “consistency unknown” and downgraded. Indirectness of evidence: Evidence is direct if it reflects a single, direct link between the interventions of interest and the ultimate health outcome. Raters must determine whether the most clinically relevant outcome is measured or whether a surrogate outcome is assessed. Imprecision of the effect estimate: Pertains to the degree of certainty surrounding an estimate of effect for a specific outcome. Studies may be imprecise if they include relatively few patients or if the outcome is rare. This may lead to wide confidence intervals and unstable effect estimates. Risk of bias: Study has several other limitations including in its study design.

Large magnitude of effect: Add 1 point if RR or OR is >2 or <0.5 and 2 points if RR or OR is >5 or <0.2 Dose-response gradient

OR: odds ratio; RR: relative risk

3.2.9 Clinical Recommendations and Consensus Statements

Clinical recommendations or consensus statements were made based on the overall

body of evidence and classified as either “strong” or “weak”.

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3.3 Results Part A: Important Clinical Predictors of Surgical Outcome


The electronic search yielded a total of 1677 citations. After initial review of abstracts

and titles, 1,589 articles were excluded because 1) they studied patients without degenerative

myelopathy; 2) they were not prognostic studies; 3) patients were not treated surgically or

there was no follow-up; 4) only the predictive value of imaging factors was assessed; 5) there

was no distinction between patients with cervical myelopathy and those with cervical

radiculopathy; and/or 5) they were not in English. After full text review, two studies were

excluded because their samples included non-surgical patients. Of the remaining 86, 24

conducted a multivariate analysis to evaluate the predictive value of various clinical factors

(Figure 3-1).


We identified 86 studies discussing significant clinical predictors of surgical outcome. Of

these, only 24 (2 prospective, 22 retrospective) conducted a multivariate analysis. Sample sizes

ranged from 47 to 146 surgical patients, with mean ages between 43.8 to 77 years. All patients

were diagnosed with some form of degenerative cervical myelopathy, with the majority

presenting with either CSM or OPLL. Few patients (n=79) presented with cervical disc

herniation. Twenty-three studies assessed age as a predictor, 22 evaluated preoperative

myelopathy severity and 19 reported on duration of symptoms. Various outcome measures

were used across the studies, with the mJOA/JOA score or recovery rate reported the most

frequently (n=20), followed by the Nurick grade (n=4) (Table 3-6).

3.3.3 Risk of Bias

We critically appraised the 24 studies that conducted a multivariate analysis. Of these,

seven were classified as level II and 17 were rated level III. Most studies were level III evidence

because they were retrospective cohort studies with unreported follow-up rates or follow-up

rates <80%. The two prospective studies did not have a complete follow-up (<80%) and were

down-graded from level I to level II evidence. In each study, patients were at similar points in

49

the course of their disease or treatment and were followed long enough for outcomes to occur.

In addition, all analyses accounted for other prognostic factors.

Figure 3-1. Search Strategy and Detailed Review Process for Systematic Review A

3.3.4 Are there clinical factors that can predict surgical outcome?

The results from the 24 studies included in our review are summarized in Table 3-7.

3.3.4.1 Age

JOA Recovery Rate

Fourteen studies evaluated the association between a patient’s JOA recovery rate and

his/her age. Recovery rate was calculated using the following equation developed by

Hirabayashi: (postoperative JOA-preoperative JOA)/(17-preoperative JOA)X100%.121

Three studies dichotomized the recovery rate and defined an “excellent” outcome as a

recovery rate ≥ 50% and a “fair” outcome as a recovery rate < 50%.122-124 Yamazaki et al (2003)

50

and Naruse et al (2009) reported no significant difference in age between patients who

achieved an excellent outcome and those who did not. In the study by Kim et al (2008),

however, the interaction of diabetes and old age increased the patient’s risk of a poor surgical

outcome (OR 2.21, 1.15-4.23).

Seven studies reported that older patients had a less favorable surgical outcome based

on the JOA recovery rate.125-131 Chen et al (2001) aimed to examine the impact of T2-SI on

surgical prognosis and identified a significant association between patient age and recovery

rate (p=0.037). Fujimura et al (1998) and Kato et al (1998) explored predictors of recovery at

short-term (1 year) and long-term follow-up: age was a significant predictor of outcome at 5-

years postoperative in both studies. In a study by Koyanagi et al (1993), patients were divided

into three groups depending on whether their primary diagnosis was CSM, OPLL or CDH. Based

on univariate analysis, age was significantly correlated with JOA recovery rate in patients with

OPLL and CDH but not in patients with CSM. However, in multivariate analysis, age was deemed

an insignificant predictor of recovery rate in all three forms of DCM. Finally, three studies

developed linear regression equations relating a combination of significant clinical and imaging

variables to recovery rate. All three equations included age as a predictor.129-131

In contrast, four studies could not identify a significant association between age and JOA

recovery rate.132-135

mJOA/JOA

Seven studies used postoperative mJOA or JOA as the primary outcome measure. Of

these, five reported an insignificant association between age and surgical outcome.135-139 In a

study by Furlan et al (2011), age was significantly correlated with mJOA score at 6-months

(R2=0.287, p<0.0001) and 12-months (R2=0.185, p=0.0003) postoperatively.140 In multivariate

analysis, age was also a significant predictor of mJOA at 1-year (p=0.01). Morio et al (2001)

constructed a regression model using a continuous JOA score as the outcome variable and

included age as a predictor.129

51

Table 3-6. Characteristics of Prognostic Studies with Multivariate Analysis: Systematic Review A

Author (year) Study design

Sample and Characteristics Non-Clinical factors assessed Clinical factor assessed Outcome Measures

Chen et al. (2001) Retrospective cohort (III)

CSM (n=64) Male: 65.6% Mean age, yr (range): 56.67 (27-86)

SI grade on T2WI Cervical curvature Cord compression ratio

Age Gender Preoperative JOA

JOA recovery rate

Chibbaro et al. (2006) Retrospective cohort (II)

CSM (n=70) Male: 67.1% Mean age, yr (range): 57 (29-76) Mean duration of symptoms (range): 13.4 (4-120) months Surgery: anterior cervical corpectomy

SI changes on T1WI SI changes on T2WI Number of levels decompressed

Age Duration of symptoms

mJOA

Chiles et al (1999) Retrospective cohort (II)

CSM (n=57), CDH (n=22) Male: 62% Mean age, yr (range): 56 (29-87) Mean duration of symptoms (range): 16.9 months (1-120) Surgery: anterior, posterior laminectomy

Number of levels decompressed Spinal cord atrophy SI changes on T2WI

Spastic gait Diagnosis Hand wasting Preoperative mJOA

mJOA, Cooper Scale

Choi et al (2005) Retrospective cohort (III)

OPLL (n=47) Male: 76.6% Mean age, yr ±SD: 54.7±8.0 Surgery: anterior discectomy and corpectomy

Snake-eye appearance Occupying ratio Type of OPLL Double-layer sign Pavlov ratio

Age Duration of symptoms Gender Preoperative Nurick Diabetes

Nurick (≥1, <1)

Fujimura et al (1998) Retrospective cohort (III)

OPLL (n=55) Male: 83.64% Mean age, yr ±SD (range): 56.8±9.5 (38-78) Mean duration of symptoms: 18.6 months Surgery: laminoplasty

None Age Duration of symptoms Preoperative JOA

JOA recovery rate

Furlan et al (2011) Prospective cohort (II)

CSM (n=81) Male: 70.37 Mean age, yr ±SD (range): 57.04±1.36 (32-88) Mean duration of symptoms ±SD (range): 25.19±2.7 months (1-120) Surgery: anterior (n=56), posterior (n=23), anteroposterior (n=2)

None Age Gender Duration of symptoms Preoperative severity Charlson co-morbidity index Preoperative number of ICD-9 codes

Nurick mJOA Berg Balance Scale

52

Iwasaki et al (2007) Retrospective cohort (II)

OPLL (n=66) Male: 77.27% Mean age, yr (range): 57 (41-75) Surgery: laminoplasty

Occupying ratio Space available for spinal cord Shape of ossification Cervical alignment

Age Preoperative JOA score

JOA score, JOA recovery rate

Iwasaki et al (2002) Retrospective cohort (II)

OPLL (n=64) Male: 67.18% Mean age, yr (range): 56 (42-78) Surgery: laminoplasty

Occupying ratio Type of OPLL Space available for spinal cord

Age Preoperative JOA score Gender

JOA score

Kato et al. (1998) Retrospective cohort (II)

OPLL (n=44) Male: 84.09% Mean age, yr (range): 57 (39-75) Mean duration of symptoms (range): 36.8 months (3-240) Surgery: laminectomy

Number of resected laminae Occupying ratio Space available for spinal cord Type of OPLL Preoperative trauma

Age Duration of symptoms Preoperative JOA

JOA recovery rate at 1 and 5-years

Kim et al. (2008) Retrospective cohort (III)

CSM, OPLL (n=87) Male: 57% Mean age, yr (range): 62.3 (42-76) Mean duration of symptoms (range): 10 months (4–36) Surgery: laminoplasty

Increased SI on T2WI with a decreased SI on T1WI

Age Presence of diabetes Presence of diabetes and older age Presence of diabetes and smoking Duration of symptoms Preoperative JOA

JOA recovery rate

Koyanagi et al (1993) Retrospective cohort (III)

CSM (n=44), OPLL (n=39), CDH (n=20) Male: 70.87% Mean age, yr: CSM (57), OPLL (59), CDH (46) Mean duration of symptoms, months: CSM (8.6), OPLL (16.9), CDH (8.5) Surgery: laminoplasty (n=67), anterior decompression and fusion (n=33), anteroposterior (n=3)

Transverse area Flattening ratio of the spinal cord


JOA recovery rate

Morio et al (2001) Retrospective cohort (III)

CSM (n=42), OPLL (n=31) Male: 68.49% Mean age, yr (range): 64 (43-81) Surgery: laminoplasty

Transverse area Spinal cord signal intensity pattern


JOA score, JOA recovery rate

Naruse et al. (2009) Retrospective cohort (III)

CSM (n=71), OPLL (n=18), CDH (n=12) Male: 70.3% Mean age, yr ±SD: 63.6±11.6 Surgery: laminoplasty

Spinal-cord floating C2-C7 lordotic angle Local kyphosis Beak angles

Age Gender Preoperative JOA

JOA recovery rate (≥50%, <50%)

53

Okada et al. (1993) Retrospective cohort (III)

OPLL (n=23), CSM (n=34), CDH (n=17) Male: 70.3% Mean age, yr (range): 58.9 (35-83) Surgery: anterior (n=20), posterior (n=54)

SI ratio on T2WI (compressed vs. contiguous noncompressed) Transverse area of spinal cord at site of maximal compression Compression ratio of spinal cord at site of maximal compression


JOA recovery rate

Park et al. (2006) Retrospective cohort (III)

CSM (n=61), OPLL (n=11), CDH (n=8) Male: 62.5% Mean age, yr (range): 62.1 (36-86) Mean duration of symptoms (± SD): 19.1±21.1 months Surgery: anterior (n=46), posterior (n=34)

Number of compressed segments on T2WI Number of high intensity segments on T2WI Surgical method

Age Duration of symptoms Preoperative severity Diagnosis

NCSS recovery rate

Rajshekhar and Kumar (2005) Retrospective cohort (III)

CSM (n=59), OPLL (n=12) Male: 94.44% Mean age, yr (range): 49.7 (30-67) Mean duration of symptoms (range): 21.4 months (2-144) Surgery: central corpectomy

None Age Duration of symptoms Preoperative grade (4 or 5) Diagnosis (CSM or OPLL)

Nurick improvement, cure (grade of 0 or 1)

Shin et al. (2010) Retrospective cohort (III)

CSM (n=70) Male: 64.3% Mean age, yr (range): 51.1 (26-69) Mean duration of symptoms (range): 9.9 weeks (1-60) Surgery: anterior discectomy with fusion

SI grade on T2WI Length (mm) of SI on T2WI Compression ratio of spinal cord at site of maximal compression Cervical curvature Cervical stenosis


JOA recovery rate

Suri et al. (2003) Retrospective cohort (III)

CSM (n=146) Male: 79.5% Mean age, yr (range): 47.1 (17-76) Mean duration of symptoms (range): 11.7 months (1.5-120) Surgery: anterior discectomy, corpectomy, laminectomy, laminoplasty

Increased SI on T2WI with a decreased SI on T1WI Number of compressed segments (prolapsed intervertebral discs) Surgical approach


Motor improvement of symptoms, Nurick grade

Tanaka et al (1999) Retrospective cohort (III)

CSM (n=47) Male: 29.79% Mean age, yr (range): 77 (67-90) Mean duration of symptoms (range): 36 months (1 month-18 years) Surgery: laminoplasty

None Preoperative motor function score of the lower extremities Preoperative JOA Duration of lower limb disability Age Duration of symptoms

JOA score, motor function score of the lower extremities

54

Uchida et al. (2005) Retrospective cohort (III)

CSM (n=77), OPLL (n=58) Male: 62% Mean age, yr (range): 43.8 (27-73) Surgery: en bloc C3-7 open door laminoplasty (n=92), Robinson’s anterior fusion (n=15), subtotal spondylectomy at 1-2 vertebrae with interbody fusion (n=28)

SI grade on T2WI Number of compressed segments/ levels Percentage of flattening of the cord Spinal cord evoked potentials type Spinal canal narrowing (preop CT) Radiological abnormality Type of OPLL

Age Preoperative JOA Duration of symptoms

JOA score

Wada et al. (1999) Retrospective cohort (III)

CSM (n=50) Male: 72% Mean age, yr (±SD): 61.0 ± 10.9 (range, 45-81) Mean duration of symptoms (±SD): 9.1±8.5 months (range, 1-36) Surgery: open-door laminoplasty

Number of high SI segments on T2WI AP canal diameter at max compression on plain radiographs Transverse area of spinal cord at max compression on CT myelography Number of blocks on myelogram

Age Preoperative JOA Duration of symptoms

JOA recovery rate

Yamazaki et al. (2003) Retrospective cohort (III)

CSM, OPLL (n=64) Male: 51.6% Mean age, yr (± SD): 64.6 ±12.0 Mean duration of symptoms (± SD): 25.6 ± 30.6 months Surgery: laminoplasty using the spinous process splitting technique

SI changes on T2WI Canal diameter on CT myelogram Transverse area on CT myelogram


JOA recovery rate

Zhang et al. (2011) Prospective cohort (II)

CSM (n=52) Male: 57.7% Mean age, yr (range): 56.3 (45-67) Mean duration of symptoms (range): 16.1 months (3-34) Surgery: anterior (n=31), posterior (n=16), anteroposterior (n=5)

SI ratio on T2WI:T1WI Age Duration of symptoms Preoperative JOA

JOA recovery rate

Zhang et al. (2010) Retrospective cohort (III)

CSM (n=73) Male: 67.1% Mean age, yr (range) : 53.3 (34–77) Surgery: anterior, posterior, anteroposterior

SI ratio on T2WI (compressed vs. non-compressed C7-T1 levels)

Age Duration of symptoms Preoperative JOA Babinski sign

JOA recovery rate

Level of evidence of each study is given in the first column; CSM: cervical spondylotic myelopathy; SI: signal intensity; WI: weighted image; (m)JOA: modified Japanese

Orthopaedic Association; CDH: cervical disc herniation; OPLL: ossification of the posterior longitudinal ligament; SD: standard deviation; ICD: international classification

of disease; NCSS: neurosurgical cervical spine scale; CT: computed tomography

55

Nurick

In three studies, the Nurick score was dichotomized: a “poor” neurologic outcome was

defined as either no change or a decrease in the Nurick grade and a “good” neurologic outcome

as an increase of at least one Nurick grade.141-143 In studies by Choi et al (2005) and Rajshekar

and Kumar (2005), age was not a significant predictor of outcome. However, according to Suri

et al (2003), patients in the <40 age group were 2.17 times more likely to exhibit improvement

on the Nurick than patients aged 40-60 years (p<0.001). In a fourth study, Furlan et al (2011)

identified a significant association between Nurick score at 1-year and age (p=0.015).140

3.3.4.2 Duration of symptoms

JOA Recovery Rate

Ten studies evaluated the association between JOA recovery rate and preoperative

duration of symptoms. In the study by Yamazaki et al (2003), duration of symptoms was a

significant predictor of an “excellent” recovery (≥50%) in patients aged ≥65 years, but not in

those aged <65 years.122 According to Kim et al (2008), duration of symptoms was not a

significant predictor of a recovery rate ≥50%.124

Fujimura et al (1998) identified that patients with a longer duration of symptoms had a

worse JOA recovery rate at both short-term (1 and 3 years) and long-term follow-up.126 This

finding was also supported by Koyanagi et al (1993): duration of symptoms was a significant

predictor of outcome in patients with CSM, OPLL and CDH.128 Of the five studies that

constructed multiple regression equations, all included duration of symptoms as a significant

predictor of JOA.129-132, 134 In contrast, duration of symptoms was not a significant predictor in

the studies by Kato et al (1998) and Shin et al (2010).127, 133

mJOA/JOA

Five studies reported the relationship between postoperative mJOA or JOA and duration

of symptoms. Tanaka et al (1999) identified duration of symptoms as a significant predictor of

postoperative JOA score and included it in a regression model.138 Duration of symptoms was

also a contributor to a model developed by Morio et al (2001), even though it was not

significantly correlated with postoperative JOA score in univariate analysis.129 The remaining

56

three studies reported that duration of symptoms was not a significant predictor of

postoperative mJOA/JOA.136, 139, 140

Nurick

Four studies evaluated the association between Nurick and duration of symptoms.

Rajshekhar and Kumar (2005) reported that patients with a duration of symptoms ≤12 months

were 4.8 times more likely to improve and 14.0 times more likely to be “cured” following

surgery than patients with a duration of symptoms >12 months.142 In addition, according to Suri

et al (2003), patients with a duration of symptoms >2 years were less likely (OR 0.68) to exhibit

improvement on the Nurick.143 Two studies identified no relationship between duration of

symptoms and Nurick outcome.140, 141

3.3.4.3 Baseline Severity Score

JOA Recovery Rate

Fourteen studies examined the relationship between baseline JOA score and JOA

recovery rate. Using multiple logistic regression analysis, Yamazaki et al (2003) and Kim et al

(2008) reported that preoperative JOA was not predictive of an “excellent” recovery rate

(≥50%).122, 124 In contrast, in a study by Naruse et al (2009), patients with a higher preoperative

JOA were more likely to experience a recovery rate ≥50% (OR 1.645, 1.202-2.253, p=0.0019).123

Four studies demonstrated that patients with milder myelopathy experience a higher

recovery rate on the JOA than those with a lower preoperative score.127, 130, 131, 133 The two

regression equations developed by Zhang et al (2010, 2011) included preoperative JOA as a

predictor of JOA recovery rate.

Seven studies reported an insignificant relationship between preoperative JOA and

recovery rate.125, 126, 128, 129, 132, 134, 135 This is likely because a patient’s recovery rate is already

based on his/her preoperative JOA score.

mJOA/JOA

Results were significantly different when the primary outcome was postoperative

JOA/mJOA instead of recovery rate. All studies reported that patients with a higher

57

preoperative score and milder myelopathy have a significantly higher postoperative score and a

better surgical outcome.129, 135, 137-140, 144 In the two studies that constructed regression models,

preoperative JOA was included as an important predictor of postoperative JOA.130, 131

Nurick

Three studies used the Nurick as an outcome measure. In a study by Choi et al (2005),

preoperative Nurick was a significant predictor of a ≥1 point improvement on the Nurick in

univariate (p=0.0268) but not multivariate (p=0.1552) analysis.141 Rajshekhar and Kumar (2005)

examined predictors of Nurick improvement as well as of a “cure,” defined as a postoperative

Nurick score of 0 or 1.142 Based on this study, patients with a preoperative Nurick score of 4

were 8.6 times more likely to be “cured” than those with a score of 5. Preoperative severity

score, however, was not a significant predictor of “improvement” on the Nurick. According to

Furlan et al (2011), patients with a lower and milder Nurick score had a better surgical

outcome.140

3.3.4.4 Other Predictors

The predictive value of other clinical factors was also examined: gender (n=5),123, 125, 137,

140, 141 diagnosis/type of myelopathy (n=3),142, 144, 145 diabetes (n=2),124, 141 Charlson Co-morbidity

Index (n=1),140 number of ICD-9 codes (n=1),140 smoking status (n=1)124 and various neurological

signs (n=2).130, 144

Gender was not significantly associated with surgical outcome across all outcome

measures.120,122,134,137,138

Three studies evaluated whether type of myelopathy was predictive of surgical

outcome: one study compared patients with OPLL to those with CSM,142 another examined

recovery rates in OPLL, CSM and CDH patients145 and a final study evaluated differences in

outcome between patients with CSM and those with soft disc herniation.144 According to

Rajshekar and Kumar (2005), patients with CSM were 5.3 times more likely to exhibit

improvement on the Nurick than those with myelopathy secondary to OPLL (p=0.02). There was

no significant association between disease diagnosis (CSM or OPLL) and a “cure” or score of 0 or

58

1 on the Nurick. In the study by Park et al (2006), the Neurological Cervical Spine Scale (NCSS)

recovery rates were not significantly different across the three diagnosis groups (OPLL, CSM,

CDH). Finally, patients with soft disc herniation had a better postoperative mJOA and change in

mJOA than patients diagnosed with CSM.144

According to Furlan et al (2011), preoperative number of ICD-9 codes was a significant

predictor of postoperative mJOA at 1-year follow-up (p=0.013).140 In multivariate analysis, this

variable was not significantly associated with Nurick or Berg Balance Scale at 1-year. Charlson

Co-morbidity Index was also not a significant predictor of outcome. The predictive value of

diabetes was examined by two studies.124, 141 Choi et al (2005) reported that patients without

diabetes were 14.3 times more likely to experience improvement by ≥1 point on the Nurick

than patients with diabetes. Kim et al (2008) confirmed these findings: patients with diabetes

were 2.92 times more likely to have a <50% recovery rate than healthy patients. The odds ratio

of this association increased to 4.01 if the patient also smoked.

The predictive value of spastic gait, hand wasting and a positive Babinski sign was

examined by single studies. Hand wasting was significantly associated with postoperative mJOA

and change in mJOA; spastic gait was not predictive of either.144 A positive Babinski sign was

significantly correlated with JOA recovery rate and was included in the final regression equation

developed by Zhang et al (2010).130

3.3.5 Results of studies without multivariate analysis

Sixty-two additional studies discussed the predictive value of various clinical factors but

did not conduct a multivariate analysis. Forty-seven studies assessed age as a predictor, 41

reported on duration of symptoms and 34 examined preoperative myelopathy severity. A wide

variety of outcome measures were used in these studies, including the mJOA/JOA, Nurick score,

European Myelopathy Scale, 30-meter walking test. Other studies used unvalidated or

subjective measures to evaluate outcome such as “improvement of symptoms,” neurological

examination and the modified Lees and Turner. To be thorough, we summarized the results of

these studies in this section.

59

Table 3-7. Important Clinical Predictors of Surgical Outcomes: Results of Univariate and Multivariate Analysis

Author (year) Study Design (level of evidence)

Univariate Analysis Multivariate Analysis (Clinical Factors)

Chen et al (2001)

Multivariate regression analysis

Gender: p=0.294 Age: p=0.014 Preoperative JOA: p=0.055

Gender: p=0.489 Preoperative JOA: p=0.061 Age: p=0.037

Chibbaro et al (2006)


NR Age: p=0.47 Duration of symptoms: p=0.29 Preoperative mJOA: p=0.0005

Chiles et al (1999)


Postoperative mJOA Spastic gait: p=0.0039 OPLL diagnosis: p=0.0096 Hand wasting: p=0.1389 Change in mJOA OPLL diagnosis: p=0.0004 Spastic gait: p=0.9476 Hand wasting: p=0.9619

Postoperative mJOA Preoperative JOA: p<0.0001 Hand wasting: p=0.0210 OPLL diagnosis: p=0.0226 Spastic gait: p=0.1902 Change in mJOA Preoperative JOA: p<0.0001 Hand wasting: p=0.0255 OPLL diagnosis: p=0.0366 Spastic gait: p=0.1817

Choi et al (2005)

Multiple logistic regression analysis (≥ 1 change/<1 change)

Duration of symptoms: p=0.1082 Diabetes: p=0.0064 Preoperative Nurick: p=0.0268

Diabetes: OR 14.302 (1.034-197.766), p=0.0471 Duration of symptoms: p=0.1127 Preoperative Nurick: p=0.1552

Fujimura et al (1998)


NR At 1 and 3-years after surgery: Duration of symptoms: p<0.06 At 5-years after surgery: Duration of symptoms: p<0.05 Age: p<0.05

Furlan et al (2011)


Nurick at 1-year Gender: p=0.269 Age: p=0.002 Duration of symptoms: p=0.50 CCI: p=0.123 Preoperative number of ICD-9 codes: p=0.0009 mJOA at 1-year Gender: p=0.331 Age: p=0.0003

Nurick at 1-year Preoperative Nurick: p=0.004 Age: p=0.015 mJOA at 1-year Preoperative JOA: p=0.007 Age: p=0.01 Number of ICD-9 codes: p=0.013 BBS at 1-year Preoperative BBS: p=0.0001

60

Duration of symptoms: p=0.459 CCI: p=0.102 Preoperative number of ICD-9 codes: p=0.0004 BBS at 1-year Gender: p=0.168 Age: p<0.0001 Duration of symptoms: p=0.991 CCI: p=0.198 Preoperative number of ICD-9 codes: p=0.01

Age: p=0.002 Gender, duration of symptoms or CCI were not significantly related to outcome.

Iwasaki et al (2007)


NR JOA at time of maximum recovery: Preoperative JOA: p=0.0003 Age: p=0.1866

Iwasaki et al (2002)


NR Preoperative JOA: p=0.0001 Age: p=0.064 Gender: p>0.05

Kato et al (1998)


NR Short-term (1-year): Preoperative JOA: p=0.004 Age: p=0.005 Long-term (5-years): Preoperative JOA: p=0.04 Age: p=0.002

Kim et al (2008)

Multiple logistic regression analysis (≥50% recovery rate/<50% recovery rate)

Age: OR 1.07 (1.09-1.14), p=0.04 Duration of symptoms: p=0.09 Preoperative JOA: p=0.08 Diabetes: OR 2.86 (1.29-5.48), p=0.03 Smoking: p=0.12

Diabetes: OR 2.92 (1.32-6.12), p=0.01 Diabetes+age: OR 2.21 (1.15-4.23), p=0.04 Diabetes+smoking: OR 4.01 (1.89-8.32), p=0.02

Koyanagi et al (1993)


CSM Age: p>0.05 Duration of symptoms: p<0.01 Preoperative JOA: p>0.05 OPLL Age: p<0.05 Duration of symptoms: p<0.01 Preoperative JOA: p>0.05 CDH Age: p<0.05 Duration of symptoms: p>0.05 Preoperative JOA: p>0.05

Duration of symptoms was included in the final regression equation for CSM and OPLL.

61

Morio et al (2001)


Recovery rate Preoperative JOA: p=0.9289 Duration of symptoms: p=0.050 Age: p=0.0031 Postoperative JOA score Preoperative JOA: p<0.0001 Duration of symptoms: p=0.5565 Age: p<0.0001

Recovery rate Duration of symptoms and age were included in the final regression equation Postoperative JOA score Duration of symptoms, preoperative JOA score and age were included in the final regression equation.

Naruse et al (2009)

Multiple logistic regression analysis (≥50% recovery rate/<50% recovery rate)

At 1-year Preoperative JOA score: OR 1.369 (1.080-1.734), p=0.0093 Gender: p=0.3076 Age: p=0.7619

Preoperative JOA score: OR 1.645 (1.202-2.252), p=0.0019

Okada et al (1993)


RR of patients with CDH was significantly higher than the RR of patients with CSM or OPLL (p<0.01) Duration of symptoms: r=-0.463 for OPLL, r=-0.401 for CSM Age: NS Preoperative JOA: NS

Duration of symptoms was included in the final regression equation.

Park et al (2006)


Duration of symptoms: p=0.0039 Preoperative NCSS: p<0.0001 Age: p=0.4279 Diagnosis (OPLL CSM, CDH): p=0.2002

Duration of symptoms: p<0.01 Preoperative NCSS: p>0.05 Age: p>0.05

Rajshekhar and Kumar (2005)

Multiple logistic regression (Nurick improvement ≥1/ <1; cure (Nurick=0 or 1)/ no-cure)

Improvement Age: p=1.00 Duration of symptoms: p=0.003 Preoperative Nurick: p=0.01 OPLL Diagnosis: p=0.005 Cure Age: p=0.40 Duration of symptoms: p=0.007 Preoperative Nurick: p=0.06 OPLL Diagnosis: p=1.00

Improvement Duration of symptoms (ref>12): OR 4.8 (1.4-16.4), p=0.01 Diagnosis (ref=OPLL): OR 5.3 (1.3-22.2), p=0.02 Cure Duration of symptoms (ref>12): OR 14.0 (1.7-115.9), p=0.01 Preoperative Nurick (ref=5): OR 8.6 (1.0-73.0), p=0.05

Shin et al (2010)


NR Age: p=0.0604 Duration of symptoms: p=0.2969 Preoperative JOA: p<0.0001

Suri et al (2003)

Multiple logistic regression analysis (improvement in

Motor Outcome: Age (ref=40-60): <40, OR 2.5 (0.64-7.3)

Motor Outcome: Age (ref=40-60): <40, OR 2.9 (0.7-4.2), p<0.05

62

(m)JOA: (modified) Japanese Orthopaedic Association; OPLL: ossification of the posterior longitudinal ligament; ICD: international classification of disease; CCI: Charlston

co-morbidity index; BBS: berg balance scale; OR: odds ratio; CDH: cervical disc herniation; NCSS: neurosurgical cervical spine scale; NS: not significant

symptoms/no improvement)

Duration of symptoms (ref>2 years): <1 year, OR 3.9 (1.1-10.4); 1-2 years, OR 3.11 (0.46-2.66) Disability Outcome: Age (ref=40-60): <40, OR 3.1 (0.71-9.1) Duration of symptoms (ref <1 year): 2 years, OR 0.68 (0.30-1.54)

Duration of symptoms (ref >2 years): <1 year, OR 5.9 (0.82-12.5), p<0.05; 1-2 years, OR 2.89 (1.2-9.5), p<0.05 Disability Outcome: Age (ref=40-60): <40, OR 2.17 (0.61-9.1), p<0.001

Tanaka et al (1999)


NR Postoperative motor function score of the lower extremity: Preoperative motor function score of the lower extremity and duration of lower limb disability were included in the final regression equation. Postoperative JOA score: Preoperative JOA and duration of symptoms were included in the final regression equation.

Uchida et al (2005)


NR CSM/OPLL: Preoperative JOA: p<0.05

Wada et al (1999)

Multiple regression analysis

Duration of symptoms: r=-0.364 Age: poor correlation Preoperative JOA: poor correlation

Duration of symptoms was included in the final regression equation.

Yamazaki et al (2003)

Multiple logistic regression analysis (recovery rate ≥50%/<50%)

Younger patients (<64 years) Age: p=0.334 Duration of symptoms: p=0.291 Preoperative JOA: p=0.780 Older patients (>65 years) Age: p=0.466 Duration of symptoms: p=0.004 Preoperative JOA: p=0.070

Older patients (>65 years) Duration of symptoms: OR 0.886 (0.817-0.961), p=0.004

Zhang et al (2011)


NR Age, duration of symptoms and preoperative JOA were included in the final regression equation.

Zhang et al (2010)


NR Age, duration of symptoms, preoperative JOA and Babinski sign were included in the final regression equation.

63

3.3.5.1 Age

One study reported a positive,146 13 a negative147-159 and 30 an insignificant association

between age and surgical outcome.9, 160-187 Table 3-8 summarizes the results from these studies.

Table 3-8. The Association between Age and Surgical Outcome: Results from Studies without

Multivariate Analysis

Association Articles Outcome Measures

Negative Arnold et al. (1993), Bertalanffy and Eggert (1988), Chagas et al. (2005), Cheng et al. (2009), Heidecke et al. (2000), Holly et al. (2008), Lyu et al. (2004), Masaki et al. (2007), Matsuda et al. (1999), Naderi et al. (1998), Nagata et al. (1996), Satomi et al. (2001), Sinha and Jagetia (2011)

Nurick: Chagas mJOA/JOA: Cheng, Holly, Lyu, Masaki, Matsuda, Naderi, Nagata, Satomi Improvement of symptoms: Arnold, Bertalanffy and Eggert European Myelopathy Scale: Heidecke Roosen and Grote: Bertalanffy and Eggert NCSS: Sinha and Jagetia

Positive Singh et al. (2001) Walking test: Singh

None Ahn et al. (2010), Arnasson et al. (1987), Bishara et al. (1971), Chen et al. (2009), Chung et al. (2002), Ebersold et al. (1995), Fessler et al. (1998), Gok et al. (2009), Gregorius et al. (1976), Hamanishi et al. (1996), Hamburger et al. (1997), Handa et al. (2002), Hasegawa et al. (2002), Houten and Cooper (2003), Huang et al. (2003), Hukuda et al. (1985), Kawaguchi et al. (2003), Koc et al. (2004), Lee et al. (1997), Lu et al. (2008), Mastronardi et al. (2007), Matsuoka et al. (2001), Naderi et al. (1996), Nagashima et al. (2011), Ryu et al. (2010), Saunders et al. (1991), Singh et al. (2009), Suzuki et al. (2009), Wiberg et al. (1986), Wohlert et al. (1984)

Nurick: Ebersold, Fessler, Gok, Huang, Lee, Mastronardi, Saunders mJOA/JOA: Ahn, Chen, Chung, Hamanishi, Hamburger, Handa, Hasegawa, Houten and Cooper, Hukuda, Kawaguchi, Koc, Lu, Mastronardi, Matsuoka, Naderi, Nagashima, Ryu, Suzuki Cooper: Houten and Cooper Walking test: Singh Gait Improvement: Lee Modified Lees and Turner: Gregorius Questionnaire/Exam: Arnasson, Wiberg Improvement of symptoms: Bishara, Wohlert

Conditional* Guidetti and Fortuna (1969)188: Significant negative predictor when evaluated alone. When adjusting for disease duration, age was a less important predictor. Nagashima et al. (2006)189: In patients with a preoperative JOA score from 10-12, the recovery rates between ``old`` and ``young`` age groups were similar. In more severe cases, the recovery rates were lower in the older group. Ogawa et al. (2004)190: Negative predictor in severe myelopathy. Insignificant predictor in moderate myelopathy.

JOA: Nagashima, Ogawa Neurological Evaluation: Guidetti and Fortuna

*Indicates a significant relationship only under certain conditions. NCSS: neurosurgical cervical spine scale; (m)JOA: (modified) Japanese Orthopaedic Association

64

3.3.5.2 Duration of Symptoms

Twenty-seven studies reported a negative9, 147-150, 154, 155, 157, 158, 162, 165, 167, 173, 178, 179, 182,

183, 185, 187, 188, 191-197 and 11156, 161, 163, 166, 171, 172, 175, 176, 186, 198, 199 an insignificant association

between duration of symptoms and surgical outcome. Table 3-9 illustrates the results from

these studies.

Table 3-9. The Predictive Value of Duration of Symptoms: Results from Studies without

Multivariate Analysis


Negative Agrawal et al. (2004), Arnold et al. (1993), Bertalanffy and Eggert (1988), Bishara (1971), Chagas et al. (2005), Ebersold et al. (1995), Gok et al. (2009), Guidetti and Fortuna (1969), Hamanishi et al. (1996), Heidecke et al. (2000), Hukuda et al. (1985), Iencean et al. (2007), Kim et al. (2007), Masaki et al. (2007), Mastronardi et al. (2007), Matsuda et al. (1999), Matsuoka et al. (2001), Moussa et al. (1983), Nagata et al. (1996), Phillips et al. (1973), Ryu et al. (2010), Satomi et al. (2001), Saunders et al. (1991), Scardino et al. (2010), Sinha and Jagetia (2011), Suzuki et al. (2009), Wohlert et al. (1984)

Nurick: Agrawal, Chagas, Ebersold, Gok, Lee, Mastronardi, Phillips, Saunders, Scardino mJOA/JOA: Hamanishi, Hukuda, Iencean, Masaki, Mastronardi, Matsuda, Matsuoka, Naderi, Nagata, Ryu, Satomi, Suzuki EMS: Heidecke Herkowitz: Kim Modified Lees and Turner: Moussa Roosen and Grote: Bertalanffy and Eggert NCSS: Sinha and Jagetia Neurological Evaluation: Guidetti and Fortuna Improvement of symptoms: Arnold, Bertalanffy and Eggert, Bishara, Wohlert

None Arnasson et al. (1987), Chen et al. (2009), Emery et al. (1998), Gregorius et al. (1976), Houten and Cooper (2003), Huang et al. (2003), Koc et al. (2004), Kumar et al. (1999), Lee et al. (1997), Naderi et al. (1998), Wiberg et al. (1986)

Nurick: Emery, Huang mJOA/JOA: Chen, Houten and Cooper, Koc Cooper: Houten and Cooper SF-36/Harsh: Kumar Questionnaire/Exam: Wiberg Neurological Evaluation: Arnasson Modified Lees and Turner: Gregorius Gait Improvement: Lee

Conditional* Chung et al. (2002)200: Negatively correlated with outcome if baseline JOA is <9. Handa et al. (2002)169: Negative relationship with outcome in the older but not the younger group. Ogawa et al. (2004)190: An important predictor in moderate but not severe myelopathy.

JOA: Chung, Handa, Ogawa

*Indicates a significant relationship only under certain conditions. NCSS: neurosurgical cervical spine scale; (m)JOA: (modified) Japanese Orthopaedic Association; SF-36: short form-36

65

3.3.5.3 Baseline Severity Score

Fourteen studies reported a positive,162, 168, 173, 175, 179, 192, 193, 197-203 four a negative146, 165,

172, 184 and 139, 153-155, 158, 160, 163, 164, 167, 183, 186, 187, 204 an insignificant association between baseline

severity score and surgical outcome. Table 3-10 provides an overview of the results from these

studies.

Table 3-10. The Relationship between Preoperative Myelopathy Severity and Surgical Outcome:

Results from Studies without Multivariate Analysis


Negative Gok et al. (2009), Huang et al. (2003), Singh et al. (2009), Singh et al. (2001),

Nurick: Gok, Huang Walking test: Singh, Singh

Positive Alafifi et al. (2007), Bishara et al. (1971), Chung et al. (2002), Emery et al. (1998), Hamburger et al. (1997), Hukuda et al. (1985), Iencean (2007), Kim et al. (2007), Kiris and Kilincer (2008), Koc et al. (2004), Kumar et al. (1999), Matsuoka et al. (2001), Sinha and Jagetia (2010), Wang et al. (2003).

Nurick: Alafifi, Emery, Wang mJOA/JOA: Chung, Hamburger, Hukuda, Iencean, Kiris and Kilincer, Koc, Matsuoka Herkowtiz: Kim Improvement of symptoms: Bishara SF-36/Harsh: Kumar NCSS: Sinha and Jagetia

None Ahn et al. (2010), Chen et al. (2009), Ebersold et al. (1995), Fessler et al. (1998), Hamanishi et al. (1996), Lyu et al. (2004), Magnaes and Hauge (1980), Masaki et al. (2007), Matsuda et al. (1999), Satomi et al. (2001), Saunders et al. (1991), Wiberg et al. (1986), Wohlert et al. (1984),

Nurick: Fessler, Saunders mJOA/JOA: Ahn, Chen, Hamanishi, Lyu, Masaki, Matsuda, Satomi Questionnaire/Exam: Wiberg Patient`s subjective evaluation: Magnaes and Hauge Improvement of symptoms: Wohlert

Conditional Guidetti and Fortuna (1969)188: Significant positive predictor when evaluated alone. When adjusting for disease duration, baseline severity was a less important predictor. Handa et al. (2002)169: Significant predictor of outcome in the younger but not older group. Ogawa et al (2004)190: Significant predictor of outcome in the severe group but not in the moderate group.

JOA: Handa, Ogawa Neurological Evaluation: Guidetti and Fortuna

*Indicates a significant relationship only under certain conditions. NCSS: neurosurgical cervical spine scale; (m)JOA: (modified) Japanese Orthopaedic Association; SF-36: short form-36

3.3.5.4 Other Predictors

Several other clinical predictors were evaluated. Table 3-11 summarizes the results from

additional studies that examined the predictive performance of other clinical variables.

66

Table 3-11: Other Clinical Predictors of Surgical Outcome: Results from Studies without Multivariate Analysis

N/Hi: Normal T1 image/High T2 signal change, Lo/Hi: Low T1 signal change/High T2 signal change; MRI: magnetic

resonance imaging; ROM: range of motion

Co-Morbidities

Number of co-morbid diseases

Nagata et al. (1996): A greater number of co-morbidities was associated with a poor surgical outcome in the older group.157

Presence of co-morbid disease

Houten and Cooper (1999): Presence of co-morbidities is not related to outcome.171

Diabetes Chen et al. (2009), Kawaguchi et al. (2000): Diabetes is not a significant predictor of outcome.163, 205

Psychological disorders Kumar et al. (1999): Patients in the “poor” outcome group had greater emotional problems than those in the “good” outcome group.206

Signs and Symptoms

Lower extremity dysfunction

Lee et al. (1997): Not a significant predictor of outcome.176 Gregorius et al. (1976): Presence of lower extremity weakness is associated with a worse outcome.166

Upper extremity dysfunction

Lee et al. (1997): Not a significant predictor of outcome.176 Magnaes and Hauge (1980): Presence of arm symptoms is positively associated with leg outcome.204

Bowel/ Bladder Dysfunction

Houten and Cooper (1999), Lee et al. (1997): Not a significant predictor of outcome 171,

176 Gregorius et al. (1976), Sinha and Jagetia (2011): Presence of bladder/bowel dysfunction is associated with a worse outcome.159, 166

Babinski Sign Alafifi et al. (2007): A positive Babinski sign was predictive of a worse outcome in patients with either a N/Hi or Lo/Hi T1/T2-weighted MRI.201

Leg spasticity/Spastic gait Gregorius et al. (1976): Not a significant predictor of outcome.166 Alafifi et al. (2007), Bertalanffy and Eggert (1998): Presence of leg spasticity is associated with a worse outcome.149, 201

Hyperreflexia Hand Atrophy

Alafifi et al. (2007): Both these signs were predictive of a worse outcome in patients with either a N/Hi or Lo/Hi MRI.201

Sexual dysfunction Clonus Gait Impairment

Found to be negative predictors of outcome by single studies.159, 201, 203

Upper extremity atrophy Radicular pain Lower cervical pain Cervical ROM Long tract signs

Found to be insignificant predictors of outcome by single studies.166, 176

Other

Race Race is not a predictor of outcome.166

Gender Emery et al. (1998): Males showed more improvement than females.198 Not a significant predictor of outcome by several studies.163, 165, 166, 187, 200, 207-214

Onset of symptoms Patients with a gradual onset of symptoms have a worse outcome.149

Disease progression Patients with a slower disease progression have a better outcome.194

67

3.3.6 Evidence Summary

Moderate evidence suggests that age is not associated with postoperative JOA score. It

is unknown, however, whether age is predictive of JOA recovery rate or postoperative Nurick.

According to low level evidence, a longer duration of symptoms is predictive of a worse

recovery rate on the JOA. The association between duration of symptoms and postoperative

mJOA/JOA or Nurick are unclear in the literature.

Based on moderate evidence, more severe preoperative myelopathy is predictive of a

lower postoperative mJOA/JOA. Given that the JOA recovery rate is based off of both pre- and

post-operative JOA, there is no association between baseline JOA and JOA recovery rate (low)

evidence).

Gender is not a significant predictor of surgical outcome based on low level evidence.

There is insufficient evidence suggesting that smoking status, hand wasting, spastic gait,

Babinski sign and type of myelopathy are important predictors of surgical outcome.

Based on low to moderate evidence, patients with diabetes are more likely to

experience a worse surgical outcome than patients without diabetes. On the other hand,

Charlson Co-morbidity Index and number of ICD-9 cases are not associated with surgical

outcome (low evidence) (Table 3-12).

3.3.7 Discussion

Our systematic review summarizes important clinical predictors of surgical outcome.

This knowledge will provide decision support to surgeons and allow them to better predict how

their patients are likely to fare following surgery. Furthermore, this information can be used by

clinicians to help manage patients’ expectations, especially those who are less likely to achieve

optimal recovery.

Based on this review, patients with a longer duration of symptoms and more severe

myelopathy preoperatively are likely to have a worse surgical outcome. The rationale behind

this finding is that severe and chronic longstanding compression of the spinal cord may lead to

68

Table 3-12. Evaluation of Overall Body of Evidence using GRADE: Systematic Review A

Baseline quality: HIGH = majority of articles are Level I/II. LOW = majority of articles are Level III/IV.

UPGRADE: Large magnitude of effect (1 or 2 levels); dose response gradient (1 level); Plausible confounding decreases magnitude of effect (1 level)

DOWNGRADE: Inconsistency of results (1 or 2 levels); indirectness of evidence (1 or 2 levels); imprecision of effect estimates (1 or 2 levels); risk of bias (1 or 2 levels);

failure to specify subgroup analysis a priori (1 level); reporting bias (1 level)

Strength of

evidence

Conclusions/Comments Baseline UPGRADE

(levels)

DOWN-GRADE

(levels)

Are there clinical factors that predict post-surgical patient outcome?

Age MODERATE

UNKNOWN

Not associated with JOA: As reported by five retrospective studies, age is not a significant predictor of postoperative JOA.

It is unknown whether age is associated with JOA recovery rate or postoperative Nurick

HIGH Precision unknown

(-1)

Duration of symptoms LOW-

INSUFFICIENT

UNKNOWN

Associated with Lower JOA Recovery Rate: As reported by seven retrospective studies, a longer duration of symptoms is predictive of a worse recovery rate on the JOA

It is unknown whether duration of symptoms is associated with postoperative mJOA/JOA or Nurick.

LOW

N/A

N/A

Precision unknown

(-1)

N/A

Baseline severity score LOW-

INSUFFICIENT

MODERATE

UNKNOWN

Not associated with JOA Recovery Rate: As reported by nine retrospective studies, there is no association between baseline JOA and JOA recovery rate.

Associated with mJOA/JOA: According to five studies (1 prospective, 6 retrospective), more severe preoperative myelopathy is predictive of a lower postoperative mJOA/JOA.

It is unknown whether baseline Nurick score is predictive of postoperative Nurick.

LOW

HIGH

N/A

N/A

Precision unknown

(-1)

Precision unknown

(-1)

N/A

Gender LOW-

INSUFFICIENT

Not associated with outcome: As reported by five studies (1 prospective, 4 retrospective), gender is not a significant predictor of surgical outcome.

LOW Precision unknown

(-1)

Diabetes

LOW-

MODERATE

Associated with a poorer outcome: As reported by two studies (2 retrospective), diabetes is a significant predictor of a worse surgical outcome

HIGH

Large

magnitude of

effect (+1-2)

Imprecision (-1)

69

Charlson co-morbidity index

Number of ICD-9 codes

LOW Not associated with outcome: As reported by a single prospective study, CCI and number of ICD-9 codes are not associated with surgical outcome

HIGH Consistency

unknown (-1), risk

of bias (-1)

Diagnosis INSUFFICIENT There is insufficient evidence (3 retrospective studies) that type of myelopathy (CDH, CSM or OPLL) affects surgical outcome

LOW Inconsistency of

results (-1), precision

unknown (-1)

Hand wasting Spastic gait Babinski sign

INSUFFICIENT

INSUFFICIENT

There is insufficient evidence that hand wasting and spastic gait are predictors of surgical outcome

There is insufficient evidence that a positive Babinski sign is a significant predictor of surgical outcome

LOW

LOW

Inconsistency of

results (-1), precision

unknown (-1)

Smoking status INSUFFICIENT There is insufficient evidence (1 retrospective study) suggesting smoking status predicts surgical outcome

LOW Large

magnitude of

effect (+1)

Consistency

unknown (-1),

Indirect evidence (-1)

Unknown: controversy across studies prevented assessment of strength of evidence. (m)JOA: (modified) Japanese Orthopaedic Association; ICD: international

classification of disease; CCI: Charlston co-morbidity index; CDH: cervical disc herniation; CSM: cervical spondylotic myelopathy: OPLL: ossification of the posterior

longitudinal ligament.

70

irreversible histological damage such as demyelination, cavitation and necrosis of the gray

matter.215 It is therefore essential that primary care physicians detect myelopathy at an early

stage, differentiate between this disease and other common diagnoses such as carpal tunnel

syndrome and refer these patients for early surgical consultation.

There is little consensus in the literature as to whether age is a significant predictor of

surgical outcome. Although most surgeons will not discriminate on the basis of age, they should

be aware that the elderly may not be able to translate neurological improvement to functional

recovery as well as their younger patients. Potential explanations for this discrepancy include 1)

the elderly experience age-related changes in their spinal cord, including a decrease in γ-

motorneurons, number of anterior horn cells and number of myelinated fibers in the

corticospinal tracts and posterior funiculus; 2) since CSM is a progressive disease, older patients

are likely to have more substantial degenerative pathology and may require a more complex

surgery; 3) older patients have reduced physiological reserves and are more likely to have

unassociated co-morbidities that may affect outcome; and 4) the elderly may not be able to

conduct all activities on a certain functional scale due to these co-morbidities (ex. walking time

may be affected by osteoarthritis).124, 157, 170, 216, 217 We recommend that surgeons consider a

patient’s physiological age, evaluate all potential co-morbidities and predict outcome

accordingly.

This review also explored several other predictors of surgical outcome, including gender,

smoking status, co-morbidities, signs and symptoms. Based on low to moderate evidence,

patients with diabetes have reduced odds of achieving a favorable surgical outcome. This is

likely because diabetic individuals exhibit abnormalities in their spinal cord and peripheral

nerves such as infarcts, demyelination, atrophy and softening of the posterior columns.141

Furthermore, diabetics are at an increased risk of perioperative complications that may impact

their recovery following surgery.218 The predictive value of other factors remains unknown and

requires further exploration.

Results from this review significantly differed depending on what scale was used to

evaluate surgical outcome. This may be a result of limitations in the scales rather than an

71

indication of the actual association between predictor and outcome. For example, the Nurick

score is a scale with lower sensitivity; it is a six grade scale that is largely weighted towards

lower limb function and employment.70 Based on our summary of evidence, we were unable to

determine whether age, duration of symptoms or baseline severity score were significantly

associated with postoperative Nurick. On the other hand, various forms of the mJOA were

significantly associated with preoperative myelopathy severity and duration of symptoms. We

suggest that future prediction studies use more sensitive and reliable measures to evaluate the

association between various clinical factors and surgical outcome.

This review also provides insight to researchers as to the most important predictors of

outcome; these should be kept in mind before designing and conducting future prognostic or

therapeutic studies. In addition, when evaluating the predictive value of a particular clinical or

imaging factor, it is important to control for duration of symptoms and baseline severity score.

Furthermore, analyses should also consider age as a potential confounder and adjust

accordingly.

The limitations of this review include 1) we did not separate studies based on length of

follow-up; 2) articles that dichotomized a predictor might have done it differently; and 3) some

of the articles with relevant abstracts or titles were excluded because they were not available

or in another language other than English. The results from this review should encourage

further exploration in this area. Even though many studies have examined important predictors

of surgical outcome in CSM, there still remains a lack of evidence in the form of high quality,

prospective studies using validated outcome measures. A large prospective analysis is required

to reemphasize the predictive value of duration of symptoms and baseline severity score, to

settle the controversy surrounding age and to evaluate the predictive value of smoking status,

signs, symptoms and co-morbidities.

3.3.8 Evidence-Based Clinical Recommendations

Recommendation #1: Duration of symptoms and preoperative myelopathy severity are

significant predictors of surgical outcome as longstanding and chronic compression of the spinal

72

cord can lead to irreversible histological damage. We recommend that these patients are

diagnosed accurately and in a timely fashion and referred early for surgical consultation.

Strength of statement: Moderate

Consensus statement #1: It is unclear whether age is a significant predictor of surgical

outcome. We therefore suggest that surgeons do not discriminate on the basis of chronological

age but instead consider a patient’s physiological age and co-existing co-morbidities.

Consensus Statement #2: We suggest that future prediction studies use more sensitive

outcome measures to evaluate changes in functional status and quality of life.

3.4 Results Part B: Important Imaging Predictors of Surgical Outcome


The electronic search yielded a total of 175 citations. An additional nine citations were

found through a bibliography search. After initial review of abstracts and titles, 137 did not

meet inclusion criteria and were excluded. Of the remaining 47 studies, 31 were excluded due

to small sample sizes or a lack of a multivariate analysis. A total of 17 studies were deemed

relevant following this rigorous review process. (Figure 3-2).

Figure 3-2. Search Strategy and Detailed Review Process for Systematic Review B

73


A total of 17 cohort studies (3 prospective, 15 retrospective) met our inclusion criteria.

Sample sizes ranged from 50 to 197 patients, with mean ages between 44 to 65 years. Average

follow-up varied widely (3 months to 8 years). All studies investigated preoperative SI change

on MRI as a predictor and seven studies evaluated the predictive value of anatomic

characteristics. Various outcome measures were used across the studies, with JOA recovery

rate reported most consistently, followed by the Nurick grade. All studies controlled for age and

the majority also controlled for symptom duration and disease severity (Table 3-13).

This review analyzed the predictive value of both anatomic features and spinal cord

properties. The anatomic characteristics considered were cervical curvature, transverse area at

level of maximum compression, spinal canal diameter, number of compressed segments and

rate of cord flattening (Figure 3-3). Cord properties assessed included high SI on T2WI, low SI on

T1WI, combined T1/T2 signal change, SI ratio, number of SI segments and length on T2WI

(Figure 3-4).

3.4.3 Risk of Bias

We critically appraised 17 studies that conducted a multivariate analysis and controlled

for two of the following three covariates: age, duration of symptoms and baseline severity

score. Of these, four were classified as level II and 13 were rated level III. Most studies were

level III evidence because they were retrospective cohort studies with unreported follow-up

rates or follow-up rates <80%. The three prospective studies did not have a complete follow-up

and were down-graded from level I to level II evidence. In each study, patients were at similar

points in the course of their disease or treatment and were followed long enough for outcomes

to occur. In addition, all analyses accounted for other prognostic factors.

74

Figure 3-3. Summary Figure of Anatomic MRI Characteristics i) green outline is the transverse area of the cord, A is the transverse diameter or width of the

cord and B is the anterioposterior axis or sagittal diameter of the cord; ii) normal cervical

curvature; iii) abnormal cervical curvature where the bodies of vertebrae C3 to C6 cross the line

drawn from the dorsocaudal aspect of C2 to the dorsocaudal aspect of C7; iv) multilevel disc

herniation and spondylosis; v) canal diameter at normal and stenotic levels.

75

Table 3-13. Characteristics of Prognostic Imaging Studies with Multivariate Analysis: Systematic Review B

Author (year) Study design

Sample and Characteristics Non-MRI factors assessed MRI factor assessed Outcome Measures

Chen et al. (2001) Retrospective cohort (III)

CSM (n=64) Male: 65.6% Mean age, yr (range): 56.67 (27-86)

Gender Preoperative JOA Age

SI grade on T2WI Cervical curvature Cord compression ratio

JOA recovery rate

Chibbaro et al. (2006) Retrospective Cohort (II)

CSM (n=70) Male: 67.1% Mean age, yr (range): 57 (29-76) Mean duration of symptoms (range): 13.4 (4-120) months Surgery: anterior cervical corpectomy

Age Duration of symptoms Number of levels decompressed

SI changes on T1WI SI changes on T2WI

mJOA

Kim et al. (2008) Retrospective Cohort (III)

CSM, OPLL (n=87) Male: 57% Mean age, yr (range): 62.3 (42-76) Mean duration of symptoms (range): 10 months (4-36) Surgery: expansive open door laminoplasty

Age Presence of diabetes Presence of diabetes and older age Presence of diabetes and smoking Duration of symptoms Preoperative JOA


JOA recovery rate

Nakashima et al. (2012) Prospective Cohort (II)

CSM (n=87), OPLL (n=14) Male: 60.4% Mean age, yr (±SD): 63.6±11.8 Mean duration of symptoms (±SD): 2.6±3.6 years Surgery: double door laminoplasty

Preoperative step test ≥ 14.5 Age Gender Duration of symptoms Preoperative JOA C2-C7 angle on lateral radiographs C7 plumb line on whole spinal lateral radiographs

SI changes on T2WI JOA recovery rate “Effective” clinical results on JOACMEQ-L

Okada et al. (1993) Retrospective cohort (III)

OPLL (n=23), CSM (n=34), CDH (n=17) Male: 70.3% Mean age, yr (range): 58.9 (35-83) Surgery: anterior (n=20), posterior (n=54)


SI ratio on T2WI (compressed vs. contiguous noncompressed) Transverse area of spinal cord at site of maximal compression Compression ratio of spinal cord

JOA recovery rate

Park et al. (2006) Retrospective Cohort (III)

CSM (n=61), OPLL (n=11), CDH (n=8) Male: 62.5% Mean age, yr (range): 62.1 (36-86) Mean duration of symptoms (± SD): 19.1±21.1 months Surgery: anterior (n=46), posterior (n=34)

Age Duration of symptoms Preoperative severity Surgical method Type of disease

Number of compressed segments on T2WI Number of high intensity segments on T2WI

NCSS recovery rate

76

Setzer et al. (2009) Prospective cohort (II)

CSM (n=60) Male: 66.7% Mean age, yr (range): 61.5 (26-86) Mean duration of symptoms (± SD): 22.0±30.5 months Surgery: anterior discectomy and fusion (n=41), corpectomy (n=19)

Age Duration of symptoms Preoperative mJOA APOE ε4 carrier status

Diameter of the most effected segment of the spinal cord Number of affected segments SI on T2WI

mJOA

Shin et al. (2010) Retrospective cohort (III)

CSM (n=70) Male: 64.3% Mean age, yr (range): 51.1 (26-69) Mean duration of symptoms (range): 9.9 weeks (1-60) Surgery: (anterior discectomy and fusion at one (n=43) or two (n=27) levels

Age Duration of symptoms Cervical curvature Cervical stenosis Preoperative JOA Postoperative JOA

SI grade on T2WI Length (mm) of SI on T2WI Compression ratio of spinal cord at site of maximal compression

JOA recovery rate

Suda et al. (2003) Retrospective cohort (III)

CSM (n=154) Male: 79% Mean age, yr (range): 60 (range 30-81) Surgery: bilateral open-door laminoplasty (n=154)

Age Gender Preoperative JOA Local kyphosis angle Number of enlarged laminae Overall cervical alignment (C2-C7)


JOA recovery rate

Suri et al. (2003) Retrospective cohort (III)

CSM (n=146) Male: 79.5% Mean age, yr (range): 47.1 (17-76) Mean duration of symptoms (range): 11.7 (1.5-120) months Surgery: anterior discectomy, corpectomy, laminectomy, laminoplasty

Age Duration of symptoms Surgical approach

Increased SI on T2WI with a decreased SI on T1WI Number of compressed segments (prolapsed intervertebral discs)

Improvement in motor symptoms Nurick grade

Uchida et al. (2005) Retrospective cohort (III)

CSM (n=77), OPLL (n=58) Male: 62% Mean age, yr (range): 43.8 (27-73) Duration of symptoms: < 1 year to ≥ 3 years Surgery: en bloc C3-7 open door laminoplasty (n=92), Robinson’s anterior fusion (n=15), subtotal spondylectomy at 1-2 vertebrae with interbody fusion (n=28)

Age Preoperative JOA Type of OPLL Type of myelopathy Type of spinal cord evoked potentials Spinal canal narrowing (CT) Postoperative expansion rate of canal Radiological abnormality

SI grade on T2WI Number of compressed segments/ levels Percentage of flattening of the cord

JOA score

Vedantam et al. (2001) Retrospective

CSM, OPLL (n=197) Male: 93.9% Mean age, yr (± SD): 48.8±0.6

Age Duration of symptoms Preoperative Nurick

SI grade on T2WI Increased SI on T2WI with a decreased SI on T1WI

Nurick grade change ≥1

77

Cohort (III) Mean duration of symptoms (range): 8 months (1-180) Surgery: central corpectomy at 1-level (n=99), 2-levels (n=92), and 3-levels (n=6)

Cure: Nurick f/u grade of 0 or 1

Wada et al. (1999) Retrospective cohort (III)

CSM (n=50) Male: 72% Mean age, yr (±SD): 61.0±10.9 (range, 45-81) Mean duration of symptoms (±SD): 9.1±8.5 months (range, 1-36) Surgery: open-door laminoplasty

Age Duration of symptoms Preoperative JOA AP canal diameter at maximal compression on plain radiographs Transverse area of spinal cord at maximal compression on CT myelography Number of blocks on myelogram

Number of high SI segments on T2WI JOA recovery rate

Wang et al. (2010) Retrospective cohort (III)

OPLL (n=58) Male: 71% Mean age, yr (range): 59.6 (47-77) Surgery: expansive open-door laminoplasty

Age Duration of symptoms Preoperative JOA Babinski sign Ankle clonus


JOA recovery rate JOA score

Yamazaki et al. (2003) Retrospective cohort (III)

CSM, OPLL (n=64) Male: 51.6% Mean age, yr (± SD): 64.6±12.0 Mean duration of symptoms (± SD): 25.6±30.6 months Surgery: laminoplasty

Age Duration of symptoms Preoperative JOA Canal diameter on CT myelogram Transverse area on CT myelogram

SI changes on T2WI JOA recovery rate

Zhang et al. (2011) Prospective cohort (II)

CSM (n=52) Male: 57.7% Mean age, yr (range): 56.3 (45-67) Mean duration of symptoms (range): 16.1 months (3–34) Surgery: anterior (n=31), posterior (n=16), anteroposterior (n=5)


SI ratio on T2/T1-WI JOA recovery rate JOA score

Zhang et al. (2010) Retrospective cohort (III)

CSM (n=73) Male: 67.1% Mean age, yr (range) : 53.3 (34–77) Surgery: anterior, posterior, anteroposterior

Age Duration of symptoms Babinski sign


JOA recovery rate JOA score

Level of evidence of each study is given in the first column. CSM: cervical spondylotic myelopathy; (m)JOA: (modified) Japanese Orthopaedic Association; SI: signal intensity;

WI: weighted image; OPLL: ossification of the posterior longitudinal ligament; CDH: cervical disc herniation: JOACMEQ: Japanese Orthopaedic Association Cervical

Myelopathy Evaluation Questionnaire (lower limb); NCSS: neurosurgical cervical spine scale; SD: standard deviation; AP: anteroposterior; CT: computed tomography

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Figure 3-4. Summary Figure of Cord Signal Change Properties i) absence of signal change on T2-WI.;ii) high signal change on T2-WI, small signal intensity ratio; iii) high

signal change on T2-WI, large signal intensity ratio; iv) low signal change on T1-WI; v) from Vedantam et

al. (2011): left: grade 1, predominantly faint and indistinct border; middle: grade 2: predominantly

intense and well-defined border; right: low signal change on T1-WI; vi) multilevel signal change.

3.4.4 Are there anatomic characteristics that can predict outcome?

Table 3-14 summarizes the predictive value of various anatomic MRI characteristics. In a

study by Chen et al. (2001), abnormal curvature was defined as a configuration of the cervical

spine in which any part of the dorsal aspect of the C3 to C6 vertebral bodies crossed the line

drawn to and from the dorsocaudal aspects of C2 and C7 (Figure 3-3, ii).125 There was no

significant difference in JOA recovery rate between patients with abnormal and normal cervical

curvature (R=-0.768, p=0.901).

Okada et al. (1993) evaluated the importance of transverse area in predicting outcome.

132 A larger transverse area was predictive of a higher JOA recovery rate in patients with OPLL

and CSM but not in those with disc herniation. This factor was included in the multiple

regression equations developed to predict outcome in all three forms of cervical myelopathy.

The predictive value of the compression ratio, calculated by dividing the sagittal

diameter by the transverse diameter (x100%), was assessed by three studies (Figure 3-3, i) 132,

79

133, 212. All studies reported a non-significant association between cord compression ratio and

recovery rate.

The diameter of the spinal canal was assessed as a predictive factor by a single

prospective study.219 Multivariate logistic analysis yielded a non-significant association between

diameter and final mJOA.

The greatest number of studies (n=4) evaluated the relationship between number of

compressed segments and JOA recovery rate139, NCSS recovery rate220, mJOA score219, Nurick

grade or improvement of motor, sensory and autonomic symptoms143. In the study by Park et

al. (2006), correlation analysis revealed no significant association between NCSS recovery rate

and number of compressed segments (R=-0.0427, p=0.7259).145 Setzer et al. (2009) observed no

differences in the number of affected segments between an “unchanged,” “improvement” and

“deterioration” group.221 Multivariate analysis, conducted by Uchida et al. (2005), however,

demonstrated that outcome in spondylotic patients undergoing either laminoplasty (n=45)

(p=0.0084) or anterior surgery (n=32) (p=0.0293) was significantly impacted by involvement of

≥3 intervertebral levels. 139 Poor outcome in patients with OPLL, on the other hand, was highly

associated with two-vertebra involvement in anterior surgery (p=0.0388) and laminoplasty

(p=0.0076) and with ≥3 vertebra in laminoplasty (p=0.0029). Finally, Suri et al. (2003) reported a

non-significant association between the number of prolapsed intervertebral discs (PIVD) and

motor, sensory or autonomic outcome.143 In terms of disability outcome, evaluated by Nurick

grade, however, patients with single (p<0.001, OR: 2.91, 95%CI: 0.7-10.4) or two-level (p<0.001,

OR: 2.61, 95%CI 0.4-8.9)) involvement had better outcome than patients with multilevel PIVD.

The predictive value of cord flattening rate was examined by a single study.139 This rate

is calculated by dividing the anteroposterior axis of the spinal cord by the spinal cord width

(x100%). A rate of flattening <50% was significantly associated with a worse JOA recovery rate

following anterior surgery and laminoplasty in patients with spondylosis (p=0.0381, p=0.0116).

In patients with OPLL, a flattening rate ≥50% was predictive of a better outcome in both surgical

groups (p=0.0457, p=0.0298).

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Table 3-14. Association of Anatomic MRI Characteristics with Surgical Outcomes

MRI Characteristic Outcome Author (year) Controlled for Associated with Outcome? Age Duration Severity

Abnormal cervical curvature†

JOA recovery rate

Chen (2001) Yes No Yes No

Larger transverse area of cord at level of maximum compression

JOA recovery rate

Okada (1993) Yes Yes Yes Positively

Larger compression ratio at level of maximum compression§

JOA recovery rate

Chen (2001) Okada (1993) Shin (2010)

Yes No Yes

No No Yes

Yes No Yes

No No No

Diameter of the spinal canal

mJOA score Setzer (2009) Yes Yes Yes No

Greater number of compressed segments**

NCSS recovery rate

Park (2006) Yes Yes Yes No

JOA recovery rate

Uchida (2005) Yes Yes Yes Negatively

mJOA score Setzer (2009) Yes Yes Yes No

Nurick grade Suri (2003) Yes Yes No Negatively

Improvement in motor symptoms††

Suri (2003) Yes Yes No No

Higher rate of flattening of cord (less AP compression)‡‡

JOA recovery rate

Uchida (2005) Yes Yes Yes Positively

(m)JOA: (modified) Japanese Orthopedic Association; NR: not reported †Measured with Batzdorf and Batzdorff method. Abnormal cervical curvature is a configuration of the cervical spine in which any part of the dorsal aspect of any of the vertebral bodies C3 through C6 crosses the C2 through C7 line. §Measured using the following equation: (sagittal diameter)/(transverse diameter) X 100 (%). **Defined as the number of segments where the spinal cord was deformed with disappearance of the surrounding subarachnoid space in one study (Park 2006); the number of prolapsed intervertebral discs (PIVDs) in a second study (Suri 2003); and not reported in the other two (Uchida 2005, Setzer 2009). ††To include weakness, spasticity, wasting, flexor spasms, and fasciculations. ‡‡Measured using the following equation: (anteroposterior axis of the spinal cord)/(spinal cord width) X 100 (%).

3.4.5 Are there cord properties that can predict outcome?

There were more studies that analyzed important MRI SI characteristics than anatomic

properties (Table 3-15). Six studies considered the prognostic value of high SI changes on T2WI.

134, 136, 143, 219, 222, 223 Chibbaro et al. (2006) and Setzer et al. (2009) evaluated the relationship

between presence of a hyperintense signal on T2WI and postoperative mJOA score.136, 219

Setzer et al. (2009) found no significant differences in T2WI SI between the “unchanged,”

“improvement,” and “deterioration” groups. Chibbaro et al. (2006), on the other hand,

concluded that a high SI on a T2WI was significantly correlated with a higher postoperative

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mJOA (p<0.01). Three studies examined the association between T2WI SI changes and JOA

recovery rate.134, 222, 223 In a study by Yamazaki et al. (2003), T2WI SI changes were not

significantly different between the “excellent” or “fair” recovery groups in the younger

(p=0.848) or elderly population (p=0.051). Both Wada et al. (1999) and Nakashima et al. (2012)

confirmed this non-significant relationship between T2WI SI and outcome using multivariate

analysis.134, 223 Nakashima et al. (2012) also considered the effect of T2WI SI change on a second

outcome measure, JOACMEQ-l. Neither univariate (OR: 0.5, CI: 0.19-1.33, p=0.164) or

multivariate (OR: 0.39, CI: 0.13-1.18, p=0.98) analysis yielded a significant association. Finally, in

a study by Suri et al. (2003), there was no significant difference in motor (OR: 1.26, 0.82-9.81,

p=NS), sensory, autonomic or disability outcome (OR: 0.79, CI: 1.79-7.61, p=NS) in patients with

T2WI SI change and those without.143

The predictive value of high SI grade on T2WI was also examined by four studies. 133, 139,

212, 224 A grade of 0 was defined as no intramedullary high SI, grade 1 as a signal change with a

predominantly faint and indistinct border and grade 2 as a signal change with a predominantly

intense and well-defined border. Uchida et al. (2005) also included a fourth grade in their

definition: cystic formation. In a study by Chen et al. (2001), patients with grade 2 had the

worst surgical outcome (p<0.001, R=-33.302) and no significant difference in prognosis was

detected between grades 0 and 1. The study by Shin et al. (2010) reported a statistically

significant difference in recovery rate among these three signal change groups (p=0.002).

Vedantam et al. (2001) determined that the presence of grade 2 signal change was associated

with a decreased probability of a cure (Nurick grade of 0 or 1, OR: 0.48, CI: 0.2-0.9, p=0.04).

Grade 1 signal, on the other hand, was not predictive of either improvement in Nurick (OR: 0.7,

CI: 0.3-1.5, p=0.41) or a cure (OR: 1.4, CI: 0.7-2.7, p=0.23). Uchida et al. (2005) did not find any

relationship between SI grade and postoperative JOA score in patients with spondylosis or

OPLL.

Low SI change on a T1WI was evaluated as a predictive factor by Chibbaro et al. (2006).

136 This study reported that patients with a low SI change on a T1WI had a lower postoperative

mJOA (p<0.05).

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Four studies further examined the predictive value of SI characteristics by exploring

combined T1/T2 signal change.143, 224-226 Kim et al. (2008) identified this predictor as an

important risk factor for poor outcome on the JOA using both univariate (OR: 3.02, CI: 1.56-

5.32) and multivariate (OR: 2.53, CI: 1.67-5.95) logistic regression analysis.225 Suda et al. (2003)

also identified combined T1/T2 signal change as a risk factor for poor outcome, defined as a

JOA recovery rate <50% (univariate: OR: 3.25, CI: 1.34-7.91, p<0.01; multivariate: OR: 4.10, CI:

1.51-11.12, p<0.01).226 Suri et al. (2003) reported that patients without SI changes had a

significantly better motor improvement than patients with a combined T1/T2 SI (OR: 5.1, CI:

1.87-25.1, p<0.001). This was not the case when assessing disability outcome using

improvements in Nurick grade. When examining predictors of a Nurick grade of 0 or 1,

however, patients with T1/T2 SI had a lower probability of achieving this “cure.”224

Okada et al. (1993), Wang et al. (2010) and Zhang et al. (2010, 2011) explored the

predictive value of signal change ratio on surgical outcome.132, 227-229 Okada et al. (1993)

developed a SI ratio by dividing the SI at the site of maximal compression on a T2WI by readings

at adjacent noncompressed sites.132 JOA recovery rate was highly correlated with SI ratio in the

OPLL (r=0.53) and CSM (r=0.426) groups and was included in the final multiple regression

equation for all three degenerative diseases. Wang et al. (2010) and Zhang et al. (2010), on the

other hand, compared T2WI SI at sites of compression with SI at the C7-T1 levels. In both

studies, patients were divided into groups according to their SI ratio: group 1, low SI ratio;

group 2, middle SI ratio; and group 3, high SI ratio.228 Recovery rates in group 1, group 2 and

group 3 were significantly different (p<0.0001).

Table 3-15. Association of MRI Signal Intensity Characteristics with Surgical Outcomes

MRI Characteristic Outcome Author (year) Controlled for Associated with Outcome? Age Duration Severity

High SI grade on T2WI†

JOA recovery rate

Chen (2001) Shin (2010)

Yes Yes

No Yes

No Yes

Negatively Negatively

JOA score Uchida (2005) Yes No Yes No

Nurick grade change ≥ 1

Vendantam (2001)

Yes Yes Yes No

Nurick grade 0 or 1

Vendantam (2001)

Yes Yes Yes Negatively

High SI changes on T2WI

mJOA score Chibbaro (2006) Setzer (2009)

Yes Yes

Yes Yes

No Yes

Positively No

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JOA recovery rate

Nakashima (2012) Yamazaki (2003) Wada (1999)

Yes Yes Yes

Yes Yes Yes

Yes Yes Yes

No No No

“Effective” clinical results on JOACMEQ-L§

Nakashima (2012) Yes Yes Yes No

Improvement in motor symptoms**

Suri (2003) Yes Yes No No

Nurick grade Suri (2003) Yes Yes No No

Low SI changes on T1WI

mJOA score Chibbaro (2006) Yes Yes No Negatively

High SI changes on T2WI + low SI changes on T1WI

JOA recovery rate

Kim (2008) Suda (2003)

Yes Yes

Yes No

Yes Yes


Improvement in motor symptoms**

Suri (2003) Yes Yes No Negatively

Nurick grade Suri (2003) Yes Yes No No

Nurick grade 0 or 1

Vendantam (2001)

Yes Yes Yes Negatively

High SI ratio on T2WI (compressed vs. contiguous noncompressed levels)

JOA recovery rate

Okada (1993) Yes Yes Yes Positively

High SI ratio on T2WI (compressed vs. noncompressed C7-T1 levels)

JOA recovery rate

Wang (2010) Zhang (2010)

Yes Yes

Yes Yes

Yes No


High SI ratio on T2/T1WI††

JOA recovery rate

Zhang (2011) Yes Yes No Negatively

Greater number of high SI segments on T2WI

NCSS recovery rate

Park (2006) Yes Yes Yes Negatively

JOA recovery rate

Wada (1999) Yes Yes Yes Negatively

Longer length of signal intensity on T2WI (mm)

JOA recovery rate

Shin (2010) Yes Yes Yes No

(m)JOA: (modified) Japanese Orthopedic Association; JOACMEQ-L: JOA Cervical Myelopathy Evaluation Questionnaire

(lower limb); NCSS: neurosurgical cervical spine scale; NR: not reported; SI: signal intensity; WI: weighted image.

†Grade 0 = no intramedullary high SI on T2-weighted MRI; Grade 1 = predominantly faint and indistinct border; Grade

2 = predominantly intense and well-defined border. For Uchida 2005 only, a fourth grade was considered: cystic

formation.

§Either condition met: 1) postoperative score higher than the preoperative score by ≥ 20 points or, 2) preoperative

score < 90, and postoperative score ≥ 90 points).

**To include weakness, spasticity, wasting, flexor spasms, and fasciculations.

††Ratio of the signal intensity on T2-weighted to T1-weighted MRI (T2:T1 ratio) at the same spinal cord level and of

similar area.

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SI ratio was identified as an important predictor of outcome and was included in the

final regression equations in both studies. Finally, Zhang et al. (2011) analyzed the prognostic

value of SI ratio by comparing the SI on T2WI to the SI on T1WI at the same spinal cord level

and over a similar area.229 Patients were split into two groups based on the median T2:T1 ratio:

group 1 (1.18-1.74, n=18) and group 2 (1.79-2.77, n=18). There was a significant difference

between these two groups with respect to the recovery rate (32.6±14.4 versus 21.9±8.3,

p<0.001) and postoperative JOA score (12.3±1.6 versus 10.8±1.5, p<0.001). T2:T1 ratio was also

included in the final regression equation.

The length of the SI on a T2WI was assessed by a single study.133 This variable was not

identified as a significant predictive factor (p=0.0961) of JOA recovery rate.

Park et al. (2006) reported that number of high intensity segments was negatively

correlated with recovery rate (R=-0.289, p=0.0063) and included it in the multivariate

regression model.145 Wada et al. (1999) confirmed this finding by observing that patients in a

“focal” disease group had a better recovery rate and postoperative JOA score (56.1±22.2%,

14.3±1.0) compared to patients in a “multi-segmental” group (38.8±14.7%, 12.6±18)

(p<0.01).134

3.4.6 Evidence Summary

There is insufficient evidence that MRI anatomic characteristics, including cervical

curvature, transverse area, number of compressed segments and flattening rate can predict

surgical outcome. Compression ratio and diameter of the canal were not associated with

surgical outcome (low evidence). Low evidence suggests that high SI on T2WI is not an

important predictive factor. A combined T1/T2 signal change, SI ratio and a greater number of

SI segments on T2WI were negatively associated with outcome. The strength of evidence for

these findings was low. Conclusions could not be drawn on the predictive value of SI grade on

T2WI, low SI changes on T1WI and length of SI on T2WI (Table 3-16).

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Table 3-16. Evaluation of Overall Body of Evidence using GRADE: Systematic Review B


UPGRADE: Large magnitude of effect (1 or 2 levels); dose response gradient (1 level); plausible confounding decreases magnitude of effect (1 level)

DOWNGRADE: Inconsistency of results (1 or 2 levels); indirectness of evidence (1 or 2 levels); imprecision of effect estimates (1 or 2 levels); risk of bias (1 or 2 levels);

failure to specify subgroup analysis a priori (1 level); reporting bias (1 level)

Strength of

evidence

Conclusions/Comments Baseline UPGRADE

(levels)

DOWN-GRADE

(levels)

Are there characteristics of the MRI that predict post-surgical patient outcome?

MRI signal intensity characteristics

High SI changes on T2WI + low SI changes on T1WI

High SI ratio on T2WI (compressed vs. non-compressed C7-T1)

High SI ratio on T2/T1WI Greater number of high SI

segments on T2WI

LOW Associated with poorer outcomes: As reported by eight studies, high SI changes on T2WI combined with low SI changes on T1WI (4 retrospective cohorts), high SI ratio (compressed vs. non-compressed C7-T1) on T2WI (2 retrospective cohorts), high SI ratio on T2/T1WI (1 prospective cohort), and a greater number of high SI segments on T2WI (2 retrospective cohorts) were all associated with poorer neurological outcomes following surgery.

LOW

High SI changes on T2WI LOW No association: Five (2 prospective, 3 retrospective) out of six studies found no association between high SI changes on T2WI and various neurological outcomes.

LOW

High SI grade on T2WI Low SI changes on T1WI High SI ratio on T2WI

(compressed vs. contiguous non-compressed levels)

Longer length of signal intensity on T2WI (mm)

INSUFFICIENT There is insufficient evidence in the short- and long-term that high SI grade on T2WI (3 retrospective cohorts), low SI changes on T1WI (1 retrospective cohort), high SI ratio (compressed vs. contiguous non-compressed levels) on T2WI (1 retrospective cohort), or longer signal intensity length on T2WI (1 retrospective cohort) predict postoperative neurological outcomes.

LOW Imprecise (-1)

and/or

Consistency (-1)

86

Anatomic MRI characteristics

Larger compression ratio at level of maximum cord compression

Diameter of the spinal canal

LOW No association: Four studies found no association between a larger compression ratio at the level of maximum cord compression (3 retrospective studies) or the diameter of the spinal canal (1 prospective cohort) and neurological outcome following surgery.

LOW

Abnormal cervical curvature

Larger transverse area of cord at level of maximum compression

Greater number of compressed segments

Higher rate of flattening of cord (less AP compression)

INSUFFICIENT There is insufficient evidence in the short- and long-term that abnormal cervical curvature (1 retrospective cohort), larger transverse area of the spinal cord at the level of maximum compression (1 retrospective cohort), a greater number of compressed segments (1 prospective, 3 retrospective), or a higher rate of flattening of the cord (1 retrospective cohort) predict postoperative neurological outcome.

LOW Imprecise (-1)

and/or

Consistency (-1)

WI: weighted image; SI: signal intensity; AP: anteroposterior

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3.4.7 Discussion

Magnetic resonance imaging is a non-invasive technique routinely used to confirm the

diagnosis of CSM, evaluate cord compression due to canal stenosis and identify any

intramedullary signal changes.33, 34 It is also a valuable tool to exclude differential diagnoses as

it can visualize parenchymal abnormalities including neoplasms, demyelinating plaques and

syringomyelia.32 It should be noted that patients with metallic foreign body in their eye,

aneurysm clips, embedded wires, stimulators or batteries, nitroglycerin patches, pacemakers or

severe claustrophobia cannot be examined by MRI. CT myelography is the preferred alternative

diagnostic modality for patients with contraindications to MRI.230

The objective of this systematic review was to determine the predictive value of MRI.

There is controversy in the literature surrounding the ability of MRI to predict surgical outcome

and whether anatomical dimensions or cord properties are more significant.

A high SI on T2WI reflects a broad spectrum of compressive pathologies and a wide

range of recuperative potentials.143, 224, 227, 229, 231 T2WI SI is non-specific and may indicate either

reversible damage, including edema and ischemia or irreversible changes similar to T1WI SI,

such as necrosis, myelomalacia and cavitation.134, 143, 220, 224, 227, 231, 232 If the SI reflects more

minor pathological changes that will likely diminish post-surgery, then it is not an important

prognostic factor. Chen et al. (2001) and Vedantam et al. (2011) recognized the need to

separate T2 signal changes into grades: type 1 or “faint, fuzzy, indistinct borders” is more

representative of reversible changes whereas type 2 or “intense, well-defined borders” signifies

irreversible histological damage.212, 224 These definitions were based on a histopathologic spinal

cord study that reported that severe changes (microcavitation, spongiform changes and

necrosis) have a higher water content, resulting in more intense borders. Milder histological

damage, such as edema, demyelination and wallerian degeneration, on the other hand,

produce fainter borders. Both studies demonstrated improvements in SI in type 1 following

surgery, confirming that milder signal changes are reversible. Grading of the T2WI SI allows for

an assessment of the amount of irreversible damage. Unfortunately, there is no standard

method of classifying these changes or an approach to quantify the degree of signal change.

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Based on this review, patients with a greater number of high SI segments on T2WI had a

poor prognosis. The recovery rate of patients with focal SI did not differ from those without

SI.229, 231 Patients with multisegmental involvement, however, had a recovery rate similar to

patients with a T1-signal change and a worse recovery rate than the focal group. Wada et al.

(1999) also noted a correlation between multisegmental areas on T2WI and low SI on T1WI.134

Histologically, a greater number of signal change segments reflects more severe and

irreversible damage than a focal signal change.134, 143

Although less prevalent, combined T1/T2 signal change was an important predictor of

outcome. This type of SI is indicative of severe histological damage including cystic necrosis,

secondary syrinx and cavitation.134, 143, 220, 224, 229 Signal intensity at the site of compression as

well as at unaffected levels, however, differs from patient to patient. Three studies addressed

this issue by creating a ratio comparing SI at compressed and non-compressed sites or by

comparing SI on T2WI and T1WI.227-229 Signal intensity ratio was found to be an important

predictor.

It is clear from this review that there is a lack of evidence in the form of high-quality

prospective studies using validated outcome measures. Other studies have neglected to control

for important confounders when assessing the relationship between predictor and outcome. It

is also important to note that the definitions of CSM varied from study to study: 8 studies

enrolled only patients with CSM; 5 studies included patients with OPLL or CSM; 2 studies

specified cervical myelopathy as the diagnosis and did not provide further explanation; 1 study

explored only patients with OPLL; and 1 study separately examined patients with OPLL, CSM

and disc herniation. In addition, approximately 40% of the studies were conducted in Japan,

where the prevalence of OPLL is higher than in Western populations. Some of these studies

may be considered flawed due to a heterogeneous patient population.

Unfortunately, the scaling of signal intensity changes has not been universally quantified

or agreed upon. From the studies that have assessed high SI on T2WI, it is evident that the type

of methodology impacted the ability to establish a relationship to outcome. Though T2WI SI

changes may not relate to prognosis when measured simply for presence/absence, or

89

subjectively as has been done by previous authors,139, 212 more modern and objective

approaches which have stratified signal intensity into grades or ratios have been effective in

predicting outcome. Accordingly, further research, in the form of prospective studies with

larger patient groups are necessary to conclusively determine the role of T2WI SI changes. No

further systematic reviews should be conducted until a reliable and valid method to quantify SI

changes is devised.

A major issue is that most of the tools used to evaluate outcome in these studies are too

crude and may not be able to detect all functional improvements, especially in milder patients.

Results often differ depending on the outcome measure; this was the case in our first

systematic review on important clinical factors.215 Traditional measures such as the mJOA and

Nurick score should be used in combination with more sensitive and specific measures,

including the walking test, grip strength and the Berg Balance Scale. The use of a wider range of

functional and impairment tests may help better define the prognostic value of certain MRI

characteristics.

Diffusion Tensor Imaging (DTI) is a relatively new neuroimaging technique based on

magnetic resonance. It assesses the structural integrity of white matter tracts by evaluating

diffusion rates of extracellular water molecules through tissue.233-236 DTI measures two key

parameters: the apparent diffusion coefficient (ADC) and the fractional anisotropy (FA).236

Several studies reported significant differences in these two measurements between a control

and myelopathic group: the FA at the level of compression was significantly lower and the ADC

was significantly higher in the patient group.233-236 This form of imaging appears to be more

sensitive and specific than the MRI and can detect damage to the white matter tracts before a

high signal lesion appears on T2WI.235, 237 For example, in a study by Lee et al. (2011), four

patients who had no abnormal signal changes on the MRI had lower FA values and higher ADC

values.236 Another possible advantage of the DTI is that it may be able to distinguish between a

symptomatic and asymptomatic group of patients.235 In a study by Kerkovsky et al. (2012), the

FA values were significantly lower and the ADC values were significantly higher in a

symptomatic group than in an asymptomatic spondylotic cervical cord encroachment subgroup.

90

With respect to outcome prediction, Jones et al. (2013) and Nakamura et al. (2012)

assessed the correlation between various DTI parameters and post-surgical outcome.238, 239

Jones et al. (2013) reported a significant positive association between baseline FA measures

and outcome evaluated by NDI but not by Nurick or mJOA.238 When stratifying the patient

population based on mJOA scores, the results were slightly different. For patients with a mJOA

score of 8, lower FA values were predictive of a worse outcome: for every 0.1 point increase in

FA, the odds of improving were 1.5 times greater. In a second study by Nakamura et al. (2012),

the fiber tract (FT) ratio, calculated by dividing the number of fibers at the compressed level by

the number of fibers at the C2-level, was correlated with the JOA recovery rate (r=0.6066,

p=0.0046).239 Patients with a FT ratio below 60% typically had a poor recovery rate of less than

40%. Further studies are required to evaluate the prognostic value of DTI.


Recommendation #1: T2 signal may be a useful prognostic indicator when used in combination

with low SI change on T1WI, or as a ratio comparing compressed versus non-compressed

segments, or as a ratio of T2 compared with T1. We suggest that if surgeons use MRI signal

intensity to estimate the risk of a poor outcome following surgery, they use high SI change on

T2WI in combination with other signal intensity parameters, and not in isolation.

Strength of statement: Weak

3.5 Results Part C: Important Clinical and Surgical Predictors of Complications


The search yielded a total of 5472 citations. After initial review of abstracts and titles,

5259 studies did not meet our inclusion criteria. Following full text investigation, an additional

153 studies were excluded because 1) not all patients were diagnosed with degenerative

cervical myelopathy; 2) p-values were not provided for analyses; and/or 3) a statistical test was

not used to compare complication rates or demographic differences between two groups. A

total of 60 studies were deemed relevant following this rigorous review process (Figure 3-5).

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Figure 3-5. Search Strategy and Detailed Review Process for Systematic Review C


For KQ1, we identified 36 prognostic cohort studies discussing key clinical, imaging or

surgical predictors of complications. Of these, only nine (3 prospective, 6 retrospective)

conducted a multivariate analysis and controlled for potential confounding variables.115, 140, 240-

246 Sample sizes ranged from 81 to 58,118 surgical patients, with mean ages between 57 and

64.1 years. All patients were diagnosed with some form of degenerative cervical myelopathy,

with the majority presenting with either CSM or OPLL. The main outcome was postoperative

complications in four studies,115, 140, 240, 241 C5 or upper extremity palsy in three,242, 243, 246 major

intraoperative blood loss in one,244 and axial pain in one.245 As indicated by Table 3-17, several

clinical, imaging and surgical predictors were evaluated in these studies. Twenty-seven

additional papers reported on predictors of complications but did not use an adequate

multivariate statistical analysis. The following is a summary of the primary outcomes of these

studies: postoperative complications (n=4);4, 152, 177, 247 nerve root palsy (n=8);163, 248-254

radiculopathy (n=3);174, 255, 256 shoulder stiffness and neck pain (n=1);257 instability (n=1);258

postoperative kyphosis (n=2);259, 260 axial pain (n=3);261-263 pseudoarthrosis (n=1);198 graft

dislodgement (n=2);198, 264 closure of lamina (n=2);265, 266 and reconstruction failure (n=1).267

92

For KQ2, a total of 28 therapeutic cohort studies (2 randomized control trials, 11

prospective, 15 retrospective) met our inclusion criteria. These studies were designed to

compare the efficacy and complication rates between different surgical interventions or

techniques. Studies reported on differences between anterior and posterior approaches

(n=9);11, 107, 115, 268-273 laminoplasty and laminectomy and fusion (n=4);115, 274-276 anterior

decompression and Bryan disc (n=1);277 and anterior discectomy and fusion (ACDF) and

corpectomy (n=2).278, 279 In addition, 11 studies compared various laminoplasty techniques or

considered technical differences such as laminoplasty with and without foraminotomy or

muscle preservation.243, 250, 280-288

3.5.3 Risk of bias

For the prognostic studies, we critically appraised nine studies that conducted a

multivariate analysis. Of these, three were considered level II and six were rated level III. None

of the included prognostic studies were rated level I because patients were not at a similar time

point in the course of their disease. The two level II studies were prospective in nature, had

adequate follow-up rates and accounted for key confounding variables in their analyses. The

level III studies had moderately high risk of bias as most were retrospective cohort studies that

violated ≥1 criteria required for a good-quality cohort study.

For the therapeutic studies, the majority were graded level III as they were moderate or

poor-quality cohort studies. These studies were downgraded from level II to level III because

co-interventions were not applied equally; assessment was not blinded in prospective studies;

data was unreliable in retrospective studies; follow-up rates were <80%; and/or analyses did

not control for important confounders. Three studies were level II evidence.

3.5.4 Are there clinical or imaging factors that can predict complications?

Complications Three studies considered age and co-morbidities as important clinical predictors of

perioperative complications.115, 140, 240 Boakye et al (2008) reported that patients aged 65 years

or older were 2.28 (OR: 2.28, 95% CI: 1.74-2.98) times more likely to experience complications

93

Table 3-17. Characteristics of Prognostic Complications Studies with Multivariate Analysis: Systematic Review C

Author (year) Study Design

Sample and Characteristics Clinical or Imaging Factors Assessed

Surgical Factor Assessed

Complications

Boakye et al (2008) Retrospective cohort (III)

CSM (n=58,115) Male: 58.1% Surgery: spinal fusion

Age Gender Race Comorbidities

None Neurological, pulmonary, thromboembolic, cardiac, renal, and hematoma complications, infection, hoarseness, dysphagia

Cook et al (2008) Retrospective cohort (III)

CSM (n=37,732) Male: 56.0% Diabetes (n=3,432) Surgery: spinal fusion

Diabetes (presence vs. absence, type I vs. type II, controlled vs. uncontrolled)

None CNS, respiratory, cardiac, peripheral vascular, procedural, wound and hematoma complications, CSF leak or persistent fistula, carotid or vertebral injury, transfusion, postoperative infection, hoarseness, dysphagia

Fehlings et al (2012) Prospective cohort (II)

CSM (n=302) Male: 58.9% Mean age, yr (range): 57 (29-86) Surgery: anterior (n=176), posterior (n=107), anteroposterior (n=19)

Age Gender Comorbidity score BMI Preoperative severity Smoking status

Approach Number of stages Number of levels Spinal fusion Fusion approach Laminectomy Corpectomy Operative duration Blood loss

Minor and major perioperative complications Major perioperative complications

Furlan et al (2011) Prospective cohort (II)

CSM (n=81) Male: 70.4% Mean age, yr (range): 57 (32-88) Surgery: anterior (n=56), posterior (n=23), anteroposterior (n=2)

Age Gender Duration of symptoms Co-morbidities (number and CCI)

Approach Number of levels

Postoperative complications

Hasegawa et al (2007) Retrospective cohort (III)

CSM (n=587), OPLL (n=143), CDH (n=117), OLF (n=10) Surgery: ACDF (n=424), laminoplasty (n=345), laminectomy (n=88)

Age Gender Preoperative severity Diagnosis

Procedure Number of levels

Postoperative upper extremity palsy

Kaneyama et al (2010) Prospective cohort (II)

CSM (n=108), OPLL (n=31), CDH (n=7) Male: 74.0% Mean age, yr: 64.1 Surgery: open-door laminoplasty (n=73), double-door laminoplasty (n=73)

Age Gender Preoperative severity Diagnosis Physical signs† Radiologic examinations* CMAP amplitude

Surgical technique Number of levels

C5 palsy††

94

Kato et al (2012) Retrospective cohort (III)

OPLL (n=545) Male: 78.7% Mean age, yr : 62.7 Surgery: double-door laminoplasty (n=286), open-door laminoplasty (n=234), other types of laminoplasty (n=25)

Age Gender Comorbidities Size of OPLL OPLL type Occupying ratio C2-C7 angle High SI on T2-WI

Surgical technique Number of levels

Major intraoperative blood loss (>500g)

Kato et al (2008) Retrospective cohort (III)

CSM (n=135) OPLL (n=10) Male: 69.6% Mean age, yr (range): 61 (38-82) Surgery: Open-door laminoplasty

Age Gender Diagnosis Duration of symptoms Preoperative severity Preoperative alignment Preoperative ROM

Operative duration Blood loss Perseveration of paraspinal muscles

Axial pain**

Kimura et al (2012) Retrospective cohort (III)

OPLL (n=150) Male: 75.3% Mean age, yr (range): 60 (31-84) Surgery: ADF

Occupying ratio C2-C7 angle

Operative duration Blood loss Number of fused segments

Postoperative upper extremity paresis†*

CSM: cervical spondylotic myelopathy; CSF: cerebrospinal fluid; BMI: body mass index; CCI: Charlston co-morbidity index; OPLL: ossification of the posterior longitudinal ligament; CDH: cervical disc herniation; OLF: ossification of the ligamentum flavum; ACDF: anterior cervical discectomy and fusion; ADF: anterior decompression and fusion; CMAP: compound muscle action potential. †Physical signs included muscle strength of deltoid and biceps, muscles stretch reflex of deltoid and biceps tendon and neurologic impediment level of myelopathy *Radiologic examinations included cervical alignment, number of compressed segments, position of the superior articular process, cord inclination, high intensity area on T2-weighted image ††Deterioration of bicep and deltoid muscle strength by at least one level in a standard muscle manual test without any deterioration of other neurologic symptoms **New development or progression of axial pain at 6 months to 2 years postoperative †*Deterioration of upper extremity function.

95

following spinal fusion than those aged 18 to 44 years. Furthermore, patients 85 years or older

were at an even higher risk of postoperative complications (OR: 5.1, 95%CI: 3.08-8.35). In a

study by Fehlings et al (2012), age was considered to be an important predictor of minor and

major perioperative complications (OR: 1.029, 95% CI: 1.002-1.057, p=0.035). Similarly, Furlan

et al (2011) concluded that age was significantly associated with postoperative complications

(OR: 1.09, 95% CI: 1.015-1.172, p=0.018). (Table 3-18).

According to Boakye et al (2008), patients with three or more co-morbidities were twice

(OR: 1.98, 95% CI: 1.59-2.48) as likely to experience complications following spinal fusion than

healthy individuals. In an analysis by Fehlings et al (2012), a co-morbidity score was developed

to encompass both number of co-morbidities and severity of disease. Patients who experienced

major or minor perioperative complications did not have a significantly different co-morbidity

score than those who did not (p=0.84). In a univariate analysis by Furlan et al (2011), there was

a greater number of co-morbidities, as defined by the number of ICD-9 codes, in the

complications group compared to the non-complication group (p=0.033). In addition, the mean

Charlson co-morbidity index was higher in patients with postoperative complications, although

this difference did not reach statistical significance (p=0.092). In multivariate analysis, however,

neither the number nor the severity of co-morbidities were found to be predictive.

Cook et al (2008) focused on the impact of diabetes on various complication categories,

including dysphagia, postoperative infection, and procedural complications.241 In univariate

analysis, patients with diabetes experienced a higher rate of dysphagia (p<0.01), transfusion

(p=0.01) and cardiac (p<0.01), peripheral vascular (p=0.01), respiratory (p<0.01) and hematoma

complications (p=0.01) than patients without diabetes. In addition, cardiac (p=0.01) and

hematoma (p=0.01) complications were more prevalent in patients with uncontrolled diabetes

compared to those with controlled disease. There were no significant differences in

complication rates between patients with type I and type II diabetes.241 Following adjustment

for confounders, patients with diabetes were at a higher risk of cardiac complications than

those without diabetes (OR: 1.57, 95% CI: 1.14-2.16, p=0.01).218 In addition, patients with un-

controlled diabetes were more likely to experience, cardiac complications (OR: 2.82, 95% CI:

1.14-7.01, p=0.03), complications of hematomas (OR: 5.13, 95% CI: 2.16-12.17, p<0.01) and

96

postoperative infection (OR: 7.46, 95% CI: 1.33-41.79, p=0.02) compared to patients with

controlled diabetes.

Other clinical predictors such as gender, duration of symptoms, baseline severity score,

smoking status or BMI were not significantly associated with postoperative complications in

either univariate or multivariate analysis.115, 140, 240

Upper extremity or C5 palsy Hasegawa et al (2007), Kaneyama et al (2010) and Kimura et al (2012) explored various

clinical and imaging risk factors of upper extremity palsy following surgical decompression.242,

243, 246 The sample used in the studies by Hasegawa et al (2007) and Kaneyama et al (2010)

consisted of surgical patients with various forms of degenerative cervical myelopathy, including

CSM, OPLL and CDH. Both studies found that patients with OPLL as the primary diagnosis were

at a markedly higher risk of experiencing upper extremity palsy than those with other forms of

degenerative myelopathy (Hasegawa et al (2007), OR: 19.0, p<0.0001); Kaneyama et al (2010),

OR: 43.8, p<0.05). In the study by Hasegawa et al (2007), although older age was found to be a

significant predictor of upper extremity palsy in univariate analysis (OR: 0.1, p<0.05), it was not

significant in multivariate analysis (OR: 2.59, p=0.108). Age was also an insignificant predictor of

C5 palsy in the study by Kaneyama et al (2010).

Kimura et al (2012) conducted a study on 150 OPLL patients to determine risk factors of

neurological complications occurring within two weeks of anterior decompression and

fusion.246 High occupying ratio was the only significant clinical or imaging predictor of

postoperative upper extremity paresis (OR: 1.047, p=0.040). Age was not considered in this

analysis.

Gender, baseline severity score, and various clinical signs were not significant clinical

predictors of upper extremity palsy.242, 243 Several imaging factors were also not related to

postoperative palsy development, including cervical alignment, number of compressed

segments, position of superior articular process, cord inclination, signal change on T2-weighted

MRI and mid C2 to mid C7 angle.243, 246

97

Major Blood Loss A single study by Kato et al (2012) examined important clinical and imaging predictors of

major intraoperative blood loss during laminoplasty in patients with OPLL.244 When comparing

a group of patients with >500g blood loss to a group with ≤500g, there were no significant

differences with respect to age, gender, size or type of OPLL (continuous, segmental, mixed or

local), C2 to C7 alignment or high signal intensity on T2-weighted MRI. The occupying ratio,

however, was significantly higher (48.3±13.4%) in the major blood loss group compared to the

control group (42.7±13.2%, p=0.02). Furthermore, an occupying ratio of 60% or greater was the

only significant risk factor following multivariate logistic regression analysis (OR: 2.94, 95% CI:

1.1-5.3, p=0.03).

Axial Pain Kato et al (2008) defined postoperative axial pain as pain from the nuchal to scapular

region developing or progressing between six months and two years postoperatively.245

Following multivariate analysis, older age was the only significant predictor of axial pain (>63,

OR: 0.17, 95% CI: 0.04-0.72, p>0.05). Gender, diagnosis, duration of symptoms, baseline

severity score, preoperative alignment and range of motion were not significant risk factors.

3.5.5 Are there surgical factors that can predict complications?

Complications Two studies assessed the predictive value of various surgical factors.115, 140 Based on

univariate analysis, a two stage anteroposterior surgery (p=0.016), a longer operative duration

(p=0.009) and greater blood loss (p=0.005) were significantly associated with perioperative

complications. Following multivariate analysis, however, operative time was the only significant

surgical predictor of overall (major and minor) perioperative complications (OR: 1.005, 95% CI:

1.002-1.008, p=0.001).115 Fehlings et al (2012) also explored risk factors for major

complications, defined as an event resulting in permanent or prolonged morbidity,

prolongation of hospital stay or invasive intervention.115 Patients who were treated surgically

with a two-stage procedure were 5.30 more times (OR: 5.30, 95% CI: 1.626-17.256) likely to

98

experience a major complication compared to those undergoing either a single-stage anterior

or posterior surgery (p=0.006). Furlan et al (2011) reported that the anterior approach was

associated with fewer postoperative complications than either a posterior or circumferential

procedure (p=0.018).140 This surgical factor, however, was not a significant predictor following

multivariate analysis (p=0.248).

Fehlings et al (2012) identified no differences in surgical approach (p=0.11) or number of

operated vertebrae (p=0.067) between patients who experienced perioperative complications

and those who did not.115 Furthermore, whether the procedure included spinal fusion (p=0.82)

(anterior or posterior, p=0.064), a laminoplasty (p=0.48) or a corpectomy (p=0.84) did not

influence postoperative complication rates.

Upper Extremity or C5 Palsy Hasegawa et al (2007) reported that surgical approach (anterior versus posterior or ADF

versus laminoplasty versus laminectomy) was not associated with the occurrence of C5 palsy.242

The number of decompressed levels tended to be higher in patients with C5 palsy, although this

relationship did not reach statistical significance. Following stepwise logistic regression, a

greater number of levels was predictive of postoperative C5 palsy in patients undergoing

anterior decompression and fusion (OR: 11.5, p<0.001).

In a study by Kimura et al (2012), patients with fusion of three or more levels (p=0.017)

and a longer operative duration (p=0.002) were at an increased risk of developing upper-

extremity palsy.246 These two relationships, however, were not significant in multivariate

analysis (p=0.77, p=0.74, respectively). Increased intraoperative blood loss was the only

reported surgical predictor of upper-extremity palsy (OR: 1.002, 95% CI: 1.000-1.003, p=0.047).

Finally, Kaneyama et al (2010) identified that patients undergoing open-door

laminoplasty were at a significantly higher risk of developing C5 nerve palsy than those treated

with double-door laminoplasty (OR: 69.6, 95% CI: 1.14-999.99, p=0.043).243 This estimate,

however, has wide confidence limits and is unstable. Number of open laminae was the only

other surgical factor considered in this study and was not predictive of C5 palsy (p=0.483).

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Major Blood Loss There was no difference in the number of operated levels between patients who

experienced excessive blood loss (5.1±1.0) and those who did not (5.0±0.8, p=0.68).244

Operative duration, however, was significantly longer in the major blood loss group (240±112

versus 149±59 minutes).

Axial Pain Based on multivariate analysis, preservation of paraspinal muscles attached at C2

decreased the risk of postoperative axial pain (OR: 0.13, 95% CI: 0.02-0.98, p<0.05).245

Operative duration, blood loss and preservation of paraspinal muscles attachment at C7 were

not significantly associated with postoperative axial pain.

3.5.6 Results of studies without multivariate analysis

Table 3-19 summarizes the results of studies that identified important predictors of

complications but did not conduct a multivariate analysis.

Based on the results of two studies, preoperative severity was not related to overall

complication rates.4, 152 In a study by Holly et al (2008), complication rates were compared

between patients older than 75 years and patients under 65 years.152 In the elderly group, the

complication rate (38%) was significantly higher than in the younger group (6%, p=0.002). In a

study by Lu et al (2008), the incidence of complications was also much higher (35%) in the aged

group (≥70 years) compared to the control group (9.7%) (<70 years), although this relationship

did not reach statistical significance.177

There was consensus in the literature that age (n=4),248, 250, 251, 254 gender (n=2),251, 254

preoperative severity (n=5),248, 250, 251, 253, 254 muscle manual test results (n=2),250, 251 duration of

symptoms (n=2),250, 254 diagnosis (n=2),248, 254 preoperative cervical lordosis (n=2),248, 254 and

cervical curvature (n=2)249, 250 are not significant predictors of nerve root palsy. In a study by

Imagama et al (2010), patients experiencing C5 palsy had a smaller C5 intervertebral foramen

width and a higher degree of anterior protrusion of the C5 superior articular process.254

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Table 3-18. Important Clinical, Imaging and Surgical Predictors of Perioperative Complications: Results of Univariate and Multivariate Analysis

Author (year) Study Design Univariate Analysis Multivariate Analysis (Clinical/Imaging Factors)

Multivariate Analysis (Surgical Factors)

Boakye et al (2008)

Multiple logistic regression analysis (presence/absence of complications)

Age: p<0.0001 Comorbidities: p<0.0001 Gender: p=0.60 Race: p=0.48

Age (reference=18-44 yrs) 45-64 yrs: OR 1.41 (1.10-1.83) 65-84 yrs: OR 2.28 (1.74-2.98) ≥85 yrs: OR 5.07 (3.08-8.35) Co-morbidities (reference=0) 1: OR 1.27 (1.04-1.54) 2: OR 1.47 (1.18-1.83) ≥3: OR 1.98 (1.59-2.48)

None

Fehlings et al (2012)

Multiple logistic regression analysis (presence/absence of minor and/or major complications)

Any Perioperative Complication Age: p=0.006 Number of stages: p=0.016 Operative duration: p=0.009 Operative blood loss: p=0.005 Comorbidity score††: p=0.84 Gender: p=0.75 BMI: p=0.48 Baseline mJOA: p=0.28 Smoking status: p=0.47 Surgical approach: p=0.11 Number of levels: p=0.067 Spinal fusion: p=0.82 Fusion approach: p=0.064 Laminoplasty performed: p=0.48 Corpectomy performed: p=0.84

Any Perioperative Complication Age: OR 1.029 (1.002-1.057), p=0.035 Major Complication Age: OR: 1.054 (1.015-1.094), p=0.006. Not significant in either model: Gender: p>0.05 Co-morbidity score††: p>0.05 BMI: p>0.05 Smoking status: p>0.05 Baseline severity: p>0.05

Any Perioperative Complication Operative duration: OR 1.005 (1.002-1.008), p=0.001 Number of stages: p>0.05 Major Complication Combined anterior-posterior procedure: OR: 5.297 (1.626-17.256), p=0.006. Operative duration: p>0.05 Not significant in either model: Operative blood loss: p>0.05 Approach: p>0.05 Number of levels: p>0.05 Laminoplasty: p>0.05 Corpectomy: p>0.05

Furlan et al (2011)

Multiple logistic regression analysis (presence/absence of complications)

Age: p=0.006 Number of ICD-9 codes: p=0.033 Approach: p=0.018 Gender: p=0.534 Duration of symptoms: p=0.106 CCI: p=0.092 Number of levels: p=0.233

Age: OR 1.09 (1.015-1.172), p=0.018 Gender: p=0.188 Number of ICD-9 codes: p=0.113 Duration of symptoms: p=0.309

Surgical approach: p=0.248 Number of levels: p=0.454

101

Cook et al (2008)

Multiple logistic regression analysis controlling for age, discharge year, race, gender, household income, hospital size, hospital type, primary insurance payer and number of levels fused (presence/absence of complication subgroups)

Diabetes (vs. no diabetes) Respiratory complications: p<0.01 Cardiac complications: p<0.01 Peripheral vascular complications: p=0.01 Complications of hematomas: p=0.01 Transfusion: p=0.01 Dysphagia: p<0.01 Other complications: p=0.001 Controlled diabetes (vs. un-controlled) Cardiac complications: p=0.01 Complications of hematomas: p=0.01

Diabetes (reference=no diabetes) Cardiac complications: OR 1.57 (1.14-2.16), p=0.01 Other complications: OR 1.54 (1.17-2.01), p=0.01 Un-controlled Diabetes (reference=controlled diabetes) Cardiac complications: OR 2.82 (1.14-7.01), p=0.03 Complications of hematomas: OR 5.13 (2.16-12.17), p<0.01 Postoperative infection: OR 7.46 (1.33-41.79), p=0.02

Hasegawa et al (2007)

Multiple logistic regression analysis (presence/absence of upper extremity palsy)

All patients, Anterior procedure Age: p<0.05, <0.01 Diagnosis: <0.0001, <0.001 Number of levels: p=0.0591, <0.001 Procedure: NS, NA Gender: NS, NS Baseline severity score: NS, NS

All patients: Diagnosis (OPLL): OR 19.0, p<0.0001 Age: OR 2.59, p=0.108 Anterior procedure: Age OR: 3.13, p=0.076

Anterior procedure: Number of levels: OR 11.5, p<0.001

Kaneyama et al (2010)

Multiple logistic regression analysis (presence/absence of C5 palsy)

Approach: p<0.05 Diagnosis (OPLL): OR 43.8 (1.03-999.99), p=0.048 Age: p=0.964 Gender: p=0.252 Baseline severity: p=0.219 Physical signs†: p=0.117-0.998 Radiologic examinations*: p=0.101-0.314 CMAP amplitude: p=0.112-0.291

Open-door laminoplasty (vs. double-door): OR 69.6 (1.14-999.99), p=0.043 Number of opened lamina: p=0.493

Kimura et al (2012)

Multiple logistic regression (presence/absence of upper extremity paresis)

Occupying ratio: p=0.005 Blood loss: p=0.021 Operative duration: p=0.002 Long fusion (≥3 segments): p=0.017 C2-C7 angle: p=0.758

Occupying ratio: OR 1.047 (1.002-1.093), p=0.040

Blood loss: OR 1.002 (1.000-1.003), p=0.047 Operative duration : p=0.74 Long fusion (≥3 segments): p=0.77

Kato et al (2012)

Multiple logistic regression analysis

Occupying ratio: p=0.02 Operative duration: p<0.001 Diabetes: p=0.30 Hypertension: p=0.16

Occupying ratio (≥60%): OR 2.4 (1.1-5.3), p=0.03 Age: p>0.05 Gender: p>0.05

102

OR: odds ratio; BMI: body mass index; CCI: Charlston co-morbidity index; ICD: international classification for diseases; (m)JOA: (modified) Japanese Orthopedic Association; OPLL: ossification of the posterior longitudinal ligament; SI: signal intensity; WI: weighted image; ROM: range of motion; CMAP: compound muscle action potential; NS: not significant; NA: not applicable. ††Comorbidity score includes both severity and number of co-morbidities (1 point for mild, 2 for moderate, 3 for severe: summed over several types of co-morbidities) †Physical signs included muscle strength of deltoid and biceps, muscles stretch reflex of deltoid and biceps tendon and neurologic impediment level of myelopathy *Radiologic examinations included cervical alignment, number of compressed segments, position of the superior articular process, cord inclination, high intensity area on T2-weighted image.

(presence/absence of major intraoperative blood loss)

Number of levels: p=0.68 Gender: p=0.17 Age: p=0.10 Size of OPLL: p=0.23 OPLL type: p=0.14 C2-C7 angle: p=0.93 High SI on T2WI: p=0.51

Size of OPLL: p>0.05 OPLL type: p>0.05

Kato et al (2008)

Multiple logistic regression analysis (presence/absence of postoperative axial pain)

No factors were significant in univariate analysis (all factors analyzed are shown in adjacent columns)

Age (>63 yrs): OR 0.17 (0.04-0.72), p<0.05 Gender: p>0.05 Diagnosis: p>0.05 Duration of symptoms: p>0.05 Baseline severity: p>0.05 Preoperative Alignment: p>0.05 Preoperative ROM: p>0.05

Preserving of paraspinal muscles attached at C2 (vs. non-preserving): OR 0.13 (0.02-0.98), p<0.05 Operative time: p>0.05 Blood loss: p>0.05 Preserving of paraspinal muscles attached at C7 (vs. non-preserving): p>0.05

103

Komagata et al (2004), however, noted no significant difference in the extent of anterior

protrusion between patients with (4.3±4) and without (3.73±3) palsy (p=0.51).250 Hyperintensity

on a MRI was also not an important imaging predictor of nerve root palsy.251, 254

Three studies examined important predictive factors of radiculopathy following

surgery.174, 255, 256 In a study by Greiner-Perth et al (2005), patients who had symptoms of C5

and/or C6 radiculopathy were on average older (62.4 versus 58.6 years, p<0.01) and had a

greater number of operated levels (2.2 versus 1.7 levels, p<0.001) than those who did not. The

incidence of postoperative radiculopathy, however, was not different between an elderly

patient group (≥70 years) and a younger patient group (<70) in a study by Kawaguchi et al

(2003) (p=0.18). There was consensus in the literature that preoperative severity (n=2) was not

significantly associated with postoperative radiculopathy.255, 256

Risk factors for stability complications such as instability (n=1)258 and postoperative

kyphosis (n=2)259, 260 were assessed by three studies. Guigui et al (1998) reported that patients

who were younger (p=0.03), had a hypermobile spine (p<0.0001), greater preoperative range of

motion (p<0.0001) and received a C2 laminectomy (p=0.0164) were more susceptible to spinal

destabilization. With respect to postoperative kyphosis, a single study reported that a diagnosis

of CSM, smaller preoperative Cobb’s angle in neutral (p=0.000), larger degree of preoperative

flexion (p=0.023) and small degree of preoperative extension (p=0.025) were all important

predictors.260

As demonstrated by two studies, an increase in the number of operative lamina is

significantly associated with increased incidence of axial pain (p<0.001, p=0.03).261, 263 A

diagnosis of OPLL (compared to CSM) is also an important clinical predictor of axial pain

(p=0.027).261

As indicated by single studies, risk factors for various surgical complications include: 1)

younger age (p<0.05) and multilevel surgery (p<0.002) for pseudoarthrosis;198 2) previous

surgery for graft complications (p<0.001);198 3) preoperative kyphosis for closure of lamina

(p=0.014);265 and 4) a larger number of fused segments for reconstruction failure (4.2 versus

104

3.5, p<0.05).267 Age (n=3)265-267 and gender (n=3)265-267 were not significant predictors of surgical

complications.

Table 3-19. Clinical, Imaging and Surgical Predictors of Complications: Results of Prognostic Studies without a Multivariate Analysis

Author (year)

Patient Population, Surgical Treatment

Related to Not related to

Postoperative Complication Rates

Fehlings et al (2013)

CSM (n=278), anterior (n=169), posterior (n=95), combined (n=14)

Preoperative severity

Holly et al (2008)

CSM (n=70), anterior (n=10), posterior (n=24), combined (n=2)

Older age Preoperative severity

Lu et al (2008)

CSM (n=51), corpectomy Age

Meyer et al (2011)

CSM (n=12), OPLL (n=28), laminoplasty

Diagnosis

Neurological Complications: Nerve Root Palsy

Chen et al (2007)

OPLL (n=49), laminectomy and posterior fixation

Age, gender, preoperative severity, MMT, number of operated levels, preoperative lordosis, occupying rate, SI on T2-WI

Chen et al (2009)

OPLL (n=83), laminectomy and instrumented fusion

Preoperative lordosis, occupying rate, presence of snake-eye sign on MRI

Chen et al (2013)

OPLL (n=30), posterior hybrid technique (n=15), laminoplasty (n=15)

Segmental instability

Chiba et al (2002)

OPLL, CSM, CDH (n=208), laminoplasty


Imagama et al (2010)


Smaller width of C5 intervertebral foramen, higher degree of anterior protrusion of the C5 superior articular process

Age, gender, duration of symptoms, preoperative severity, diagnosis, preoperative lordosis, number of compressed levels, SI on T2WI, operative duration, estimated blood loss, position of bony gutter, surgical procedure

Komagata et al (2004)


Age, duration of symptoms, preoperative severity, deltoid MMT, cervical spine curvature index, degree of anterior protrusion of the superior articular process

Liu et al (2010)

CSM (n=101), laminectomy and posterior internal fixation

Mean cervical curvature, cervical curvature index

Minoda et al (2003)


Age, preoperative severity, diagnosis, preoperative lordosis, foramen diameter, preoperative lamina angle, gutter position,

105

spinal cord flatness, preoperative anterior cord distance

Neurological Complications: Radiculopathy

Greiner-Perth et al (2005)

CSM (n=121), corpectomy

Older age, greater number of levels


Kawaguchi et al (2003)

CSM (n=106), laminoplasty

Age

Uematsu et al (1998)

CSM, OPLL, developmental canal stenosis (n=365), laminoplasty

Diagnosis, narrowest level, preoperative severity, degree of flatness, cervical curvature, extent of anterior protrusion of the superior articular process, laterality of osteophytes or OPLL, angle of lamina before expansion.

Neurological Complications: Shoulder stiffness and neck pain

Yoshida et al (2002)

CSM (n=90), OPLL (n=66), CDH (n=14), CLF (n=3), laminoplasty

Age, AP canal diameter, preoperative severity, diagnosis, cervical alignment.

Stability Complications: Instability

Guigui et al (1998)

CSM (n=58), laminectomy without fusion

Greater preoperative ROM, younger age, C2 laminectomy and hypermobility

Gender, number of removed lamina, preoperative olisthesis, preoperative type of curvature.

Stability Complications: Postoperative Kyphosis

Kaptain et al (2000)

CSM (n=46), laminectomy

Gender, age, history of smoking, C2 laminectomy, foraminotomy, number of levels, preoperative type of curvature (lordosis vs. straight)

Suk et al (2007)

CSM (n=52), OPLL (n=29), CDH (n=4), laminoplasty

Diagnosis of CSM, smaller preoperative Cobb’s angle in neutral, <10⁰ preoperative lordotic angle in neutral, preoperative kyphotic angle during flexion greater than lordotic angle during extension

Axial Pain

Motosuneya et al (2011)


Diagnosis of OPLL, number of open lamina

Ohnari et al (2006)

CSM (n=33), OPLL (n=14), CYL (n=2), CDH (n=1), OYL (n=1), laminoplasty

Age, blood loss, operative duration, gender, duration of cervical orthosis use, reconstruction of semispinalis cervicis muscle, preoperative axial symptoms

Sasai et al (2005)

CDH (n=32), microsurgical posterior

Number of EBLPs

106

herniotomy with en bloc laminoplasty

Surgical Complications: Pseudoarthrosis (Emery et al (1998)); Graft (Emery et al (1998), Kumar et al (2009)); Closure of lamina (Matsumoto et al (2008), Wang et al (2011)); Reconstruction failure (Okawa et al (2011)).

Emery et al (1998)

CSM (n=108), anterior decompression and arthrodesis

Pseudoarthrosis: Younger age, multilevel surgery

Smoking

Emery et al (1998)

CSM (n=108), anterior decompression and arthrodesis

Graft: Previous surgery

Kumar et al (2009)

OPLL, CSM (n=410), corpectomy

Number of levels, the location of the lower mortise in the C7 body (compared to other vertebral bodies)

Matsumoto et al (2008)


Preoperative kyphosis Gender, age, cause of myelopathy, use of anchor screws.

Wang et al (2011)


Age, gender

Okawa et al (2011)

CSM (n=7), OPLL (n=24), corpectomy and reconstruction

Larger number of fused segments

Age, gender, preservation of vertebral endplates, preoperative C2-C7 lordotic angle, fused lordotic angle, C7 horizontal angle, screw type, use of intermediate screws for fibular grafting.

CSM: cervical spondylotic myelopathy; OPLL: ossification of the posterior longitudinal ligament; SI on T2-WI: hyperintensity on T2-weighted MRI; MRI: magnetic resonance image; CDH: cervical disc herniation; MMT: muscle manual test; CLF: calcification of the ligamentum flavum; AP: anteroposterior; JOA: Japanese Orthopedic Association; ROM: range of motion; OYL: ossification of the yellow ligament; CYL: calcification of the yellow ligament; EBLP: en bloc laminoplasty

3.5.7 Are rates of complications different between surgical interventions or varying techniques?

Anterior versus Posterior Fehlings et al (2012, 2013) compared rates of complications between anterior and

posterior surgical groups.107, 115 Patient demographics were significantly different in the two

cohorts: patients treated anteriorly were on average younger and less severe, had fewer

operated levels and a smaller volume of blood loss than those treated posteriorly. There were,

however, no significant differences in rates of perioperative complications (Fehlings 2012:

p=0.11; 2013: p=0.197), major complications (p=0.61), new neurological deficits (p=1.00), C5

radiculopathy (p=1.00) or dysphagia (p=0.65). Based on results from Fehlings et al (2012), there

was a higher incidence of postoperative infection in the posterior group (4.7%) compared to the

anterior group (0.6%, p=0.030). Ghogawala et al (2011) also reported similar overall

complication rates (p=1.00) between patients who underwent anterior cervical decompression

and fusion and those treated posteriorly by laminectomy with fixation.268 In addition, there

107

were no significant differences in rates of overall (p=0.654), surgical (p=0.507), medical (p=1.00)

or irreversible complications (p=0.74) between corpectomy and laminectomy with fusion

surgeries.269 The frequency of dysphagia and transient hoarseness, however, was greater in the

corpectomy group (7.1%) compared to the laminectomy with fusion group (0%). (Table 3-20).

Five studies analyzed differences in complication rates between anterior decompression

surgery and laminoplasty.11, 270-273 The surgical approach was at the discretion of the attending

surgeon in two studies.11, 272 In the other three studies, the design was quasi-randomized:

patients were treated anteriorly within a specific time frame and posteriorly in subsequent

years.270, 271, 273 Based on results from Edwards et al (2002) and Liu et al (2011), there was a

higher incidence of complications in the anterior group (ACDF with plate cage benezech

(p<0.05) or corpectomy (p<0.05)) compared to the laminoplasty group. Rates of axial pain were

also significantly higher in laminoplasty surgery compared to anterior decompression and

fusion.270 These conclusions, however, were based on single studies. According to Sakaura et al

(2005), there were no significant differences in rates of C5 palsy, bone graft complications,

donor site morbidities, axial pain or postoperative kyphosis between the groups.

Anterior Techniques Three studies compared the efficacy and safety of various anterior approaches: ACDF

versus Bryan Disc Prosthesis (n=1)277 and ACDF versus corpectomy (n=2).278, 279 Cheng et al

(2011) conducted a randomized control trial to compare operative and postoperative

characteristics between a Bryan disc and an ACDF group. Complication rates were higher in the

ACDF group since dysphagia was seen in seven patients as compared to only one in the Bryan

group (p<0.001).

When comparing ACDF to anterior corpectomy, there were no significant differences in

rates of surgical complications (p=0.694), CSF leakage (p=0.604), hoarseness (Song (2012):

p=0.742; Ling (2012): p=1.00), epidural hematoma (p=1.00), C5 radiculopathy (p=1.00),

dysphagia (Song (2012): p=0.436; Ling (2012): p=1.00), graft dislodgement (p=0.246),

subsidence (p=0.121) and dural tear (p=0.688). Although graft-related complications were not

significantly different between surgical groups in the study by Song et al (2012), Lin et al (2012)

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reported a higher incidence of total instrumented and graft-related complications in the

corpectomy group (p=0.032).

Posterior Techniques Four studies compared complication rates between laminoplasty and laminectomy with

fusion.115, 274-276 According to studies by Fehlings et al (2012) and Woods et al (2011), there is no

difference in overall complication rates between posterior surgeries (Fehlings (2012): p=0.14;

Wood (2011): p=0.087). In addition, rates of wound infections (Yang (2013): p=0.285; Fehlings

(2012): p=0.43), dysphagia (p=0.32), neck pain (p=0.066), CSF leakage (p=0.252), kyphosis

(p=0.756) and restenosis (p=0.448) were not significantly different between the two groups. In

studies by Chen et al (2012) and Yang et al (2013), there was a higher incidence of C5 palsy in

the laminectomy with fusion group compared to the laminoplasty group (p<0.05). Fehlings et al

(2012), however, noted no differences in rates of C5 radiculopathy between the two posterior

techniques. Although Chen et al (2012) reported no significant differences between

laminoplasty and laminectomy with fusion with respect to axial pain, Yang et al (2013) found a

higher incidence of axial pain in patients treated with laminectomy with fusion.

Laminoplasty Techniques Various laminoplasty techniques have been designed and modified to improve surgical

outcome and decrease postoperative morbidity. In this review, 11 studies compared

complication rates between different forms of laminoplasty procedures. In a quasi-randomized

study, Park et al (2012) observed no significant differences in aggravated neck pain (p=0.34), C5

palsy (p=0.34) or other complications (p=1.00) between midline-splitting and unilateral single-

door laminoplasty.284 Similarly, hinge position (wide group: hinge located at the inner margin of

the lateral mass; narrow group: hinge at the lamina margin) was not associated with the

development of C5 palsy (p=0.17).288 In a comparative study between open-door and double

door laminoplasty, C5 palsy rates tended to be higher following open-door laminoplasty

(p<0.05).243

109

Selective expansive open-door laminoplasty (ELAP) was designed to reduce damage to

cervical posterior elements and prevent common long-term problems of laminoplasty such as

axial symptoms, malalignment and decreased range of motion.287 This procedure consists of an

open-door laminoplasty at the levels of stenosis and a partial laminectomy combined with

laminoplasty at the most inferior stenotic level. As indicated by Tsuji et al (2007), the incidence

of C5 palsy was significantly lower in the selective ELAP group than in a C3-C7 ELAP group

(p=0.037).

Another modification of laminoplasty was designed to reduce the amount of bone

grafting during operation (three levels only in the open side), shorten the period of orthosis

application and encourage patients to exercise their posterior neck muscles postoperatively.283

Complication rates in this modified group were compared to rates following traditional open-

door en bloc laminoplasty. There was a lower incidence of axial pain and neck stiffness in the

modified group (p=0.0019).

Two studies examined the effectiveness of foraminotomy in combination with

laminoplasty in preventing postoperative C5 palsy.250, 282 Both Komagata et al (2004) and

Katsumi et al (2012) reported a significantly lower incidence of C5 palsy in the concurrent

foraminotomy group (p<0.05).

Four studies have explored muscle-preserving techniques and have compared rates of

axial pain, postoperative kyphosis and C5 nerve root palsy.280, 281, 285, 286 Sakaura et al (2010,

2008) examined whether preservation of 1) subaxial deep extensor muscles or 2) the funicular

section of the nuchal ligament attached to the C6 and C7 spinous process could prevent poor

radiologic outcomes and axial pain. Based on results from these two studies, there was no

significant difference in rates of axial pain between muscle-preserving and muscle-disrupting

groups. Incidence of postoperative kyphosis was also not different between the subaxial deep

extensor preserving group and the control group.

The cohort study conducted by Hosono et al (2006) compared C3-7 to C3-6

laminoplasty. The only difference between the procedures was that the C7 lamina was opened

in the C3-7 group because these patients exhibited cord compression at C6/7 or lower levels.

110

Early and late axial pain rates were higher in the C3-7 group likely due to disruption of the

trapezius and rhomboideus minor muscles that typically attach to the C7 vertebra (early:

p=0.006; late: p=0.015). These results were confirmed by a second study by Hosono et al

(2007): there was a higher incidence of early and late axial pain in the left-opened C3-7 group

compared to either the left- or right-opened C3-6 groups (early: p=0.0008; late: p=0.0036).

3.5.8 Summary of Evidence

High evidence suggests that old age is predictive of perioperative complications. Low

evidence reports that old age decreases the risk of postoperative axial pain. Age, however, is

not associated with upper extremity/C5 palsy (low evidence).

Based on low evidence, there is no association between perioperative complications

and co-morbidities, baseline severity score, BMI, duration of symptoms or smoking status.

Gender is also not a significant predictor (moderate evidence).

Low evidence suggests that patients with a diagnosis of OPLL are at a greater risk of

developing upper extremity palsy or C5 palsy postoperatively.

Low to moderate evidence suggests no association between perioperative

complications and surgical approach, number of levels, estimated blood loss, laminoplasty or

corpectomy. Longer operative duration is predictive of overall perioperative complications

(moderate evidence) but not of major complications (low evidence).

Based on moderate evidence, there are higher rates of neck pain in laminoplasty

compared to anterior spinal fusion and higher rates of dysphagia in ACDF than in Bryan disc.

With respect to laminoplasty techniques, there is no difference in rates of C5 palsy between

wide and open-door laminoplasty (moderate evidence). Low evidence reports a lower incidence

of C5 palsy in laminoplasty with concurrent foraminotomy compared to laminoplasty with no

foraminotomy. (Table 3-21).

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Table 3-20. Comparative Surgical Studies reporting differences in Complication Rates

Author (year) Diagnosis, surgical procedure Similarities and differences between groups

Complications Results

Anterior versus Posterior

Fehlings et al (2013) III

Discretion of surgeon CSM (n=264) Anterior (n=169), posterior (n=95)

No difference in gender, smoking status or duration of symptoms. Age (52.33 years in anterior and 62.83 years in posterior, p<0.0001); Preoperative mJOA (13.47 in anterior and 11.84 in posterior, p<0.0001); Preoperative Nurick (3.96 in anterior, 4.40 in posterior, p<0.0001). Number of operated levels (3.13 in anterior and 5.12 in posterior, p<0.0001).

Complications, new neurological deficits, superficial infection

No significant differences in rates of complications (p=0.197), new neurological deficits (p=1.00) or superficial wound infections (p=0.058).


Discretion of surgeon CSM (n=283) Anterior (n=176), posterior (n=107)

No difference in operative duration. Age (52.3 years in anterior and 62.9 years in posterior, p<0.001); Preoperative mJOA (13.6 in anterior and 11.8 in posterior, p<0.001); Estimated blood loss (170 mL in anterior, 381 mL in posterior, p<0.001).

Minor and/or major complications, major complications, wound infection, C5 radiculopathy, dysphagia

No significant differences in rates of minor and/or major complications (p=0.11), major complications (p=0.61), C5 radiculopathy (p=1.00) or dysphagia (p=0.65). Higher incidence of wound infection in posterior group (p=0.030).

Anterior Discectomy versus Laminectomy

Ghogawala et al (2011) III

Discretion of surgeon CSM (n=50) ACDF (n=28), LMF (n=22)

No difference in age, gender or preoperative NDI, EQ-5D or SF-36. Baseline mJOA (13.4 in anterior and 11.6 in posterior, p<0.01). Number of operated levels (2.1 in anterior and 3.1 in posterior, p<0.001).

Complications No significant difference in complications (p=1.00).

Anterior Corpectomy versus Laminectomy

Kristof et al (2009) III

Quasi-randomized CSM (n=103) Corpectomy (n=42), LMF (n=61)

No difference in gender, comorbidities, ASA, BMI, duration of symptoms, preoperative Nurick, preoperative neck pain, C3-7 Cobb angle or blood loss.

Surgical, medical or irreversible complications, radiculopathy,

No significant difference in overall complications (p=0.654), surgical complications (p=0.507), radiculopathy (p=0.232), wound infection (p=0.331), hardware failure (p=0.108), medical

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Age (62.5 years in corpectomy and 66.0 years in LMF, p=0.012). Operative duration (229 min in corpectomy and 184 min in LMF, p<0.001).

dysphagia, wound infection, hardware failure, pneumonia, renal failure, sepsis

complications (p=1.00), pneumonia (p=0.797), or irreversible complications (p=0.74). Higher incidence of dysphagia/transient hoarseness in corpectomy group (p=NR).

Anterior Decompression versus Laminoplasty

Edwards et al (2002) III

Discretion of surgeon CSM (n=25), OPLL (n=1) Corpectomy (n=13), LMP (n=13)

No difference in age, duration of symptoms, myelopathy severity, mean lordosis, sagittal motion from C2-7, or operative duration.

Complications, axial pain

Higher incidence of complications in corpectomy group (p<0.05). No significant differences in axial pain.

Hosono et al (1996) III

Quasi randomized CSM (n=98) Corpectomy (n=26), LMP (n=72)

No difference in age, gender, preoperative JOA, or AP spinal canal diameter.

Axial pain Higher incidence of axial symptoms in laminoplasty group (p<0.05).

Koakutsu et al (2010) II

Quasi randomized Soft disc herniation (n=50) ACDF (n=25), LMP (n=25)

No difference in age, gender, level of disc herniation, AP canal diameter and operative duration. Blood loss (128mL in ACDF and 63mL in LMP, p=0.0084). Occupancy ratio (47.4% in ACDF and 55.5% in LMP, p=0.0043).

Neck pain Higher incidence of neck pain at 1-year in laminoplasty group (p=0.037).

Liu et al (2011) III

Discretion of the surgeon CSM (n=52) ACDF with PCB (n=25), LMP (n=27)

No difference in age, duration of symptoms, number of blocks, canal diameter, preoperative JOA score, sagittal alignment, Cobb angle, sagittal diameter or ROM. Operative duration (116 in ACDF and 188 min in LMP, p<0.001) Estimated blood loss (118 mL in ACDF and 361 mL in LMP, p<0.001)

Complications Higher incidence of complications in the ACDF group (p<0.05).

Sakaura et al (2005) III

Quasi randomized CDH (n=43) ASF (n=21), LMP (n=22)

No difference in age, preoperative JOA, duration of symptoms, number of levels of disc herniation, AP diameter or transverse area.

C5 palsy, bone graft, donor site morbidity, axial pain, kyphotic deformities

No significant difference in C5 palsy, bone graft complications, donor site morbidities, axial pain or kyphotic deformities.

Anterior Decompression and Fusion versus Bryan Disc

Cheng et al (2011) II

Randomized CDH or stenosis (n=83) ACDF (n=42), Bryan (n=41)

No difference in age, gender, smoking status or preoperative NDI, SF-36 and JOA. Blood loss (100 mL in Bryan and 150 mL in ACDF, p<0.0001).

Dysphagia

Higher incidence of dysphagia in ACDF (p<0.001).

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Operative duration (132.4 min in Bryan, 115.1 min in ACDF, p<0.0001)

Anterior Discectomy versus Corpectomy

Lin et al (2012) III

Allocation undefined CSM (n=120) ACDF (n=57), ACCF (n=63)

No difference in age, gender or operative segments. Blood loss (103 mL in ACDF and 149 mL in ACCF, p=0.000). Operative duration (138 min in ACDF and 125 min in ACCF, p=0.021).

CSF leakage, hoarseness, epidural hematoma, C5 radiculopathy, dysphagia, graft dislodgement, subsidence

No significant difference in surgery-related complications (p=0.694), CSF leakage (p=0.604), hoarseness (p=1.00), epidural hematoma (p=1.00), C5 radiculopathy (p=1.00) or dysphagia (p=1.00). No significant difference in graft dislodgement (p=0.246) or subsidence (p=0.121). Higher incidence of total instrumentation and graft-related complications in ACCF group (p=0.032).

Song et al (2012) III

Allocation undefined CSM (n=40) ACDF (n=25), ACCF (n=15)

No difference in age, gender, number of fusion levels or graft materials. Blood loss (621 mL in ACDF, 1011 mL in ACCF, p=0.001). Operative duration (186 min in ACDF, 268 min in ACCF, p=0.024).

Hardware related, pseudoarthrosis, dysphagia, hoarseness, donor site pain, graft related, dural tear

No significant difference in hardware-related complications (p=0.408), pseudoarthrosis (p=0.537), dysphagia (p=0.436), hoarseness (p=0.742), donor site pain (p=0.092), graft-related complications (p=0.158), or dural tear (p=0.688).

Laminoplasty versus Laminectomy

Chen et al (2012) III

Cervical alignment OPLL (n=164) ACCF (n=91), LMP (n=41), LMF (n=32)

No difference in age, gender, occupying rate or number of intervertebral levels. Preoperative JOA score (10.2 in LMP, 9.1 in LMF, p<0.05). All patients in LMF group had cervical kyphosis. All patients in LMP group had cervical lordosis.

C5 palsy Axial pain

No difference in axial pain between LMP and LMF. Higher incidence of C5 radiculopathy in LMF compared to LMP (p<0.001).


Discretion of surgeon CSM (n=302) LMP (n=34), LMF (n=82)

No significant difference in age or baseline severity. Operative duration (150 min in LMP and 225 min in LMF, p<0.001). Estimated blood loss (198 mL in LMP and 476 mL in LMF, p<0.001).

Minor and/or major complications, major complications, wound infection, C5 radiculopathy, dysphagia.

No significant difference in rates of minor and/or major complications (p=0.14), major complications (p=1.00), wound infections (p=0.43), C5 radiculopathy (p=0.50) or dysphagia (p=0.32).

Woods et al (2011) III

Discretion of the surgeon CSM (n=121) LMF (n=82), LMP (n=39)

No difference in preoperative alignment.

Complications, neck pain

No significant difference in complication rates (p=0.087) or neck pain at final follow-up (p=0.066).

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Yang et al (2013) III

Discretion of the surgeon CSM (n=141) LMF (n=66), LMP (n=75)

No difference in age, gender, baseline severity score, number of operative levels, cervical curvature, ROM, or preoperative area of dural sac. Blood loss (284.53 mL in LMP and 310.91 mL in LMF, p=0.003). Operative duration (145.07 min in LMP and 173.79 min in LMF, p=0.000).

C5 radiculopathy, CSF leakage, kyphosis, wound infection, restenosis, axial pain

No significant difference in CSF leakage (p=0.252), kyphosis (p=0.756), wound infection (p=0.285) and restenosis (p=0.448). Higher incidence of C5 radiculopathy (p=0.022) and axial pain (p=0.001) in the laminectomy and fusion group.

Laminoplasty Techniques


Patients were treated by C3-C7 LMP if they had cord compression at C6/7 or lower levels. CSM (n=65) C3-C6 (n=37), C3-C7 (n=28) LMP

No difference in age, gender, or blood loss. Operative duration (113 min in C3-6 and 129 min in C3-7 group, p=0.0082).

C5 palsy, early and late axial pain

No significant difference in C5 palsy (p=0.63). Higher incidence of early and late axial pain in C3-7 group (p=0.006, p=0.015)


Quasi-randomized CSM (n=65), OPLL (n=21), CDH (n=5) Left-opened C3-C7 (n=37), C3-C6 (n=31), right opened C3-C6 (n=23) LMP

No difference in age, gender or preoperative JOA

Early and late axial pain

Higher incidence of early and late axial pain in left-opened C3-C7 group compared to the C3-C6 opened groups (p=0.0008, p=0.0036).

Kaneyama et al (2010) III

Quasi-randomized CSM (n=108), OPLL (n=31), CDH (n=7) Open-door (n=73), double-door (n=73) LMP

No difference in gender, age, diagnosis, preoperative JOA or number of opened laminae.

C5 palsy Higher incidence of C5 palsy in open-door group (p<0.05).

Katsumi et al (2012) III

Quasi-randomized CSM (n=238), OPLL (n=42), CDH (n=2) LMP (NFG, n=141), LMP with C4/5 foraminotomy (FG, n=141)

No difference in age, gender, diagnosis, blood loss or number of decompressed levels. Operative duration (129 min in FG and 102 min in NFG, p<0.0001).

C5 palsy Higher incidence of C5 palsy in NFG (p<0.05).

Kawaguchi et al (2003) III

Quasi-randomized CSM (n=56) Modified LMP (n=28)††, original LMP (n=28)

No difference in estimated blood loss, preoperative cervical alignment, ROM or space available for spinal cord. Operative duration (130 min in modified group and 162 min in original group, p=0.001).

Axial pain Lower incidence of axial pain and neck stiffness in modified group (p=0.0019).

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Komagata et al (2004) III

Allocation undefined CSM (n=197), OPLL (n=108), expansive LMP (230 with foraminotomy)

Not reported C5 palsy Lower incidence of C5 palsy in concurrent foraminotomy (p<0.05).

Park et al (2012) III

Quasi-randomized CSM (n=45), OPLL (n=55) Mid-splitting (n=21) or unilateral single door (n=79) LMP

No difference in age, gender, preoperative JOA, diagnosis or surgical level.

Aggravated neck pain, C5 palsy, other complications

No significant differences in aggravated neck pain (p=0.34), C5 palsy (p=0.34) or other complications (p=1.00).


Segregated into two groups according to funicular section of the nuchal ligament on MRI. CSM (n=37) C6+7 (n=19), C7 preserving (n=18) C3-6 LMP**

No difference in age, gender, preoperative JOA, sagittal alignment or segmental alignment at C6/7.

Early and late axial pain†

No differences in axial neck pain (p>0.05).


Quasi-randomized CSM (n=36) C3-6 open-door LMP: preserved bilateral subaxial deep extensor muscles (n=18), non-preserved (n=18)

No difference in age, gender or preoperative JOA.

Early and late axial pain†, postoperative kyphosis

No difference in axial pain or postoperative kyphosis (p>0.05).

Tsuji et al (2007) III

CSM (n=64) Selective ELAP*† (n=42), C3-7 ELAP (n=22)

No difference in age, gender, preoperative JOA, stenotic level, C2-7 angle, ROM, number of expanded lamina

C5 palsy Incidence of C5 palsy was significantly lower in selective ELAP group than in C3-7 ELAP group (p=0.037).

Xia et al (2011) II

Randomized CSM (n=102) EOLP (n=57), narrow-open group (n=45)α

No difference in age, operative duration or estimated blood loss.

C5 palsy No significant differences in C5 palsy (p=0.17).

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Other

Caroom et al (2013) III

CSM (n=112) Vancomycin (n=40), control (n=72) Posterior decompression and instrumentation (n=112)

No difference in age, diabetes, preoperative severity, BMI, operative duration, estimated blood loss or number of instrumented levels.

Surgical site infection Higher incidence in control group (p=0.007).

Yoshida et al (2012) III

CSM (n=31), controls (n=30) ELAP C3-7 (n=31), lumbar decompression (n=30)

No difference in age, gender, ASA or operative duration. Blood loss (165 mL in LMP and 257 mL in lumbar decompression, p=0.0132).

Complications Higher incidence in LMP group (p=0.0183).

CSM: cervical spondylotic myelopathy; mJOA: modified Japanese Orthopaedic Association; ACDF: anterior cervical discectomy and fusion; LMF: laminectomy and fusion; NDI: Neck Disability Index; EQ-5D: EuroQol-5D; SF-36: short form-36; ASA: American Society of Anesthesiologists; BMI: body mass index; OPLL: ossification of the posterior longitudinal ligament; ASF: anterior spinal fusion; AP: anteroposterior; PCB: plate cage benezech; ROM: range of motion; CDH: cervical disc herniation; ACCF: anterior cervical corpectomy and fusion; CSF: cerebral spinal fluid; ELAP: expansive laminoplasty; EOLP: expansive open-door laminoplasty; FG: foraminotomy group; NFG: non-foraminotomy group ††Modified group: 1) bone grafts in the open gap were placed at three levels, 2) bone grafting in the hinged side was not performed, 3) the neck collar was worn for only 1 month and 4) patients were advised to perform early posterior neck muscle exercises. †Early axial pain: more than a week, less than a month; late axial pain: persisting for more than 1 month. **C6+7 group: funicular sections attaching to both C6 and C7 spinous process were preserved; C7 group: funicular section of the nuchal ligament attached only to the C7 spinous process was preserved (not attached to C6 process on MRI). *†In selective ELAP, open-door laminoplasty was performed only at stenotic levels; partial laminectomy of the upper half of lamina at the most inferior stenotic level was combined with laminoplasty. α: Wide group: the hinge trough was positioned at the inner margin of the lateral mass; narrow group: the hinge trough was positioned at one-third outside the lamina and 2-3mm more inward than the wide-open method.

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Table 3-21. Evaluation of Overall Body of Evidence using GRADE: Systematic Review C


UPGRADE: Large magnitude of effect (1 or 2 levels); dose response gradient (1 level); plausible confounding decreases magnitude of effect (1 level)

DOWNGRADE: Inconsistency of results (1 or 2 levels); indirectness of evidence (1 or 2 levels); imprecision of effect estimates (1 or 2 levels); risk of bias (1 or 2 levels); failure to specify subgroup analysis a priori (1 level); reporting bias (1 level)

Strength of evidence

Conclusions/Comments Baseline UPGRADE (levels)

DOWN-GRADE (levels)

KQ1: Are there clinical or imaging factors that can predict perioperative complications?

Age

HIGH LOW LOW INSUFFICIENT

Associated with perioperative complications: As reported by three studies (2 prospective, 1 retrospective), older patients are at a greater risk of perioperative complications

Associated with axial pain: A single retrospective study reported that older age decreased the risk of postoperative axial pain.

Not associated with upper extremity/C5 palsy: Two studies (1 prospective, 1 retrospective) reported no association between age and upper extremity/C5 palsy.

There is insufficient evidence from a single retrospective study that age predicts major intraoperative blood loss.

HIGH LOW HIGH LOW

Large effect size (+2)

Consistency unknown, imprecise (-2) Inconsistent, indirect (-2) Consistency/precision unknown (-2), indirect (-1)

Co-morbidities

LOW LOW-INSUFFICIENT

Not associated with perioperative complications: Two (prospective) out of three studies found no association between co-morbidities and perioperative complications.

There is insufficient evidence from a single retrospective study that diabetes (compared to no diabetes) is associated with cardiac complications and that uncontrolled diabetes (compared to controlled diabetes) is related to cardiac complications, hematomas and postoperative infection.

HIGH LOW

Large effect size (+1-2)

Inconsistent, precision unknown (-2) Consistency unknown, imprecise (-2)

Gender MODERATE INSUFFICIENT

Not associated with perioperative complications: As reported by two prospective studies, gender is not related with perioperative complications.

There is insufficient evidence from a single prospective study that gender predicts C5 palsy.

HIGH HIGH

Precision unknown (-1) Consistency/precision unknown, sparse (-3)

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INSUFFICIENT

There is insufficient evidence that gender predicts intraoperative blood loss (1 retrospective), and axial pain (1 retrospective).

LOW

Indirect (-1) and/or consistency unknown, imprecise or precision unknown (-2)

BMI Smoking Status Duration of

symptoms

LOW INSUFFICIENT

Not associated with perioperative complications: There is evidence from single prospective studies that BMI, duration of symptoms and smoking status are not related to perioperative complications.

There is insufficient evidence from a single retrospective study that duration of symptoms predicts axial pain.

HIGH LOW

Consistency/precision unknown (-2) Consistency unknown, imprecise (-2)

Baseline severity LOW INSUFFICIENT

Not associated with perioperative complications: A single prospective study reported no association between baseline severity and perioperative complications.

There is insufficient evidence that baseline severity predicts C5 palsy (1 prospective) and axial pain (1 retrospective).

HIGH LOW

Consistency/precision unknown (-2) Sparse (-1) and/or consistency unknown, imprecise (-2)

Diagnosis LOW Associated with upper extremity or C5 palsy: Two studies (1 prospective, 1 retrospective) reported an association between a diagnosis of OPLL and postoperative upper extremity or C5 palsy.

HIGH Large effect size (+2)

Inconsistent, indirect, imprecise/precision unknown, risk of bias (-4)

Size of OPLL Type of OPLL Preoperative

Alignment Preoperative ROM Cervical alignment Number of

compressed segments

Position of articular process

Cord inclination SI on T2-WI

INSUFFICIENT INSUFFICIENT INSUFFICENT

There is insufficient evidence from a single retrospective study that size of OPLL and type of OPLL are predictive of intraoperative blood loss.

There is insufficient evidence from a single retrospective study that preoperative alignment and ROM predict axial pain.

There is insufficient evidence from a single retrospective study that cervical alignment, number of compressed segments, position of the superior articular process, cord inclination and high intensity on T2-WI are predictive of C5 palsy.

LOW LOW HIGH

Consistency/precision unknown, indirect (-3) Consistency unknown, imprecise (-2) Consistency unknown, imprecise, sparse (-3)

Occupying ratio INSUFFICIENT

There is insufficient evidence from a single retrospective study that occupying ratio is predictive of upper extremity paresis.

LOW

Consistency unknown (-1)

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INSUFFICIENT There is insufficient evidence from a single retrospective study that occupying ratio is predictive of intraoperative blood loss.

LOW Large effect size (+1)

Consistency unknown, imprecise, indirect (-3)

KQ1: Are there surgical factors that can predict perioperative complications?

Number of levels MODERATE INSUFFICIENT INSUFFICIENT

Not associated with perioperative complications: Based on two prospective studies, the number of levels is not associated with perioperative complications.

There is insufficient evidence that number of opened lamina is predictive of C5 palsy.

There is insufficient evidence from a single retrospective study that long fusion predicts upper extremity paresis.

HIGH HIGH LOW

Precision unknown (-1) Consistency unknown, imprecise, sparse (-3) Consistency unknown, imprecise, indirect (-3)

Surgical approach MODERATE Not associated with perioperative complications: As reported by two prospective studies, surgical approach is not related to perioperative complications.

HIGH Precision unknown (-1)

Operative duration MODERATE LOW INSUFFICIENT

Associated with perioperative complications: Based on a single prospective study, a longer operative duration is associated with perioperative complications.

Not associated with major perioperative complications: As reported by a single prospective study, operative duration is not associated with major perioperative complications.

There is insufficient evidence from single retrospective studies that operative duration is predictive of axial pain and upper extremity paresis.

HIGH HIGH LOW

Consistency unknown (-1) Consistency/precision unknown (-2) Consistency unknown/imprecise (-2); indirect, consistency unknown (-2)

Number of stages HIGH LOW

Associated with major perioperative complications: Based on a single prospective study, a two stage procedure is a risk factor for major perioperative complication development.

Not associated with perioperative complications: Based on a single prospective study, number of stages is not related with perioperative complications.

HIGH HIGH

Large effect size (+2)

Consistency unknown, imprecise (-2) Consistency/precision unknown (-2)

Operative blood loss LOW

Not associated with perioperative complications: A single prospective study reported no association between operative blood loss and perioperative complications.

HIGH

Consistency/precision unknown (-2)

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INSUFFICIENT There is insufficient evidence from single retrospective studies that operative blood loss is predictive of axial pain and upper extremity paresis

LOW Consistency unknown (-1)

Laminoplasty, corpectomy

LOW Not associated with perioperative complications: A single prospective study reported no association between laminoplasty or corpectomy and perioperative complications.

HIGH Consistency/precision unknown (-2)

KQ2: Are there differences in rates of surgical complications between procedures?


INSUFFICIENT There is insufficient evidence from four studies (3 prospective, 1 retrospective) that complication rates differ between anterior and posterior surgery.

LOW Risk of bias (-1)

Anterior corpectomy versus Laminoplasty

INSUFFICIENT There is insufficient evidence from one retrospective study that complication rates differ between anterior corpectomy and laminoplasty.


Anterior decompression versus Laminoplasty

MODERATE INSUFFICIENT

Higher rates of neck pain in laminoplasty: A single prospective study reported a higher incidence of neck pain in laminoplasty compared to anterior spinal fusion.

There is insufficient evidence that rates of complications, C5 radiculopathy, bone graft complications, axial pain or postoperative kyphosis differ between anterior decompression and laminoplasty

HIGH LOW

Consistency unknown (-1) Risk of bias and/or consistency unknown/inconsistent (-1)

ACDF versus Bryan disc

MODERATE Higher rates of dysphagia in ACDF: A single RCT reported a higher rate of dysphagia in ACDF than in Bryan disc surgery.

HIGH Consistency unknown (-1)

ACCF versus ACDF INSUFFICIENT There is insufficient evidence from two retrospective studies that complication rates differ between ACCF and ACDF.

LOW Risk of bias and/or inconsistency (-1/-2)

Laminectomy and fusion versus Laminoplasty

INSUFFICIENT There is insufficient evidence from three studies (1 prospective, 2 retrospective) that complication rates differ between laminectomy with fusion and laminoplasty.

LOW Risk of bias and/or inconsistency (-1/-2)

Laminoplasty techniques

MODERATE LOW

No difference in rates of C5 palsy between wide and narrow open door laminoplasty: As reported by one RCT, hinge position does not affect rates of C5 palsy.

Lower incidence of C5 palsy in laminoplasty with concurrent foraminotomy: Two studies (1 prospective, 1 retrospective) reported a

HIGH LOW

Consistency unknown (-1)

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INSUFFICIENT INSUFFICIENT INSUFFICIENT

lower incidence of C5 palsy in a concurrent foraminotomy group compared to a non-foraminotomy group.

There is insufficient evidence from three studies (1 prospective, 1 retrospective, 1 case-control) that rates of axial pain and C5 palsy differ between C3-6 selective laminoplasty and C3-7 laminoplasty.

There is insufficient evidence from two prospective studies that rates of axial pain and postoperative kyphosis differ between muscle-preserving and muscle-disrupting groups.

There is insufficient evidence from single studies (2 prospective, 1 retrospective) that rates of C5 palsy differ between open-door and double door laminoplasty; rates of axial pain and neck stiffness differ between modified and original laminoplasty; and rates of aggravated neck pain, C5 palsy and other complications differ between mid-splitting and unilateral single-door laminoplasty.

LOW LOW LOW

Consistency unknown (-1) and/or risk of bias (-1) Consistency unknown, risk of bias (-2) Consistency unknown, risk of bias (-2)

Vancomycin INSUFFICENT There is insufficient evidence from a single study that rates of infection differ between a vancomycin and a control group.


BMI: body mass index; OPLL: ossification of the posterior longitudinal ligament; ROM: range of motion; SI on T2-WI: hyperintensity on T2-weighted MRI; ACDF: anterior cervical discectomy and fusion; ACCF: anterior cervical corpectomy and fusion; RCT: randomized control trial

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3.5.9 Discussion

This review summarizes important clinical and surgical predictors of post-operative

complications. This knowledge will allow clinicians to identify high-risk patients and institute

rigorous prevention strategies. Furthermore, surgeons can use this information to objectively

discuss surgical risks with their patients. This, together with outcome prediction, will enable

patients to make more informed decisions during the surgical-consent process and will also aid

in expectation management. Finally, from an economic standpoint, health care providers will be

able to appropriately allocate resources and predict future hospital utilization costs for each

surgical patient.

Literature discussing complication prediction is limited compared to outcome

prediction. In a review by Tetreault et al (2013), 91 studies reported associations between

clinical parameters and postoperative functional status or quality of life.215 In this current

review, only 36 studies evaluated the relationship between various clinical or surgical variables

and complications. Nine of these used multivariate analysis and controlled for important

confounders. It was therefore challenging to formulate strong evidence-based

recommendations due to limitations in study design, inconsistency and imprecision of results,

single study conclusions and risk of bias due to uncontrolled analyses.

Based on this review, age is a significant predictor of postoperative complications. This

is consistent with previous lumbar spine studies that have shown significant adverse events in

elderly patients following surgery.289, 290 As CSM is a progressive disease, older patients are

likely to have substantial degenerative pathology and, as a result, may require a more complex

surgery.115 In addition, the elderly can be less tolerant to surgery due to worse overall general

health status, co-morbidities and reduced physiological reserves. Clinicians must inform these

patients that they are at a higher risk of postoperative complications and are less likely to

achieve a favorable outcome.

There is controversy in the literature surrounding the predictive value of co-morbidities.

The number of co-morbidities was significantly associated with perioperative complications in a

study by Boakye et al (2008). This finding was based on a large retrospective analysis of 58,115

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patients undergoing spinal fusion at a single hospital. Furlan et al (2011), on the other hand,

reported no relationship between the number of ICD-9 codes and perioperative complications.

This insignificant multivariate association may be due to a potential interaction between older

age and number of co-morbidities.140 Patients with diabetes, particularly uncontrolled or type I,

are at a greater risk of perioperative complications and may require a longer length of stay and

more hospital resources.241 These findings are consistent with results from previous lumbar

spine studies that concluded diabetes is a significant risk factor of wound infections and non-

unions.291-294

With respect to surgical factors, a longer operative duration is associated with higher

rates of perioperative complications.115 Duration of surgery may be a surrogate for case

complexity. Therefore, we do not recommend surgeons speed up surgery but rather identify

complex cases, anticipate complications and plan accordingly. A two-stage anteroposterior

surgery is predictive of major complications; similarly, this factor may reflect greater

degenerative pathology and increased case complexity.115

The second objective of this review was to compare complication rates between various

surgical techniques. Based on the results of KQ1, studies had to control for age and operative

duration in order to be classified as good quality cohort studies. Unfortunately, the majority of

the literature included in this review did not adjust for these two confounders, preventing

accurate comparisons between intervention groups. Further research is required to determine

real differences in complication rates between anterior and posterior surgery, laminectomy

with fusion and laminoplasty and various laminoplasty techniques.

There was low to moderate evidence, however, suggesting differences in complication

rates between ACDF and laminoplasty, ACDF and Bryan’s disc, narrow and wide-hinge

laminoplasty and presence and absence of concurrent foraminotomy. Postoperative neck pain

is frequently encountered following laminoplasty and rates are significantly higher than in

anterior spinal fusion surgery.

Current hypotheses for intra- or post-operative C5 nerve root palsy include traumatic

surgical procedure, edema of the spinal cord or tethering of the root. Yonenobu et al (1991,

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1992) suggested that C5 palsy is more common following wide-open door laminoplasty due to

excessive backward shift of the spinal cord.295, 296 The hinge position determines how open the

door is and an inward shift of the hinge restricts excessive backward shift and overstretching of

the nerve roots.288 Although no patients in the narrow-group experienced C5 palsy and three

cases were seen in the wide group, this difference did not reach statistical significance. There

was a lower incidence of this complication following laminoplasty with concurrent

foraminotomy, suggesting that another potential cause of C5 palsy is existing C4/C5 foraminal

stenosis.282


Recommendation #1: While surgeons should not discriminate on the basis of age, they should

be informed that older patients are at a higher risk of complications. We therefore recommend

surgeons discuss these risks with their patients; plan and institute rigorous preventative

strategies; and closely monitor their patients in the perioperative period. Furthermore, health

care systems should anticipate higher associated costs and allocate resources accordingly.

Strength of Statement: Moderate

Recommendation #2: Longer operative duration and two-stage surgery are important

predictors of complications. However, both likely reflect substantial degenerative pathology

and increased case complexity. We therefore recommend surgeons identify these cases

preoperatively, anticipate complications and plan both preventative and postoperative

management strategies.

Strength of Statement: Moderate

Consensus Statement #1: We suggest that results from this study guide the development of

future complication prediction rules. Furthermore, we suggest that analyses in comparative

complication studies control for operative duration and age.

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Chapter 4: Surgeons’ Perceptions of Significant Predictors of Surgical

Outcome and Complications: Results from two Surveys of AOSpine

International

4.1 Introduction

This chapter summarizes the results from two surveys distributed to members of

AOSpine International. AOSpine International is a community of orthopaedic- or neuro-

surgeons, academics, researchers and other spine care professionals dedicated to knowledge

expansion, education development and innovative research in the field of spine to improve

patient care and outcomes. Members are professionals from six international regions: North

America, Asia Pacific, Europe, Africa, Middle East and Latin America.

The objective of these surveys was to address the following clinical questions:

Part A:

1. What are important clinical predictors of surgical outcome in patients with CSM?

2. Is MRI a valuable prognostic tool? If so, what are the most important imaging predictors

of surgical outcome in patients with CSM?

3. Do perceptions of important predictors of outcome vary from region to region?

Part B:

1. What are important clinical and imaging predictors of complications in patients treated

surgically for CSM?

2. What are important surgical predictors of complications in patients treated surgically for

CSM?

3. Do perceptions of important predictors of complications vary from region to region as a

result of differences in patient characteristics and surgical preferences?

This information will guide the construction of our clinical and complications prediction

models which will be used to predict functional outcomes and quantify risk of complications for

each surgical patient. In addition, by exploring regional differences in perceptions, we can

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identify international biases in CSM management and preferences of surgeons from different

regions. These differences will be valuable when interpreting findings from our external

validation study.

4.2 Overview of Common Methods

Two English language surveys were designed to ask spine professionals what they

believed were the most important predictors of surgical outcome and complications in patients

with CSM. An email request to participate in these surveys was sent to members of AOSpine

International with a cover letter outlining the objectives of these studies and an attached link to

Survey Monkey. This link was available electronically for 35-50 days, with reminders sent out

throughout this period.

Both surveys were designed to confirm the results of our systematic reviews and to

address key knowledge gaps in the literature. In addition, questions were developed based on

available data from the AOSpine CSM-North America and International studies as results from

these surveys will serve to validate our future clinical and complications prediction rules.

Finally, the questions were constructed to ascertain regional biases in opinions that may exist

due to international differences in patient characteristics and surgical preferences. Tables 4-1

and 4-2 summarize the surveys created for part A and part B, respectively.

Table 4-1. Survey Questions and Answer Options for Part A

1. CLINICAL FACTORS: Rank the following clinical factors from the most (1) to least (8) important in terms of their ability to predict surgical outcome. Options: Age Baseline Severity Score Gender Co-morbidities Duration of symptoms Signs Smoking status Symptoms

2. AGE: What is the threshold age above which there is a negative impact on surgical outcome? Options: 30, 40, 50, 60, 65 years, Other (please specify)

3. DURATION OF SYMPTOMS: What is the threshold duration of symptoms above which there is a negative impact on surgical outcome? Options: 1-3, 6, 12, 24 months, Other (please specify)

4. SMOKING STATUS: Is current smoking status important in predicting outcome? Options: YES, NO Is past smoking status important in predicting outcome? Options: YES, NO

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5. BASELINE SEVERITY SCORE: What is the threshold baseline mJOA above/ below which there is a negative impact on surgical outcome? Options: 0-18 points

6. CO-MORBIDITIES: Select the five most important co-morbidities in terms of their ability to predict surgical outcome. Options: Cardiovascular (myocardial infarct, angina/coronary artery disease, hypertension, peripheral arterial disease, arrhythmia, venous disease), Respiratory, Gastrointestinal (stomach, pancreas, hepatic), Renal, Endocrine (diabetes), Psychiatric, Rheumatologic, Neurologic (neuromuscular disorders, stroke, paralysis)

7. SIGNS: Select the two most important myelopathic signs in terms of their ability to predict surgical outcome. Options: Corticospinal motor deficits, Atrophy of intrinsic hand muscles, Hyperreflexia, Positive Hoffman sign, Upgoing plantar responses, Broad-based unstable gait

8. SYMPTOMS: Select the two most important symptoms of CSM in terms of their ability to predict surgical outcome. Options: Numb hands, Clumsy hands, Impaired gait, Bilateral arm paresthesiae, L’Hermitte’s phenomena, Muscular weakness in legs

9-11. MAGNETIC RESONANCE IMAGING FACTORS: Does Magnetic Resonance Imaging (MRI) provide prognostic information? Options: YES, NO Are cord properties (ex. Signal change) more important than canal measurements (ex. Transverse area) for predicting surgical outcome? Options: YES, NO Rank the following MRI factors from the most (1) to least (8) important in terms of their ability to predict of surgical outcome. Options: Transverse area Area of signal intensity on T2 High T2 signal intensity Height of signal intensity on T2 Low T1 signal intensity Number of compressed segments High T2/Low T1 Segmentation of T2 signal intensity

Table 4-2. Survey Questions and Answer Options for Part B

GENERAL 1: What type of complications do you see the most in your practice (select 2)? Options: Pseudoarthrosis, C5 radiculopathy, dysphagia, dural tear, axial pain, wound-related, postoperative kyphosis

GENERAL 2: What type of factors are the most important in predicting complications? Clinical factors (ex. Age, duration of symptoms, preoperative severity, co-morbidities) Imaging factors (ex. Transverse area, signal intensity) Surgical factors (ex. Approach, technique, operative time).

3. CLINICAL FACTORS: Rank the following clinical factors from the most (1) to least (6) important in terms of their ability to predict complications: Age Baseline Severity Score Gender Co-morbidities Duration of symptoms Smoking Status

4. CO-MORBIDITIES: A. Which of the following is the most important predictor of complications? Number of co-morbidities Type of co-morbidity (ex. Endocrine vs. cardiovascular) Severity of co-morbidity B. Select the five most important co-morbidities in terms of their ability to predict complications: Options: myocardial infarct, angina/coronary artery disease, hypertension, peripheral arterial disease, arrhythmia, venous disease, respiratory disease, stomach/intestine, pancreas, hepatic system, renal dysfunction, diabetes, psychiatric disorders, rheumatologic issues, neuromuscular disorders, stroke, paralysis.

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C. Are the following complications more prevalent in diabetic patients than in non-diabetic patients? Dysphagia Extensive bleeding Cardiac complications Wound infection C5 radiculopathy Respiratory complications Non-union

5. AGE: What is the threshold age above which there is an increased risk of complications? Options: 40, 50, 60, 65, other (please specify)

6. SMOKING STATUS: Does smoking lead to pseudoarthrosis or non-union?

7. IMAGING FACTORS: How important are the following imaging factors in predicting complications (1=not important, 10=very important) Transverse area Anteroposterior diameter Low signal intensity on T1-MRI High signal intensity on T2-MRI High signal change ratio Combined T1/T2 signal change Multilevel involvement

8. ANTERIOR VERSUS POSTERIOR: A. Are rates of complications different between anterior and posterior surgery? B. Select whether the following complications are more prevalent in anterior or posterior surgery or if there is no difference: Pseudoarthrosis C5 radiculopathy Dysphagia Dural tear Axial pain Wound-infection Instability Adjacent segment degeneration

9. 1-STAGE VERSUS 2-STAGE: Are rates of complications different between 1-stage and 2-stage surgery?

10. LAMINOPLASTY VERSUS LAMINECTOMY AND FUSION: A. Are rates of complications different between laminoplasty and laminectomy with fusion? B. Select whether the following complications are more prevalent in laminectomy with fusion or laminoplasty or if there is no difference: Pseudoarthrosis C5 radiculopathy Dysphagia Dural tear Axial pain Wound-infection Instability Adjacent segment degeneration

11. NUMBER OF LEVELS: What is the threshold number of operative levels above which there is an increased risk of complications: Options: 2, 3, 4+

12. FUSION VERSUS NON-FUSION: A. Are rates of complications different between fusion and non-fusion surgery? B. Select whether the following complications are more prevalent in fusion or non-fusion surgery or if there is no difference: Pseudoarthrosis Instrumentation migration Non-union Instability Axial pain Adjacent segment degeneration

The results from the ranking questions are summarized as means +/- standard

deviations and modes with percentages of that response. A mean score closer to one indicates

that the clinical or imaging factor has high predictive value based on professional opinion. The

results from the other questions are presented as frequencies or percentages of responses. To

assess variations in international perceptions, we separated the data into six geographic groups

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and ran similar analyses for each region. The results from Africa were not included in this sub-

analysis as a limited number of professionals from this continent participated in these surveys.

4.3 Results Part A: Important Clinical and Imaging Predictors of Surgical Outcome

4.3.1 Summary of Respondents

Six hundred and eighty nine members of AOSpine International completed the survey,

reflecting a response rate of 11.6% (689/5,934). The majority of respondents were either

neuro- (n=219), orthopaedic- (n=215) or spine- surgeons (n=171). Eighteen residents also

participated as well as two operating room nurses, two researchers, one neurologist, one pain

management specialist, one radiologist and one rheumatologist. Geographically, the greatest

number of participants were from Europe (n=204), followed by Asia Pacific (n=161), North

America (n=107), the Middle East (n=55), with the lowest representation from Africa (n=9)

(Figure 4-1).

Figure 4-1. Geographical Distribution of Survey Participants: Part A Purple: North America; yellow: Latin America; blue: Europe; pink: Middle East; forest green: Africa; green: Asia Pacific. The red flags represent the number of respondents from each country. Any country in Europe with fewer than 5 participants were not flagged but only colored (Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Hungary, Ireland, Israel, Latvia, Malta, Moldova, Netherlands, Norway, Romania, Serbia, Slovakia, Slovenia, Ukraine).

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4.3.2 Significant Clinical Predictors of Surgical Outcome

Table 4-3 displays the results from question 1 for the entire sample and for each

geographic region. Figure 4-2 illustrates the distribution of responses for each clinical factor.

Based on the distributions, it is evident that professionals believe duration of symptoms,

baseline severity score, signs and symptoms are more predictive than gender, smoking status

and the presence of co-morbidities. For age, 45% of the responses were 1-4, whereas 55% were

5-8, making it difficult to draw conclusions about this predictor. Duration of symptoms and

baseline severity were perceived as the most significant clinical predictors of outcome, with

mean rankings of 2.66±1.54 and 3.14±1.95, respectively. The mode for preoperative severity

was 1, with 28.2% of the sample selecting this answer. Although the mode for duration of

symptoms was 2, 26% of participants still ranked this factor as the most important predictor of

outcome. Symptoms and signs were also selected as having the greatest predictive value by

15.4% and 14.4% of the sample, respectively. Smoking status, gender and co-morbidities all had

low mean rankings and each had less than 5% choosing it as the number one predictor.

Figure 4-2. Distribution of Responses for each Clinical Factor The x-axis reflects the ranking number and the y-axis represents the number of responses.

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Compared to the entire sample, participants from Europe ranked signs as a significantly

more important predictor and smoking status as a significantly less important predictor of

surgical outcome. Duration of symptoms had the highest mean ranking across all geographies,

with the exception of North America where baseline severity score was ranked higher. The

mean ranking for baseline severity score was the second highest for Latin America, Middle East

and Asia. Europeans ranked signs as a more important clinical predictor than preoperative

severity.

Threshold age

Fifty-three percent of participants selected 65 years as the threshold age above which

there is a negative impact on surgical outcome (Table 4-4). Twenty-four percent chose 60 years

and 17% responded “other.” Of these, 29 participants specified 70 years, 36 said 75 years and

25 stated 85 years was the threshold age. Furthermore, 39 respondents declared that there is

no specific cut-off either because age is not a significant predictor of outcome or because

physiological age is far more important than chronological age.

There was international agreement that 65 years was the cut-off age, with proportions

of responses ranging from 0.42 in North America to 0.58 in Latin America. A higher proportion

(0.35) of North Americans answered “other,” with 65% (n=24/37) suggesting a limit between 70

and 85 years.

Threshold duration of symptoms

The majority of respondents answered that six months (n=241) or 12 months (n=238)

was the threshold duration of symptoms above which there is a negative impact on surgical

outcome (Table 4-4). Twenty percent selected 24-months.

There was international agreement that either six or 12 months was the cut-off duration

of symptoms, with proportions ranging from 0.24-0.42 and 0.31-0.42, respectively. Participants

from Latin America, however, significantly favored “12-months” over “six-months.” All other

geographical regions had approximately equal proportions selecting six- and 12-months.

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Table 4-3. Important Clinical Predictors of Surgical Outcome: Results for Entire Sample and each Geographic Region:

All Europe Asia Pacific Latin America North America Middle East Africa

Age Mean Mode (%) Number 1 (%)

N=677 4.40±1.84 5 (23.0) N=59 (8.7)

N=202 4.52±1.72 5 (24.2) N=14 (6.9)

N=159 4.35±2.00 5 (21.4) N=19 (11.9)

N=141 4.40±1.79 5 (24.1) N=6 (4.2)

N=106 4.26±1.81 5/6 (23.6) N=11 (10.4)

N=50 4.6±1.99 6 (26.0) N=6 (12.0)

N=9 3.33±1.80 1/3/5 (22.2) N=2 (22.2)

Gender Mean Mode (%) Number 1 (%)

N=677 7.01±1.78 8 (61.6) N=27 (4.0)

N=202 6.96±1.75 8 (56.4) N=8 (3.9)

N=159 7.17±1.63 8 (64.8) N=4 (2.5)

N=140 6.77±2.04 8 (58.6) N=9 (6.4)

N=106 7.52±1.19* 8 (77.4) N=1 (0.94)

N=51 6.67±1.91 8 (50.1) N=2 (3.9)

N=9 5.44±3.17 8 (44.4) N=2 (22.2)

Duration of Symptoms Mean Mode (%) Number 1 (%)

N=676 2.66±1.54 2 (28.2) N=176 (26.0)

N=201 2.58±1.52 2 (31.8) N=54 (26.9)

N=159 2.70±1.57 1 (27.0) N=43 (27.0)

N=141 2.66±1.50 1 (26.2) N=37 (26.2)

N=106 2.58±1.37 2 (37.7) N=22 (20.8)

N=50 2.64±1.68 1 (30.0) N=15 (30.0)

N=9 3.67±2.34 1/2/6 (22.2) N=2 (22.2)

Smoking Status Mean Mode (%) Number 1 (%)

N=677 6.18±1.56 7 (41.0) N=9 (1.3)

N=202 6.42±1.46* 7 (43.6) N=1 (0.50)

N=159 6.31±1.37 7 (40.2) N=3 (1.9)

N=140 5.91±1.81 7 (35) N=3 (2.1)

N=106 6.20±1.35 7 (50) N=1 (0.94)

N=51 5.80±1.60 7 (35.3) N=0 (0)

N=9 5.22±2.44 7 (44.4) N=0 (0)

Baseline Severity Mean Mode (%) Number 1 (%)

N=678 3.14±1.95 1 (28.2) N=191 (28.2)

N=202 3.39±1.93 4 (23.2) N=46 (22.8)

N=159 3.17±1.95 1 (25.2) N=40 (25.2)

N=141 3.28±2.07 1 (29) N=41 (29)

N=106 2.26±1.48 1 (44.34) N=47 (44.34)

N=51 3.22±2.07 1 (27.4) N=14 (28.4)

N=0 3.89±2.47 1/4 (22.2) N=2 (22.2)

Co-morbidities Mean Mode (%) Number 1 (%)

N=677 5.22±1.60 5 (25.7) N=14 (2.1)

N=202 5.28±1.65 6 (26.7) N=4 (2.0)

N=159 4.98±1.52 5 (30.2) N=3 (1.9)

N=140 5.47±1.58 6 (29.3) N=4 (2.8)

N=106 5.13±1.49 5 (29.2) N=2 (1.9)

N=51 5.33±1.80 5 (23.5) N=1 (2.0)

N=9 4.56±1.88 3/4/6 (22.2) N=0 (0)

Signs Mean Mode (%) Number 1 (%)

N=678 3.63±1.90 3 (20.5) N=98 (14.4)

N=202 3.48±1.78* 3 (22.3) N=27 (1.33)

N=159 3.76±1.96 2 (20.1) N=19 (11.9)

N=141 3.47±2.01 1 (19.8) N=28 (19.8)

N=106 3.88±1.92 3 (24.5) N=13 (12.3)

N=51 3.43±1.88 3 (23.5) N=9 (17.6)

N=9 4.89±1.76 4 (33.3) N=0 (0)

Symptoms Mean Mode (%) Number 1 (%)

677 3.75±2.09 2 (19.4) N=104 (15.4)

N=202 3.33±2.06 1 (23.8) N=48 (23.8)

N=159 3.55±2.04 2 (20.1) N=28 (17.6)

N=140 4.0±2.05 2 (20) N=13 (9.3)

N=106 4.17±2.02 4 (17.9) N=9 (8.5)

N=51 4.14±2.18 2/4 (21.6) N=4 (7.8)

N=9 5.0±2.4 5 (33.3) N=1 (11.1)

Mean ranking ± standard deviations are given in the second row. Frequencies and percentages of number 1 answers are provided in the fourth row. A * indicates that the mean for that region is significantly different than the mean from the entire sample

133

Smoking Status

Seventy-one percent of participants said that current smoking status was valuable in

predicting surgical outcome, whereas only 39% percent believed that past smoking status was

important (Table 4-4).

Although there was international agreement that current smoking status is an important

predictor, a more convincing proportion from the Middle East (0.85) answered “YES” to this

question. There was no consensus surrounding the value of past smoking status: participants

from North America and Europe had a significantly higher proportion of “NO” responses than

the entire sample, whereas professionals from Latin America suggested that past smoking does

carry prognostic value.

Threshold Baseline mJOA Score

Forty-one percent of participants chose 12 points as the threshold baseline mJOA

severity score below which there is a negative impact on surgical outcome (Table 4-4). The

second most frequently selected score was 10 points, which was chosen by 24% of the sample.

There was international agreement that a mJOA of 12 reflects the cut-off preoperative

severity score, with proportions ranging from 0.37-0.43. Although few participants chose the

“other” option, a significantly larger proportion (0.08) of Europeans answered this compared to

the entire sample, with the majority noting that they were either unaware of this scale or that it

was not commonly used in their practice.

Co-morbidities

Five hundred and thirty-eight professionals rated diabetes as an important co-morbidity

with respect to outcome prediction (Figure 4-3). Neuromuscular disorders, stroke and paralysis

were also frequently selected. Two-hundred and ninety-three respondents chose psychological

disorders and 266 agreed that rheumatologic issues were predictive. Diseases of the respiratory

system (n=211), renal dysfunction (n=182) and gastrointestinal diagnoses (n=52) were not

frequently ranked as important co-morbidities.

134

Table 4-4. Threshold Duration of Symptoms, Age and Baseline Severity Score and Smoking as a

Predictor

All Geographies

Comments for Other Regional Differences

Threshold Duration of Symptoms 1-3 months 6 months 12 months 24 months Other

N=684 (99.3%) N=48 (7.0%) N=241 (35.2%) N=238 (34.8%) N=140 (20.5%) N=17 (2.5%)

18 months (n=1) 5 years (n=2) >10 years (n=1) Unsure (n=4) None (n=5) Depends on severity (n=1) Severity/signs/symptoms are more important (n=3)

Asia Pacific: 42%/31% said 6/12 months Europe: 37%/34% said 6/12 months Latin America: 42% said 12 months* Middle East: 29%/31% said 6/12 months North America: 39%/33% said 6/12 months

Threshold Age 30 years 40 years 50 years 60 years 65 years Other

N=647 (94%) N=8 (1.2%) N=4 (0.6%) N=35 (5.4%) N=152 (23.5%) N=341 (52.7%) N=107 (16.6%)

70 years (n=29) 75 years (n=36) 80 years (n=25) 85 years (n=4) 90 years (n=1) Unsure (n=9) Physiological age is more important than chronological age/patient specific (n=6) None (n=33)

Asia Pacific: 46% said 65 years Europe: 54% said 65 years Latin America: 58% said 65 years Middle East: 48% said 65 years, 37% said 60 years* North America: 42% said 65 years, 35% said “other”* International Agreement

Threshold Severity Score (mJOA) 8 points 10 points 12 points 14 points 16 points Other

N=647 (94%) N=87 (13.5%) N=153 (23.7%) N=267 (41.3%) N=84 (13.0%) N=29 (4.5%) N=27 (4.0%)

0 points (n=1) 5 points (n=1) Unsure (n=13) None (n=2) Unfamiliar with scale (n=10)

Asia Pacific: 37% said 12 points and 30% said 10 points Europe: 43% said 12 points, 8% said “other”* Latin America: 43.4% said 12 points Middle East: 41% said 12 points North America: 43% said 12 points

Smoking Status Is smoking important? Is past smoking important?

N=682 (99%) Yes: 484 (71%) No: 198 (29%) N=683 (99%) No: 415 (61%) Yes: 268 (39%)

N/A Asia Pacific: Similar to entire sample. Europe: 72%* said past smoking status is unimportant. Latin America: 78%/60%* said current/past smoking is important. Middle East: 85%*/53% said current/past smoking is important. North America: 75%* said past smoking is unimportant.

mJOA: modified Japanese Orthopaedic Association. The frequency and percentages of responses are provided for the entire sample. Global differences are highlighted. A * indicates that the proportion of responses was significantly different for that region than for the entire sample.

Professionals from all regions agreed diabetes, psychological co-morbidities,

rheumatologic issues and neuromuscular disorders were important co-morbidities with respect

to outcome prediction. Paralysis was chosen by similar proportions from all regions, with the

exception of Latin America who chose this option less frequently. Latin Americans also believed

that renal dysfunction, respiratory disease and venous disease were more important co-

135

morbidities than the entire sample. The “top-five” lists from all geographies included diabetes

and neuromuscular disorders. Paralysis and stroke were not included in Latin America’s top five

but were instead replaced by respiratory disease and rheumatologic issues. Psychological

disorders were also included in the top five lists of all regions except for Europe which selected

rheumatologic co-morbidities at a greater proportion.

Figure 4-3. Important Co-Morbidities of Outcome Prediction The x-axis reflects the co-morbidity and the y-axis represents the frequency of responses. PAD: peripheral arterial disease.

Signs

There was a clear distinction between the three most and the three least important

signs (Figure 4-4). Fifty-five and 52% of the respondents selected atrophy of intrinsic hand

muscles and broad based unstable gait as the most important signs, respectively. Corticospinal

distribution motor deficits was chosen by 38% of the sample.

When exploring geographic variations, the two signs with the highest proportions of

responses were atrophy of intrinsic hand muscles and broad based unstable gait with

proportions ranging from 0.49-0.62 and 0.42-0.69, respectively. Participants from North

America had a significantly higher number of answers in favor of broad based unstable gait than

the other regions. Corticospinal motor deficit was selected the third most frequently by all

geographies except for the Middle East. Respondents from this region believed that a Positive

0

100

200

300

400

500

600

136

Hoffman sign was more predictive of a worse surgical outcome than corticospinal motor

deficits.

Figure 4-4. The Predictive Value of Myelopathic Signs

Symptoms

Similarly to signs, there were three common symptoms chosen by survey participants

(Figure 4-5). Seventy-six percent of the sample responded that impaired gait was the most

important sign. Clumsy hands and muscular weakness were each selected by 45%.

Figure 4-5. The Predictive Value of Myelopathic Symptoms

From all regions, impaired gait received the greatest number of responses, with a

significantly higher proportion of North Americans (0.86) choosing this sign. Participants from

North America also chose clumsy hands more frequently than the other regions and stated that

weakness was a less important sign. Europeans, on the other hand, suggested that weakness

was a more important sign than clumsy hands.

19%

27%

12%

10%

6%

26%

Total Population

Corticospinal DistributionMotor DeficitsAtrophy of Intrinsic HandMusclesHyperreflexia

Positive Hoffman Sign

Upgoing Plantar Responses

Broad Based Unstable Gait

5%

23%

38%

5%

7%

22%

Total Population

Numb Hands

Clumsy Hands

Impaired Gait

Bilateral Arm Paresthesiae

L'Hermitte's Phenomena

Muscular Weakness

137

4.3.3 Significant Imaging Predictors of Surgical Outcome

Eighty-six percent of participants agreed that MRI was a valuable prognostic tool. There

was international consensus, with percent of responses ranging from 83-90%. Eighty-three

percent of respondents believed cord properties were more important predictors of outcome

than canal dimensions. There was international agreement.

Important imaging predictors

Table 4-5 displays the results from this question for the entire sample and for each

geographic region. Figure 4-6 illustrates the distribution of responses for each imaging factor.

Presence of high SI on a T2WI and area of SI on a T2WI were deemed to be the most important

imaging factors, with mean rankings of 3.02±1.84 and 3.90±1.94, respectively. The mode for

high SI on T2WI was 1, with 27.7% of participants choosing this answer. A combined T1/T2

signal change also had a mode of 1 (19.2%), while transverse area and area of T2WI SI had

modes of 2. Segmentation and height of SI on T2WI were the least important imaging

predictors. There was international consensus that high SI on a T2WI was the most important

imaging predictor of outcome.

Figure 4-5. Distribution of Responses for each Clinical Factor The x-axis reflects the ranking number and the y-axis represents the number of responses. A score of 1 indicates the most important clinical factor.

138

Table 4-5. Important Imaging Predictors of Surgical Outcome: Results for Entire Sample and each Geographic Region

All Europe Asia Pacific Latin America North America Middle East Africa

Transverse Area Mean Mode (%) Number 1 (%)

N=665 4.07±2.14 2 (16.2) N=86 (12.9)

N=193 3.99±2.03 3/4 (17.6) N=24 (12.4)

N=155 4.25±2.27 5 (15.5) N=22 (14.2)

N=141 3.69±2.06 2 (17.7) N=24 (17.0)

N=107 4.44±2.19 4 (15.9) N=11 (10.3)

N=51 4.14±2.06 2 (25.5) N=3 (5.9)

N=9 5.89±1.54 4/5/6/7 (22.2) N=0 (0)

High T2 Signal Change Mean Mode (%) Number 1 (%)

N=665 3.02±1.84 1 (27.7) N=184 (27.7)

N=193 2.97±1.84 1 (31.6) N=61 (31.6

N=155 3.05±1.81 1 (25.2) N=39 (25.2)

N=141 2.79±1.82 1 (31.2) N=44 (31.2)

N=107 3.17±1.88 1 (26.2) N=28 (26.2)

N=51 3.29±2.00 1 (23.5) N=12 (23.5

N=9 2.89±1.69 2 (66.7) N=0 (0)

Low T1 Signal Change Mean Mode (%) Number 1 (%)

N=665 4.82±2.04 6 (15.6) N=32 (4.8)

N=193 4.86±1.86 5 (18.1) N=6 (3.1)

N=155 4.62±2.24 3 (18.1) N=15 (9.7)

N=141 4.86±1.92 4 (20.6) N=3 (2.1)

N=107 4.92±2.22 2 (17.8) N=4 (3.7)

N=51 5.06±2.02 5 (19.6) N=3 (5.9)

N=9 4.11±2.20 3 (44.4) N=0 (0)

T1/T2 Signal Change Mean Mode (%) Number 1 (%)

N=665 4.13±2.27 1 (19.2) N=128 (19.2)

N=193 4.51±2.17* 5 (18.1) N=21 (10.9)

N=155 4.23±2.35 1 (21.3) N=33 (21.3)

N=141 3.86±2.17 1 (19.1) N=27 (19.1)

N=107 3.77±2.35 1 (27.1) N=29 (27.1)

N=51 4.02±2.19 1 (21.6) N=11 (21.6)

N=9 2.56±2.65 1 (66.7) N=6 (66.7)

Area of Signal Change Mean Mode (%) Number 1 (%)

N=664 3.90±1.94 2 (17.9) N=87 (13.1)

N=193 3.84±2.07 2 (22.3) N=29 (15.0)

N=155 3.93±1.78 5 (19.4) N=13 (8.4)

N=141 3.98±1.92 5 (22.7) N=20 (14.2)

N=107 3.65±1.85 2 (20.6) N=16 (15.0)

N=50 4.02±2.10 2/3 (18.0) N=6 (12.0)

N=9 4.89±1.36 4 (55.6) N=0 (0)

Height of Signal Change Mean Mode (%) Number 1 (%)

N=665 5.12±1.98 7 (22.0) N=26 (15.2)

N=193 5.09±2.03 7 (26.4) N=5 (2.6)

N=155 5.17±1.96 6 (22.6) N=6 (3.9)

N=141 5.20±1.93 7 (24.8) N=5 (3.5)

N=107 5.05±1.93 6 (20.6) N=4 (3.7)

N=51 4.98±2.33 6 (21.5) N=6 (11.8)

N=9 5.44±1.24 6 (55.6) N=0 (0)

Number of Levels Mean Mode (%) Number 1 (%)

N=663 4.67±2.43 7 (24.7) N=101 (15.2)

N=192 4.26±2.55 7 (22.9) N=43 (22.4)

N=155 4.64±2.41 7 (23.9) N=22 (14.2)

N=141 5.04±2.33 7 (30.5) N=13 (9.2)

N=107 4.92±2.32 7 (21.5) N=12 (11.2)

N=50 4.46±2.49 7 (22.0) N=10 (20.0)

N=9 5.33±2.5 5 (33.3) N=1 (11.1)

Segmentation Mean Mode (%) Number 1 (%)

N=665 6.27±2.15 8 (49.8) N=21 (3.2)

N=193 6.45±2.05 8 (53.4) N=4 (2.1

N=155 6.10±2.21 8 (48.4) N=5 (3.2

N=141 6.58±1.99 8 (54.6) N=5 (3.5)

N=107 6.09±2.19 8 (43.0) N=3 (2.8)

N=51 5.98±2.20 8 (45.0) N=0 (0)

N=9 4.89±2.98 8 (33.3) N=2 (22.2)

Mean ranking ± standard deviations are given in the second row. Frequencies and percentages of number 1 answers are provided in the fourth row. A * indicates that the mean for that region is significantly different than the mean from the entire sample.

139

4.3.4 Discussion

Based on the opinions of spine care professionals, baseline severity score and duration

of symptoms are the most important predictors of surgical outcome in CSM patients. The

rationale behind these two findings is that both severe and chronic, longstanding compression

of the spinal cord may lead to irreversible histological damage such as myelomalacia,

spongiform changes, microcavitation and necrosis of the grey matter. 215 These findings are

consistent with the literature.

In our systematic review, there was controversy surrounding the predictive value of age.

In this survey, 45% of participants ranked age as 1-4, whereas 55% scored this predictor as 5-8.

This split makes it challenging to resolve the controversy in the literature and to draw

conclusions as to whether older patients have poorer surgical outcomes. Based on these

results, we believe surgeons and other spine care professionals should not discriminate on the

basis of age, especially when the patient is otherwise healthy and fit. Surgeons, however,

should be aware that the elderly may not be able to translate neurological recovery to

functional improvements as well as a younger population and should use this information to

appropriately manage their patients’ expectations.

The purpose of questions 2, 3 and 5 was to determine how to appropriately dichotomize

age, duration of symptoms and baseline severity score for future statistical analyses. There are

no concrete definitions for “old age”, “long duration of symptoms” or “severe myelopathy” to

outline how to separate these continuous variables into specific groups. Since the majority of

survey participants selected 65 as the threshold age, it is reasonable to define “old” as patients

over 65 and “young” as patients under 65 years. This threshold was used by Nagata et al.

(1996), Yamazaki et al. (2003) and Hirai et al. (1991) to split their sample into an elderly and

younger cohort.157, 222, 297 Other studies have arbitrarily used 70208, 298, 299 or 80180 years as the

cut-off value. Given that 17% of participants responded “other,” with a large proportion

specifying 70, 75 or 80 as the threshold, it may also be appropriate to divide the sample into

clusters as was done in a study by Tanaka et al. (1999): group A, 65-74; group B, 75-79; and

group C, ≥80 years.138

140

With respect to duration of symptoms, many previous studies have concluded that

patients with a disease duration greater than 12-months are more likely to have an unfavorable

outcome than those with a shorter duration.147, 149, 158, 297, 300, 301 In this survey, however, a

similar number of respondents chose 6-months and 12-months as the threshold duration of

symptoms above which there is a negative impact on outcome. Since there is no definite

consensus, we recommend dividing a patient population into five groups: <3 months, 3-6

months, 6-12 months, 12-24 months and >24 months.

Finally, professionals agreed that a mJOA of 12 was the threshold preoperative severity

below which there is a negative impact on surgical outcome. This score was defined as the cut-

off between moderate and severe patients by Kadanka et al. (2002)103 and Fehlings et al (2013).

However, since each mJOA score may represent a range of severities, especially at the milder

end of the scale, it may be more appropriate to keep this variable as continuous.

Although smoking status was not ranked highly by spine professionals, a large

proportion of the sample indicated that current smoking status is an important predictor of

outcome. Hilibrand et al. (2001) reported that in CSM patients undergoing multilevel

decompression with bone grafting, the rate of solid osseous union was higher in non-smokers

than smokers.302 The impact of smoking on healing, pseudoarthrosis and wound infections,

however, should be further explored to better define why smoking affects outcome. It is

possible that smoking is a surrogate for socioeconomic status, poorer dietary and lifestyle

choices, and reduced access to post-surgical care.

With respect to co-morbidities, professionals agreed the most important predictors

were diabetes, neurological disease including neuromuscular disorders, stroke and paralysis

and psychological issues. Diabetes has been previously reported on as a predictor by four

studies: two indicated that this co-morbidity is not significantly related to outcome,163, 205

whereas two other studies suggested a significant relationship with outcome,124, 303 With

respect to psychological co-morbidities, Kumar et al. (1999) found that patients in a “poor”

outcome group had greater emotional problems than those in a “good” outcome group.206

Kumar et al. (1999) also noted, however, that it was hard to draw conclusions based on patient-

141

generated outcome measures, like the SF-36. The results from this survey should prompt

further study to determine the prognostic value of diabetes, neurological and psychological co-

morbidities.

The most important signs and symptoms, as ranked by the professionals, were related

to gait dysfunction (broad-based unstable gait, impairment of gait and weakness) or hand

clumsiness (atrophy of intrinsic hand muscles, clumsy hands and weakness). This is consistent

with the findings from the literature.149, 166, 201, 304

The presence of a high SI on T2WI was believed to be the most important imaging

predictor. Although the sole presence or absence of a T2 signal change is insignificantly

associated with outcome, a high signal intensity ratio132, 227-229 or a high signal grade133, 212, 224 on

these images have been reported as valuable negative predictors. These characteristics may

define the amount of irreversible histological damage to the spinal cord and therefore may

indicate recuperative potential.

4.4 Results Part B: Important Clinical and Surgical Predictors of Complications

4.4.1 Summary of Respondents

Nine-hundred and sixteen members of AOSpine International completed this survey,

representing a response rate of 15.4% (916/5,934). Geographically, the greatest number of

participants were from Europe (n=263, 28.17%), followed by Asia Pacific (n=227, 24.78%), Latin

America (n=204, 22.27%), North America (n=112, 12.23%), the Middle East (n=89, 9.72%) with

the lowest representation from Africa (n=15, 1.64%). Figure 4-7 displays the geographical

distribution of respondents.

4.4.2 Complications commonly seen in clinical practice

The first question asked clinicians to choose the two most common complications they

see in their surgical practice. The provided options were pseudoarthrosis, C5 radiculopathy,

dysphagia, dural tear, axial pain, wound infection and postoperative kyphosis. For the purpose

of this question, participants who chose one option or more than two were excluded, leaving a

total of 666 answers to analyze. Axial pain (59.01%) and dysphagia (50.30%) were the two

142

complications seen the most frequently in practice. These findings were consistent across all

regions except for Asia Pacific where C5 radiculopathy was more commonly encountered

(40.48%) than dysphagia (38.10%).

Figure 4-7. Geographical Distribution of Survey Participants: Part B Purple: North America; yellow: Latin America; orange: Europe; pink: Africa; blue: Middle East: Asia Pacific. The red flags represent the number of respondents from each country. Any country in Europe with fewer than 5 participants were not flagged but only colored (Austria, Bosnia and Herzegovina, Bulgaria, Croatia, Denmark, Estonia, Hungary, Ireland, Latvia, Malta, Moldova, Montenegro, Norway, Serbia, Slovakia, Slovenia, Sweden and Ukraine). Barbados (n=1) and Jamaica (n=1) are not on the map.

Other regional differences worth highlighting include 1) C5 radiculopathy was more

frequently seen by surgeons from Asia Pacific (40.48%) than those from North America

(24.18%), Europe (24.47%) and Latin America (28.77%); 2) the percentage of participants that

chose dysphagia was the highest in North America (67.03%) and the lowest in Asia Pacific

(38.10%); 3) no professionals from North America selected dural tear as one of the two most

common complications; and 4) postoperative kyphosis was more common in the Middle East

(21.67%) and Europe (15.96%) than in North America (6.59%). (Figure 4-8).

143

4.4.3 Factors predicting complications

According to sixty-six percent of participants, clinical factors are the most important in

predicting complications. Twenty six percent chose surgical factors and a very small proportion

selected imaging factors (8%).

Figure 4-8. Frequently Seen Surgical Complications across Six Geographic Regions The top bar (black) represents the results from the entire sample (n=666). The other five bars reflect responses of professionals from (top to bottom) North America, Asia Pacific, Europe, Latin America and the Middle East. Differences may reflect variations in definitions of complications across centers or surgical preferences.

4.4.4 Significant Clinical Predictors of Complications

Participants were asked to rank age, co-morbidities, gender, smoking status, baseline

severity score and duration of symptoms in terms of their ability to predict complications.

Figure 4-9 shows the distribution of responses for the entire sample. Table 4-6 displays the

mean ranking and mode for each clinical factor for the entire sample and by geographic region.

The presence of co-morbidities and baseline severity score were the two most important

predictors of complications, with mean rankings of 2.45±1.34 and 2.81±1.54, respectively, and

modes of “1”. Gender and smoking status were relatively unimportant predictors as displayed

by higher mean rankings and modes of 5 (34.37%) and 6 (64.75%), respectively. Although age

was scored as the third most important predictor, 59% of the responses were either 1, 2 or 3

16.22

30.03

50.3

6.91

59.01

24.17

13.36

0 10 20 30 40 50 60 70 80

Pseudoarthrosis

C5 Radiculopathy

Dysphagia

Dural Tear

Axial Pain

Wound-infection

Postoperative Kyphosis

Overall (n=666) North America (n=91) Asia Pacific (n=168)

Europe (n=188) Latin America (n=146) Middle East (n=60)

144

while 41% were 4, 5 or 6, making it difficult to draw definite conclusions about its predictive

value.

The presence of co-morbidities was the most important clinical predictor of

complications across all regions except Asia Pacific, where baseline severity score was ranked as

more important (2.45±1.44). The mean ranking for baseline severity score was second in North

America (2.85±1.38), Europe (2.94±1.59) and the Middle East (2.68±1.54), whereas age was

ranked second in Latin America (3.02±1.45). The mode for baseline severity score was 1 across

all regions except for North America, where a greater percentage of participants selected 3

(25.00%). The mean rankings for age did not vary drastically between regions: 3.02±1.45 in

Latin American to 3.17±1.39 in Europe. Duration of symptoms was scored as the third most

important predictor in Asia Pacific (2.98±1.54), Europe (3.10±1.58), and the Middle East

(2.70±1.46) but was ranked as less important by professionals from North America (3.77±1.39,

mode 5). With respect to smoking status, although the mode was 5 across all regions, the mean

ranking in North America (3.76±1.38) and Latin America (3.80±1.42) were lower than in Asia

Pacific (4.54±1.17), Europe (4.25±1.43) and the Middle East (4.46±1.28). Gender was

consistently the least important predictor of complications (5.15±1.29 to 5.58±0.98).

Figure 4-9. Important Clinical Predictors of Postoperative Complications The red bars indicate the five co-morbidities that professionals agreed were the most important predictors of complications.

145

Co-morbidities

Sixty percent of respondents chose severity of co-morbidity, 28% said type of co-

morbidity and 12% selected number of co-morbidities as the most important co-morbidity

predictor of complications. This trend was consistent across all regions.

Table 4-6. Important Clinical Predictors of Postoperative Complications: Results for Entire Sample and each Geographic Region:

Overall (n=902)

North America (n=112)

Asia Pacific (n=222)

Europe (n=259)

Latin America (n=204)

Middle East (n=84)

Age

3.08±1.40 4 (22.06%)

3.07±1.44 2 (25.00%)

3.01±1.32 3 (28.38%)

3.17±1.39 4 (23.94%)

3.02±1.45 2 (22.55%)

3.10±1.51 4 (27.38%)

Co-morbidities

2.45±1.34* 1 (34.04%)

1.97±1.18† 1 (48.21%)

2.62±1.32 1 (27.93%)

2.38±1.32 1 (36.29%)

2.46±1.41 1 (33.82%)

2.67±1.24 3 (34.52%)

Gender 5.29±1.21* 6 (64.75%)

5.58±0.98† 6 (78.57%)

5.39±1.11 6 (67.12%)

5.15±1.29 6 (57.53%)

5.16±1.37 6 (64.71%)

5.39±0.98 6 (61.90%)

Smoking status

4.18±1.38* 5 (34.37%)

3.76±1.38 5 (36.61%)

4.54±1.17 5 (39.19%)

4.25±1.43 5 (32.05%)

3.80±1.42 5 (28.92%)

4.46±1.28 5 (38.10%)

Baseline severity

2.81±1.54* 1 (27.27%)

2.85±1.38 3 (25.00%)

2.45±1.44† 1 (35.59%)

2.94±1.59 1 (24.71%)

3.08±1.62† 1 (23.53%)

2.68±1.54 1 (30.95%)

Duration of symptoms

3.20±1.57* 2 (26.16%)

3.77±1.39† 5 (25.00%)

2.98±1.54† 2 (29.28%)

3.10±1.58 2 (23.94%)

3.47±1.63† 2 (24.02%)

2.70±1.46† 2 (40.48%)

Mean ranking ± standard deviations are presented in the first row. Modes (% of response) are provided in the second row. A * indicates that this mean is significantly higher or lower (p<0.05) than what is expected from chance alone. A † reflects that the mean ranking for this region is significantly higher or lower (p<0.05) than the mean for the overall sample.

Professionals were asked to choose the five most important co-morbidities with respect

to their ability to predict complications (Figure 4-10). For the purpose of this question,

participants who selected less than or more than five choices were excluded, leaving a total of

806 answers to analyze. The most frequently selected co-morbidity was diabetes which was

chosen by 83.13% of the sample. Respiratory disorders (59.43%), angina/coronary artery

disease (42.43%), rheumatologic issues (42.18%) and myocardial infarct (39.95%) were the next

most important co-morbidities. On the other hand, diseases of the pancreas (0.5%), stomach or

intestine (2.11%) and liver (14.27%) do not increase the risk of complications. In addition,

specific cardiovascular diseases such as venous disease (9.93%), arrhythmia (12.03%),

hypertension (18.36%) and peripheral arterial disease (20.33%) were rated as less important

predictors.

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Diabetes (77.09% in Europe to 87.94% in Asia Pacific) and respiratory disorders (40% in Middle

East to 66.12% in Latin America) were rated as important predictors of complications across all regions.

All regions also included angina/coronary artery disease in their top-five lists except for the Middle East

where more participants chose myocardial infarct (44%). Forty-six percent of respondents from North

America and Asia Pacific suggested that patients with renal dysfunction are at an increased risk of

surgical complications. Rheumatologic and neuromuscular disorders were included in the top-five lists of

European and Middle Eastern respondents.

Figure 4-10. Co-morbidities Professionals agree Increase the Risk of Postoperative Complications The red bars indicate the five co-morbidities that professionals agreed were the most important predictors of complications.

Given the predictive value of diabetes, a secondary question was developed to

determine which specific complications are more prevalent in diabetics. Based on Figure 4-11,

professionals agreed that patients with diabetes are more likely to experience cardiac

complications (69.75%) and wound infections (95.07%). Fifty-eight percent of the sample also

said that non-unions are more common in diabetic patients. In contrast, respondents believed

that complications such as extensive bleeding (84.66%), C5 radiculopathy (71.95%) and

dysphagia (80.31%) are equally likely in patients with and without diabetes. A less convincing

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portion of the sample (62.5%) stated that rates of respiratory complications are similar

between patient groups.

Participants from all regions agreed that patients with diabetes are at a greater risk of

wound infections postoperatively (92.34%-98.2%). The majority of respondents from each

region also believed that diabetics are at a higher risk of cardiac complications, although there

was a greater range of percentages (60% in Latin America to 82.41% in North America).

Compared to other regions, a significantly larger percent of professionals from the Middle East

(44%) said that C5 radiculopathy was more common in patients with diabetes. Finally, there

were conflicting responses with respect to non-unions: 63% of North Americans said this

complication was equally common in patients with and without diabetes, whereas 62% and

68% of Latin Americans and Middle Easterns believed non-unions were more prevalent in

diabetics.

Figure 4-11. Differences in Specific Complications between Diabetic and Non-diabetic Patients

Threshold Age

Fifty percent of participants selected 65 as the threshold age above which there is an

increased risk of postoperative complications. Twenty percent chose 60 years and 13% believed

that there was no threshold. Finally, 11% responded “other,” with the majority specifying either

70 (37%), 75 (42%) or 80 (14%) years. Six percent, however, either specified that physiological

69.75

95.07

57.55

80.31

84.66

71.95

62.5

0 10 20 30 40 50 60 70 80 90 100

Dysphagia (n=828)

Extensive Bleeding (n=828)

Cardiac Complications (n=833)

Wound Infections (n=892)

C5 Radiculopathy (n=827)

Respiratory Complications (n=832)

Non-union (n=841)

No Yes

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age was more important than chronological age or that the threshold was dependent on

disease severity and presence of co-morbidities.

Although there were slight variations in percentages, there was international agreement

that 65 years was the threshold age above which patients have a greater risk of experiencing

complications.

Smoking Status

Eighty-one percent of the sample said that smoking results in pseudoarthrosis or non-

union. The majority of participants from each region answered “yes,” although there was a

wide range of percentages (94% in North America and 68% in Europe).

4.4.5 Significant Imaging Predictors of Complications

Question 7 asked participants to rank, from 1 to 10, how important certain imaging

factors are in predicting complications. As shown in figure 6, the results are very challenging to

interpret. The modes for transverse area, anteroposterior diameter, high signal intensity on

T2WI and combined signal change on T1/T2-WI were 10, although less than 20% of the sample

chose this answer. A more significant finding was that 42% of respondents said that multilevel

involvement is very important for predicting complications. (Figure 4-12).

Figure 4-12. Important Imaging Predictors of Postoperative Complications

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4.4.6 Significant Surgical Predictors of Complications


Participants were asked whether complications are more prevalent following anterior or

posterior surgery or if there is no difference between the two approaches. Forty percent

responded posterior, 29% anterior and 30% said rates are the same. Perceptions varied across

regions: 45% of Europeans and North Americans chose posterior, while only 33% of

respondents from Asia Pacific selected this option. In fact, a larger percentage of professionals

from Asia Pacific argued complications rates were higher following anterior surgery (37%)

(Table 4-7).

Table 4-7. Complication Rates in Anterior vs. Posterior Surgery

Anterior Posterior Same

Overall (n=899) 29.48%* 40.38%* 30.14%*

North America (n=110) 18.18%† 45.45% 36.36%

Asia Pacific (n=222) 36.94%† 33.33%† 29.73%

Europe (n=259) 23.94%† 45.17% 30.89%

Latin America (n=202) 32.67% 38.61% 28.71%

Middle East (n=85) 35.29% 37.65% 27.06%

A * indicates that this percentage is significantly higher or lower (p<0.05) than what is expected from chance alone. A † reflects that the percentage of responses for this region is significantly higher or lower (p<0.05) than the percentage for the overall sample.

Professionals agreed that the types of complications seen in posterior and anterior

surgery are very different: 1) dysphagia (97%) and adjacent segment degeneration (57%) are

more common following anterior surgery; and 2) wound infections (71.48%), axial pain

(64.57%), C5 radiculopathy (59.42%), dural tear (51.85%) and instability (50.9%) are associated

with the posterior approach. There was no consensus whether rates of pseudoarthrosis differ

between anterior (40%) and posterior surgery (36%) (Figure 4-13).

1-Stage versus 2-Stage Surgery

Fifty six percent of respondents said that complications are more prevalent in a 2-stage

anteroposterior surgery than in a single stage anterior or posterior procedure. There was

international agreement (Table 4-8).

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Figure 4-13. Differences in Specific Complications between Anterior and Posterior Surgery Error bars represent 95% confidence intervals of the proportion of response. Overlapping error bars indicate proportions are not significantly different from one another. All proportions were significantly different (p<0.05) than what is expected from chance alone except for “same” for adjacent segment degeneration and instability and “posterior” for pseudoarthrosis.

Table 4-8. Complication Rates in 1-Stage vs. 2-Stage Surgery

1-stage 2-stage Same

Overall (n=886) 18.96%* 55.76%* 25.28%*

North America (n=111) 11.71% 61.26% 27.03%

Asia Pacific (n=217) 29.03%† 47.00%† 23.96%

Europe (n=253) 14.62% 56.52% 28.85%

Latin America (n=199) 16.08% 61.81% 22.11%

Middle East (n=86) 18.60% 56.98% 24.42%


Laminectomy and Fusion versus Laminoplasty

Respondents were asked whether complications are more prevalent following

laminectomy and fusion or laminoplasty surgery or if rates are similar between these posterior

techniques. Forty-four percent selected laminectomy and fusion, 20% laminoplasty and 27%

said rates are the same. Perceptions varied across regions: 45% and 43% of surgeons from

North America and Europe, respectively, said there was no difference in complication rates

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between laminectomy with fusion and laminoplasty. Fifty-two percent of participants from Asia

Pacific, however, said patients undergoing laminectomy with fusion are at a higher risk of

complications than those treated by laminoplasty (Table 4-9).

Table 4-9. Complication Rates between Laminectomy with Fusion and Laminoplasty

Laminectomy and Fusion

Laminoplasty Same

Overall (n=895) 43.69%* 19.66%* 36.65%

North America (n=111) 32.43%† 22.52% 45.05%

Asia Pacific (n=222) 52.25%† 13.51%† 34.23%

Europe (n=256) 37.89% 19.53% 42.58%†

Latin America (n=198) 44.44% 26.26%† 29.29%†

Middle East (n=87) 52.87% 17.24% 29.89%


With respect to specific complications, professionals believed that 1) instability is more

common following laminoplasty (59.89%); 2) pseudoarthrosis is more prevalent after

laminectomy and fusion (51.55%); and 3) there is no difference in rates of dysphagia (86.07%),

wound infections (71.62%), dural tear (60.34%) or C5 radiculopathy (52.23%) between patients

treated with laminoplasty versus laminectomy with fusion. Fifty percent of participants agreed

adjacent segment degeneration is more common in laminectomy and fusion, whereas 40% said

there is no difference between techniques. Forty-five percent of professionals reported no

difference in rates of axial pain between laminectomy with fusion and laminoplasty. However,

36% argued this complication is higher in laminoplasty (Figure 4-14).

Number of Levels

Fifty-six percent of participants selected three as the threshold number of operative

levels above which there is an increased risk of complications. Thirty-four percent chose greater

than four levels while only 10% selected two as the threshold. There was international

agreement.

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Figure 4-14. Differences in Specific Complications between Laminectomy with Fusion and Laminoplasty Error bars represent 95% confidence intervals of the proportion of response. Overlapping error bars indicate proportions are not significantly different from one another. All proportions were significantly different (p<0.05) than what is expected from chance alone.

Fusion versus Non-fusion

Participants were asked whether complications are more common following fusion or

non-fusion surgery or if rates were similar between procedures. Thirty-seven percent of the

entire sample selected fusion, 36% non-fusion and 27% said rates are the same. Fifty-one

percent and 48% of respondents from North America and Asia Pacific, respectively, said

complications are more common in fusion surgery. Fifty-one percent of participants from the

Middle East believed non-fusion is associated with a higher risk of complications (Table 4-10).

Table 4-10. Complication Rates between Fusion and Non-Fusion Surgery

Fusion Non-Fusion Same

Overall (n=896) 37.17%* 35.83% 27.01%*

North America (n=111) 51.35%† 18.02%† 30.63%

Asia Pacific (n=222) 47.75%† 30.63% 21.62%

Europe (n=256) 27.73%† 39.06% 33.20%†

Latin America (n=199) 31.66% 41.21% 27.14%

Middle East (n=87) 32.18% 50.57%† 17.24%†


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Respondents agreed that the types of complications seen in fusion compared to non-

fusion surgery are very different: 1) instability (80.9%) and axial pain (50.85%) are more

commonly seen in surgery without fusion; and 2) adjacent segment degeneration (65.99%),

instrumentation migration (57.16%), non-union (51.81%) and pseudoarthrosis (55.77%) are

associated with fusion surgery (Figure 8).

Figure 4-15. Differences in Specific Complications between Fusion and Non-Fusion Surgery Error bars represent 95% confidence intervals of the proportion of response. Overlapping error bars indicate proportions are not significantly different from one another. All proportions were significantly different (p<0.05) than what is expected from chance alone except for “non-fusion” for pseudoarthrosis and instability.

4.4.7 Discussion

This study investigates what spine professionals perceive as the most important clinical

and surgical predictors of complications in patients with CSM. Our findings provide insight on

international surgical preferences and how clinicians worldwide anticipate complications. This

knowledge will be used to guide the development of our preliminary complications prediction

model that can quantify a patient’s risk of complications and provide surgeons with a tool to

accurately convey this information to their patients during the surgical consent discussion.

Furthermore, surgeons should be encouraged to design case-specific preventive strategies for

their high-risk patients and ensure adequate monitoring during the perioperative period.

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It is important to compare our survey results with existing evidence to identify where

there may be gaps between professional opinion and findings in the literature. There are

certain limitations in the methodology of previous complication studies that may account for

discrepancies between surgeons’ perceptions and results from primary research. These include

1) complication rates in prospective studies are generally higher than in retrospective studies;

2) studies where complications are recorded by surgeons have lower rates than studies where

research coordinators are responsible for data collection; and 3) it is unclear what actually

constitutes a complication and how to distinguish between minor and major events. In the

study by Fehlings et al (2013), all adverse events were collected throughout the study period

and then adjudicated by a central panel of investigators as either related to CSM, related to

surgery or unrelated.115 This likely represents the most consistent, unbiased and

comprehensive method of identifying complications in this surgical cohort. However, the

heterogeneity of complications and regional variations in definitions may still affect data

collection and reporting. In the absence of strong literature on this subject, assessment of

professional opinion provides an effective means to determine, discover and evaluate

predictors of complications.

Clinical Predictors

Based on our survey, spine professionals agreed the most important clinical predictor of

complications is the presence of co-morbidities, with severity being more relevant than number

and type of disease. The predictive value of co-morbidities has been assessed by three previous

studies.115, 140, 305 According to Boakye et al (2008), patients with three or more co-morbidities

are twice (95% C.I.: 1.59-2.48) as likely to experience a complication following spinal fusion than

healthy individuals.305 Although a significant finding, this analysis only examined number of co-

morbidities and not severity or type of disease. In a second study, Fehlings et al (2012)

developed a scoring system that summated severity (mild=1, moderate=2, severe=3) across

eight co-morbidity categories, including cardiovascular, neurological and psychiatric

disorders.115 This study found no significant difference in the co-morbidity score of patients

who did and did not suffer a major or minor perioperative complication. In a final study, the

Charlson co-morbidity index was reported as an insignificant predictor of complications.140 Our

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survey results identify a clear gap between surgeons’ perceptions of predictors and existing

evidence on this topic. Interestingly, the retrospective and prospective studies reported

different associations between co-morbidities and complications.

The diabetes question was designed based on results from previous lumbar spine

studies and a single study by Cook et al (2008) in CSM patients.241, 291-294 According to our

survey, professionals agreed that patients with diabetes are at a higher risk of cardiac

complications and wound infections. These results are consistent with the literature: diabetic

patients are 1.57 (95% C.I.: 1.14-2.16) times more likely to experience complications than

healthy individuals and those with uncontrolled disease are at a 7.46 (95% C.I.: 1.33-47.79)

times greater risk of postoperative infection than patients with controlled diabetes.241

Therefore, the association between diabetes and complications is likely dependent on how well

this co-morbidity is managed; patients with poor glycemic control may be at a higher risk. In

lumbar spine surgery, non-unions were more frequent in patients with diabetes.291 Although

not a convincing majority, 58% of professionals argued that diabetics have higher rates of non-

unions following surgery for CSM.

Age was ranked as the third most important predictor of complications. However, a

score of 3.08±1.40 is not significantly different than what is expected from chance alone.

According to existing literature (two prospective, 1 retrospective study), age is associated with

postoperative complications.115, 140, 305 The rationale behind this finding is that older patients 1)

are more likely to have substantial degenerative pathology and, as such, will require a more

complex surgery; 2) typically have an increased number of co-morbidities; 3) are less tolerant to

surgery; and 4) have reduced physiological reserves to face physical assault.306 In general, older

age is associated with an increased risk of complications; however clinicians must consider a

patient’s physiological age and co-morbidities when evaluating this risk.

Baseline severity score was ranked as an important clinical predictor of complications.307

This finding is inconsistent with results from the prospective AOSpine North American

complications study that reported no association between baseline mJOA score and minor or

major perioperative complications.115 In contrast, baseline severity score is one of the most

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significant predictors of functional status and neurological outcomes following surgery.308 As a

result, surgeons may perceive myelopathy severity as a significant predictor of complications.

However, there is insufficient evidence in the literature supporting this consensus.

Finally, the majority of surgeons agreed that smoking negatively impacts the healing

process and leads to pseudoarthrosis or non-union. This finding is consistent with results from a

single study by Hilibrand et al (2001): in patients with CSM undergoing multilevel

decompression and interbody grafting, the rate of solid osseous union was higher in

nonsmokers than smokers.302 However, in the AOSpine North America complications study,

there was no difference in rates of complications between smokers and non-smokers.115

Further research is required to evaluate whether smoking is indeed a predictor of

complications.

Surgical Predictors

This survey also aimed to identify significant surgical predictors of complications and

determine relative risks of various approaches and techniques. Patients with CSM may be

treated anteriorly or posteriorly or may undergo a 2-stage circumferential surgery. The

approach chosen by the attending surgeon is dependent on location of compressive forces,

extent of degeneration, age, sagittal alignment, presence of radiculopathy or axial pain and

surgeon’s familiarity with technique.106 With respect to complications, 40% of participants

agreed that complications are more frequent following posterior surgery than anterior surgery.

These results were inconclusive as 30% of professionals specified that rates of complications

are similar in anterior and posterior surgery. It is unlikely that respondents considered

variations in age, extent of pathology and myelopathy severity between approach groups when

selecting their answer. In previous studies, Fehlings et al (2012, 2013) and Ghogawala et al

(2011) reported similar complication rates between patients treated anteriorly and those

treated posteriorly.107, 115, 268 However, these analyses did not necessarily control for significant

confounders (e.g. age and operative duration) and, as a result, comparisons between approach

groups are subject to bias.

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In our survey, there was agreement that types of complications significantly differ

between surgical approaches. For example, surgeons specified that rates of dysphagia and

adjacent segment degeneration are higher in anterior surgery and wound infections, axial pain,

C5 radiculopathy, dural tears and instability are more common following posterior surgery.

Based on literature findings, there are no significant differences between approaches with

respect to rates of new neurological deficits, C5 radiculopathy or surgical, medical and

irreversible complications.107, 115, 268, 309 In a study by Kristof et al (2009), patients treated by

anterior corpectomy were more likely to experience dysphagia than patients treated by

posterior laminectomy and fusion.309 This finding could not be confirmed by the AOSpine North

America prospective complications study conducted by Fehlings et al (2012); however, wound

infections occurred at a higher frequency in the posterior group (4.7%) than in the anterior

group (0.6%). Evidently, there is little consensus in the literature as to differences in rates of

complications and types of complications between anterior and posterior procedures.

There was consensus that 2-stage circumferential surgery is associated with a higher risk

of complications than either a single-stage anterior or posterior surgery. This conclusion is

supported by Fehlings et al (2012): patients undergoing a combined anteroposterior operation

were 5.3 times (95% C.I.: 1.63-17.26) more likely to experience a major complication

perioperatively than patients treated with a single stage procedure.115 Forty-four percent of

participants either selected higher risk in 1-stage surgery or equal risk between the two

techniques. Given that patients treated with a 2-stage surgery likely have more complex

degenerative pathology, it seems intuitive that this procedure would be associated with higher

risks. These results confirm there is substantial variability in surgeons’ perceptions of surgical

risks and likely inconsistency across clinicians in terms of the information they convey to their

patients.

Forty-four percent of professionals agreed complications are more frequent in

laminectomy with fusion than in laminoplasty. However, 37% argued there is no significant

difference in complication rates between these posterior techniques. According to the

literature, there is no difference between the two groups with respect to overall complication

rates and rates of wound infection, dysphagia, neck pain, cerebrospinal leakage, kyphosis and

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re-stenosis.115, 276, 310 In addition, in a prospective study by Fehlings et al (2012), there was no

difference in the incidence of C5 radiculopathy between patients undergoing laminectomy with

fusion and those treated by laminoplasty.115 In our survey, surgeons believed instability is more

common following laminoplasty than laminectomy with fusion, likely because fusion is used to

stabilize the spine. This difference of perception is likely due to differences in surgical

preferences and varying skill levels for each procedure.

There were international variations in responses that may reflect regional differences in

management strategies, surgical preferences or expertise and patient demographics. In the

AOSpine International study, the majority of patients from Europe, Asia Pacific and North

America were treated anteriorly, whereas Latin American surgeons preferred the posterior

approach. Interestingly, in our survey, professionals from Asia Pacific specified that patients

undergoing anterior surgery are at a higher risk of complications than those treated posteriorly.

With respect to posterior surgeries, East Asian surgeons likely prefer posterior laminoplasty as

this technique was developed in Japan to address the limitations of laminectomy. Five studies

specifically compared complication rates between anterior decompression and laminoplasty; of

these, four were conducted at sites in Japan and one at a center in China.11, 270, 271, 273, 311 Two

reported a higher incidence of complications in the anterior group than in the laminoplasty

group.11, 311 Specific complications, were not significantly different between groups, including

C5 palsy, bone graft complications and donor site morbidities.273 Professionals from Asia Pacific

also reported higher rates of complications in laminectomy with fusion than in laminoplasty;

this result likely reflects surgical preferences in Asia Pacific. Surgeons from North America and

Latin America agreed complications were more common in fusion surgery than non-fusion

procedures; however, due to increased instability and post-laminectomy kyphosis in non-fusion

surgery, fusion is generally performed to counter these complications.

The results from this study provide insight as to what surgeons’ perceive as the most

important clinical and surgical predictors of complications. Evidently, there is discrepancy

between professional opinion and literature findings, and, in some cases, differences in

perceptions among surgeons. Future prospective cohort studies are required to address this

controversy and properly identify the most significant predictors of complications.

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Chapter 5: An Overview of the AOSpine CSM-North America and

International Studies

5.1 Introduction

This chapter provides an overview of the AOSpine CSM-North America and International

studies. Both studies were primarily undertaken to evaluate the impact of surgery on

neurological outcomes, functional status and quality of life and to assess its value across the full

range of myelopathy severity. The other primary objective of these studies was to observe

differences in demographics, complications and outcomes between patients treated anteriorly

and those treated posteriorly. These studies also had multiple secondary aims including to

assess differences between laminectomy with fusion and laminoplasty; to identify correlations

between preoperative MRI parameters and baseline clinical examination; and, in the CSM-

International study, to ascertain regional differences in disease causation, patient

demographics, surgical preferences and outcomes.

5.2 Study Design and Inclusion Criteria

Both studies were prospective, multicenter cohort studies that followed patients from

their preoperative visit to 2-years after surgery. A randomized control trial was not feasible in

this patient population; given there is increasing evidence to suggest surgery is effective, it

would be unethical to deny patients surgery for the purpose of this study.

Participating centers were selected following an open call by AOSpine and were

subsequently evaluated to ensure capacity to conduct prospective research, subject availability

and presence of experienced research personnel. All sites were either academic centers or high

volume private practices. Investigators were orthopedic or neurosurgeons who specialized in

spine. Ethical approval was obtained from the internal review boards at all participating sites.

From December 2005 to September 2007, 278 consecutive patients with clinically

diagnosed and image-confirmed CSM were enrolled in the AOSpine CSM-North America study

at one Canadian and 11 American sites. Patients were eligible for this study if they were

referred for surgical consultation to a site’s orthopaedic or neurosurgery department and if

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they satisfied the following inclusion criteria: i) aged 18 years or older; ii) presenting with

symptomatic CSM with at least one clinical sign of myelopathy; iii) image evidence of spinal

cord compression; and iv) no previous spine surgery. Our definition of CSM was all-

encompassing and included patients with cervical myelopathy secondary to either spondylosis,

disc herniation, OPLL, hypertrophy of the ligamentum flavum, subluxation or a combination of

these degenerative changes. Patients were excluded if they were asymptomatic or if they had

active infection, neoplastic disease, rheumatoid arthritis, trauma, ankylosing spondylitis and

concomitant lumbar stenosis. Full inclusion and exclusion criteria are provided in Table 5-1.

Written consent was obtained for each subject who verbally agreed to participate in this study.

Table 5-1. Inclusion and Exclusion Criteria for Participation in the CSM-North America and CSM-

International Studies

Inclusion Criteria: Subject… Exclusion Criteria: Subject…

Was referred for surgical consultation with symptomatic CSM

Has asymptomatic CSM, active infection, neoplastic disease, rheumatoid arthritis, ankylosing spondylitis, trauma, concomitant lumbar stenosis

Is able and willing to give consent to participate in study

Is a pregnant woman or planning to get pregnant during the study period

Is able and willing to comply with postoperative management program

Has a history of substance abuse (recreational drugs, alcohol)

Can understand and read English at an elementary level

Is a prisoner

>18 years of age Is currently involved in a study with similar purpose

Has one or more of the following symptoms of CSM: Numb hands Clumsy hands Impaired gait Bilateral arm paresthesia L’Hermitte’s phenomena Weakness

Has a disease process that would preclude accurate evaluation (e.g. neuromuscular disorders, significant psychiatric disease)

And one or more of the following signs: Corticospinal distribution motor deficits Atrophy of intrinsic hand muscles Hyperreflexia Positive Hoffman sign Upgoing plantar responses Lower limb spasticity Broad-based unstable gait

Previous surgery for CSM

CSM: cervical spondylotic myelopathy

The recruitment process and inclusion and exclusion criteria for the CSM-International

study were the same as for the North American study. Between October 2007 to January 2011,

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479 consecutive CSM patients were enrolled in the AOSpine CSM-International study at 16

global sites. Figure 5-1 provides an overview of the sites involved in both studies and the

geographical distribution of participating subjects. In the combined dataset, 401 patients were

enrolled from 12 North American sites, 150 from six Asian Pacific sites, 126 from five sites in

Europe and 80 from three sites in Latin America.

Figure 5-1. Enrollment Summary of the AOSpine CSM-North America and International Studies Sites in North America: Emory University, Johns Hopkins University, New England Baptist Hospital, University of Virginia, Indiana Spine Group, University of Kansas Medical Center, Thomas Jefferson University and Rothman Institute, Mayo Clinic, University of Utah, Spine Education and Research Institute, Toronto Western Hospital; Sites in Asia Pacific: Southwestern Hospital, All India Institute of Medical Sciences, Tan Tock Seng Hospital, Okayama University, Teiko Chiba Medical University, Chuba Rosai Hospital; Sites in Europe: Canisius Wilhelmina Hospital, Ege University-Faculty of Medicine, Beaumont Hospital, Medical University of Catania, Medical University of Ancona; Sites in Latin America: University of Sao Paulo, Hospital Santa Marcelina, Hospital San Juan de Dios.

5.3 Surgical Protocol

At their respective sites, all 757 patients were treated as per standard of care and

received surgical decompression of the cervical spine. The attending surgeon dictated what

approach (anterior, posterior or combined) to use, the surgical technique, the number of

segments to decompress and whether or not to use fusion or grating. Patients treated

anteriorly received a cervical discectomy and/or corpectomy with or without fusion. Posterior

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surgeries included laminoplasty or laminectomy with or without fusion. In certain complex

cases, patients were treated with a 2-stage circumferential procedure.

5.4 Data Collection

Using pre-designed case report forms, extensive data was obtained for each patient at

baseline and at 6, 12 and 24 months following surgery, including demographics, surgical

summary, imaging and clinical assessment, medical history and previous conservative

treatments. Functional status and patient reported quality of life were evaluated at each visit

using a variety of scales such as the mJOA, Nurick, NDI, SF-36 and 30-meter walking test.

An AO Clinical Investigation & Documentation representative was responsible for

monitoring these studies and ensuring that data was authentic, accurate and complete and that

the study was conducted in accordance with the protocol. Data was entered into electronic

case report forms through a secure electronic database system and was processed at the

AOSpineNet central data management centre. Data entry was validated by visual inspection

and database programming.

5.4.1 Clinical Variables

Table 5-2 summarizes some of the clinical variables collected as part of the AOSpine

studies. The ones included in this table are relevant to analyses presented in future chapters.

Table 5-2. A Summary of Relevant Clinical Variables collected as part of the CSM-North America

and International Studies

Clinical Variable Summary

Age (Continuous) Age in years at time of surgery

Gender (Male/Female)

-

Body Mass Index (Continuous)

𝐵𝑀𝐼 =𝑚𝑎𝑠𝑠 (𝑘𝑔)

(ℎ𝑒𝑖𝑔ℎ𝑡(𝑚))2

Duration of symptoms (5 categories)

Time between onset of symptoms and surgical intervention: 1: Duration ≤ 3 months 2: 3 months < Duration ≤ 6 months 3: 6 months ≥ Duration ≤ 12 months 4: 12 months ≥ Duration ≤ 24 months 5: Duration > 24 months

163

Co-morbidities (Present/Absent)

For each co-morbidity category, patients were classified as grade 1 (mild decompensation), grade 2 (moderate decompensation) or grade 3 (severe decompensation).

Cardiovascular (Present/Absent)

Myocardial infarct Grade 1: -Old MI by ECG only, age undetermined

Grade 2: -MI>6 months ago

Grade 3: -MI≤6 months ago

Angina/Coronary artery disease

Grade 1: -ECG or stress test evidence or catheterization evidence of coronary disease without symptoms -Angina pectoris not requiring hospitalization -CABG or PTCA (>6 months) -Coronary stent (>6 months)

Grade 2: -Chronic exertional angina -Recent CABG or PTCA (≤6 months) -Recent coronary stent (≤6 months)

Grade 3: -Unstable angina

Congestive heart failure

Grade 1: -CHF with dyspnea which has responded to treatment -Exertional dyspnea -Paroxysmal nocturnal dyspnea

Grade 2: -Hospitalized for CHF>6 months prior -CHF with dyspnea which limits activities

Grade 3: -Hospitalized for CHF within the past 6 months -Ejection fraction <20%

Arrhythmias Grade 1: -Sick Sinus Syndrome

Grade 2: -Ventricular arrhythmia >6 months -Chronic atrial fibrillation or flutter -Pacemaker

Grade 3: -Ventricular arrhythmia ≤6 months

Hypertension Grade 1: -DBP 90-114 mmHg while not taking antihypertensive medications -DBP<90 mmHg while taking antihypertensive medications -Hypertension, not otherwise specified

Grade 2: -DBP 115-129 mmHg -DBP 90-114 mmHg while taking antihypertensive medications -Secondary cardiovascular symptoms: vertigo, epistaxis, headaches

Grade 3: -DBP≥130 mmHg -Severe malignant papilledema or other eye changes -Encephalopathy

Venous disease Grade 1: -Old DVT no longer treated with Coumadin or Heparin

Grade 2: -DVT controlled with Coumadin or heparin -Old PE>6 months

Grade 3: -Recent PE (≤6 months) -Use of venous filter for PEs

Peripheral arterial disease

Grade 1: -Intermittent claudication -Untreated thoracic or abdominal aneurysm (<6 cm) -S/p abdominal or thoracic aortic aneurysm repair

Grade 2: -Bypass or amputation for gangrene or arterial insufficiency >6 months ago -Chronic insufficiency

Grade 3: -Bypass or amputation for gangrene or arterial insufficiency <6 months ago. -Untreated thoracic or abdominal aneurysm (≥6 cm)

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Respiratory (Present/Absent)

Grade 1: -Restrictive lung disease or COPD with dyspnea which has responded to treatment -FEV1 (66%-80%)

Grade 2: -Restrictive lung disease or COPD† with dyspnea which limits activities. -FEV1 (51%-65%)

Grade 3: -Marked pulmonary insufficiency. -Restrictive lung disease or COPD† with dyspnea at rest despite treatment. -Chronic supplemental O2

-CO2 retention (pCO2 > 50 torr) -Baseline pO2 <50 torr -FEV1 (<50%)

Gastrointestinal (Present/Absent)

Hepatic Grade 1: -Chronic hepatitis or cirrhosis without portal hypertension -Acute hepatitis without cirrhosis -Chronic liver disease manifested on biopsy or persistently elevated bilirubin (>3mg/dl)

Grade 2: -Chronic hepatitis, cirrhosis, portal hypertension with moderate symptoms “compensated hepatic failure”

Grade 3: -Portal hypertension and/or esophageal bleeding ≤6 months (encephalopathy, ascites, jaundice with total bilirubin >2)

Stomach/Intestine Grade 1: -Diagnosis of ulcers treated with medications -Chronic malabsorption syndrome -Inflammatory bowel disease on medication or h/o with complications and/or surgery

Grade 2: -Ulcers require surgery or transfusion of <6 units of blood

Grade 3: -Recent ulcers ≤6 months requiring ≥6 units of blood transfusion

Pancreas Grade 1: -Chronic pancreatitis without complications

Grade 2: -Uncomplicated acute pancreatitis -Chronic pancreatitis with minor complications*

Grade 3: -Acute or chronic pancreatitis with major complications**

End-stage renal disease (Present/Absent)

Grade 1: -Chronic renal insufficiency with creatinine 2-3mg%

Grade 2: -Chronic renal insufficiency with creatinine>3mg% -Chronic dialysis

Grade 3: -Creatinine >3mg% with multi-organ failure, shock or sepsis -Acute dialysis

Diabetes (Present/Absent)

Grade 1: -AODM controlled by oral agents only

Grade 2: -IDDM without complications -Poorly controlled AODM

Grade 3: -Hospitalization ≤6 months for DKA -Diabetes causing end-organ failure

Psychiatric (Present/Absent)

Grade 1: -Major depression or bipolar disorder controlled with medication

Grade 2: -Major depression or bipolar disorder uncontrolled

Grade 3: -Recent suicidal attempt -Active schizophrenia

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-Schizophrenia controlled with medication

Rheumatologic (Present/Absent)

Grade 1: -Connective tissue disorder on NSAIDS or no treatment

Grade 2: -Connective tissue disorder on steroids or immunosuppressant medications

Grade 3: -Connective tissue disorder with secondary end-organ failure

Neurological (Present/Absent)

Stroke Grade 1: -Stroke with no residual -Past or recent TIA

Grade 2: -Old stroke with neurologic residual

Grade 3: -Acute stroke with significant neurologic deficit

Paralysis Grade 1: -Paraplegia or hemiplegia, ambulatory and providing most of self-care

Grade 2: -Paraplegia or hemiplegia requiring wheelchair, able to do some self-care

Grade 3: -Paraplegia or hemiplegia requiring full support for activities of daily living

Neuromuscular disorders

Grade 1: -MS, Parkinson’s, Myasthenia Gravis, or other chronic neuromuscular disorder, but ambulatory and providing most of self-care

Grade 2: -MS, Parkinson’s, Myasthenia Gravis, or other chronic neuromuscular disorder, but able to do some self-care

Grade 3: -MS, Parkinson’s, Myasthenia Gravis, or other chronic neuromuscular disorder and requiring full support for activities of daily living

Number of co-morbidities (Continuous)

Number of co-existing medical illnesses

Co-morbidity score (Continuous)

Grade 1/mild=1, Grade 2/moderate=2, Grade 3/severe=3: summate disease severity across all co-morbidity categories. If severity was unknown for a particular co-morbidity, a ‘1’ was designated.

Signs (Present/Absent)

Corticospinal distribution motor deficits

Motor paralysis or weakness

Atrophy of intrinsic hand muscles

Thenar and hypothenar muscle wasting

Hyperreflexia Overactive or overresponsive reflexes

Positive Hoffman sign When tapping the nail or flicking the terminal phalanx of the middle or ring finger elicits flexion of the terminal phalanx of the thumb

Babinski Sign When stimulating the sole of the foot with a blunt instrument elicits extension of the hallux

Lower limb spasticity Increased, involuntary, velocity-dependent muscle tone of the lower limbs that causes resistance to motion

Broad-based, unstable gait

A staggering gait in which the patient walks with a wide base

Symptoms (Present/Absent)

Numb hands Loss of sensation or feeling in hands or fingers

Clumsy hands Lacking dexterity and fine motor movements in hands

Impairment of gait Any dysfunction in walking

Bilateral arm paresthesia

Nonspecific numbness and tingling in both arms

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L’Hermitte’s phenomena

Sudden transient electric-like shocks down the spine triggered by forward head flexion

Weakness Lack of physical strength, energy or vigor

OPLL (Present/Absent)

Evidence of an ossified posterior longitudinal ligament on MRI or CT scan

*Minor complications include malabsorption, impaired glucose tolerance or gastrointestinal bleeding); **Major complications include phlegmon, abscess or pseudocyst); †i.e. chronic bronchitis, asthma or emphysema MI: myocardial infarct, ECG: electrocardiogram; CABG: coronary artery bypass graft; PTCA: percutaneous transluminal coronary angioplasty; CHF: congestive heart failure; DBP: diastolic blood pressure; DVT: deep venous thrombosis; COPD: chronic obstructive pulmonary disease; FEV: forced expiratory volume; AODM: adult onset diabetes mellitus; IDDM: insulin-dependent diabetes mellitus; NSAIDS: non-steroidal anti-inflammatory drug; TIA: transient ischemic attack; OPLL: ossification of the posterior longitudinal ligament;

5.4.2 Imaging Variables

Preoperative MRIs were acquired using 1.5 Tesla magnets. All available images were

reviewed by three investigators to identify 1) the mid-sagittal slice on T2-WI, 2) the level of

greatest cord compression and canal compromise, and 3) the presence/absence of signal

change on T1-WI and T2-WI. All investigators were blinded to the patient’s clinical and

neurological status. Several parameters were measured, including spinal cord compression,

spinal canal compromise, presence/absence of signal change on T1-WI and T2-WI and signal

change ratio using methods described by Nouri et al (2014), Wang et al (2010) and Arvin et al

(2011).312, 313 We only obtained 149 images from the CSM-North America study as MRIs were

primarily taken for diagnostic purposes and MRI data collection was not a prerequisite for the

original study.

Table 5-3 summarizes a list of the imaging parameters collected and provides a

description of how each was measured. The inter- and intra-rater reliability for spinal canal

compromise is 0.75±0.04 and 0.88±0.1, respectively and is 0.79±0.09 and 0.76±0.08 for spinal

cord compression.314 Transverse area also has high inter- and intra-rater reliability (0.86±0.03,

0.92±0.07, respectively).314

Figure 5-2 demonstrates how the signal change ratios were computed. The

hyperintense signal change or “region of interest” (ROI) was circumscribed. This intensity was

compared to the cerebrospinal fluid to calculate Arvin’s ratio. For the ratios developed by Wang

et al (2010) and Nouri et al (2014), a 0.05cm2 area within the ROI was circumscribed and

compared to either reference B (below) or an average of reference A (above) and reference B.

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Table 5-3. A Summary of the Imaging Parameters collected using Quantitative Analysis of

Magnetic Resonance Images from Patients enrolled in the CSM-North America Study

MRI Variable Summary

Hyperintensity on T2WI Presence/Absence of a hyperintense signal on a T2-WI

Hypointensity on T1WI Presence/Absence of a hypointense signal on a T1-WI

Combined T1/T2 Signal Change

Presence/Absence of a combined hyperintense signal on a T2-WI and hypointense signal on a T1-WI

Height of T2 signal change

The height of a circumscribed hyperintense signal on a T2-WI Four groups (=0; >0, ≤0.75; >0.75, ≤1.50; >1.50)†

Area of T2 signal change The area of a circumscribed hyperintense signal on a T2-WI Four groups (=0; >0, ≤0.2; >0.2, ≤0.35; >0.35)†

Signal Change Ratio (Nouri et al)

Continuous

SCR =ROI (0.05cm2)

(Ref A + Ref B)/2

ROI is a 0.05cm2 region within a signal change or at the level of maximal cord compression on a T2-WI (Ref A + Ref B)/2 is the average of two signal references at C7/T1 and C2

Signal Change Ratio (Wang et al)

Continuous

SCR =ROI (0.05cm2)

Ref B

ROI is a circumscribed 0.05cm2 region within a signal change or at the level of maximal cord compression on a T2-WI Ref B is a signal reference at C7/T1

Signal Change Ratio (Arvin et al)

Continuous

SCR = (ROI

CSF Ref) X 100

ROI is a region of hyperintensity on a T2-WI Ref B is a CSF signal reference behind the dens

Spinal Canal Compromise

Continuous

SCanalC = (1 – Di

(Da + Db)/2) X 100

Di is the anteroposterior canal diameter at the level of maximum compression Da and Db are the anteroposterior diameters of non-compressed levels from above and below, respectively

Spine Cord Compression Continuous

ScordC = (1 – di

(da + db)/2) X 100

di is the anteroposterior spinal cord diameter at the level of maximum compression da and db are the anteroposterior diameters of non-compressed levels from above and below, respectively

Transverse area Spinal cord surface at site of maximal compression

†Categorization of height and area was based on variable distribution.

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Figure 5-2. Computing Signal Change Ratios

5.4.3 Surgical Variables

For each patient, the surgeon was required to provide a surgical summary and record

the approach taken, the number of levels decompressed and the operative duration. Table 5-4

describes the surgical variables included in our analyses. The type of surgery varies case by case

and is largely based on location of compression, extent of degenerative pathology and surgical

preference. Operative duration is likely related to surgical expertise and case complexity.

Table 5-4. A Summary of Relevant Surgical Variables collected as part of the AOSpine Studies

Surgical Variables Summary

Operative duration Length of surgery, skin-to-skin time (minutes)

Number of levels The number of cervical levels decompressed by surgery

Type of surgery Anterior (discectomy and/or corpectomy with or without fusion/fixation) Posterior (laminectomy with or without fusion, laminoplasty) Circumferential (anterior and posterior stage)

Number of stages 1-stage (anterior or posterior surgery), 2-stage (anterior and posterior surgery)

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5.5 Primary Outcome Measure

The mJOA was selected as the primary outcome measure for our study as it is one of the

most commonly used tool to assess functional status in patients with CSM.71 This 18-point scale

is an investigator-administered, CSM-specific index that evaluates upper and lower extremity

function, sensation and micturition (Table 5-5).69

Table 5-5. The modified Japanese Orthopaedic Association Scale

Motor dysfunction score of the upper extremity

0 - Inability to move hands

1 - Inability to eat w/ a spoon, but able to move hands

2 - Inability to button shirt, but able to eat w/ a spoon

3 - Able to button shirt w/ great difficulty

4 - Able to button shirt w/ slight difficulty

5 - No dysfunction

Motor dysfunction score of the lower extremity

0 - Complete loss of motor & sensory function

1 - Sensory preservation w/o ability to move legs

2 - Able to move legs, but unable to walk

3 - Able to walk on flat floor w/a walking aid (cane or crutch)

4 - Able to walk up and/or down stairs w/hand rail

5 - Moderate-to-significant lack of stability, but able to walk up and/or down stairs w/o hand rail

6 - Mild lack of stability but walks w/ smooth reciprocation unaided/

7 - No dysfunction

Sensory dysfunction score of the upper extremities

0 - Complete loss of hand sensation

1 - Severe sensory loss or pain

2 - Mild sensory loss

3 - No sensory loss

Sphincter dysfunction score

0 - Inability to micturate voluntarily

1 - Marked difficulty w/ micturition

2 - Mild-to-moderate difficulty w/ micturition

3 - Normal micturition

The other measures considered for this analysis were the SF-36, the NDI, the Nurick

score and the 30-meter walking test. A detail summary of the advantages and disadvantages of

each outcome measure is shown in Table 5-6.67

The mJOA at 1-year follow-up was not normally distributed in either the CSM-North

America or International datasets: 42.26% of patients had a final mJOA of 17 or 18, with 26.32%

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achieving a perfect score. A Box-Cox power transformation search was done to determine

lambda and how the mJOA should be transformed to normalize its distribution

Lambda=2 was the result of this transformation search, indicating that a Y2 power

transformation should yield normally distributed data. However, this transformation did not

result in a normally distributed mJOA variable.

Instead, we dichotomized the mJOA at 1-year so it could be used in logistic regression

analysis which does not require a normally distributed dependent variable. An “optimal”

outcome was defined as a 1-year mJOA greater than or equal to 16 and a “suboptimal”

outcome was a score less than 16. The rationale behind using this cut-off value will be

described in Chapter 6.

5.6 Complications

Throughout the study period, investigators were also required to record all adverse and

serious adverse events. An adverse event was defined as any untoward medical occurrence in a

subject. A serious adverse event was any adverse event that led to death or serious

deterioration in health; resulted in a life threating illness or permanent impairment; required

patient hospitalization, prolongation of stay or medical/surgical intervention; or led to fetal

distress, fetal death or a congenital abnormality or birth defect.

Surgeons could select from a list of 24 anticipated adverse events or specify the adverse

event in an “other” textbox (Table 5-7). Information was collected on severity of event, timing

relative to surgery, frequency, action taken (none, medication, conservative, operative

treatment) and outcome. All adverse events were adjudicated by a central panel of

investigators and classified as either related to CSM, related to surgery or unrelated.

Any uncertainties were resolved by consulting the source documents at the specific site.

An adverse event related to surgery was defined as a surgical complication. Complications were

further classified as minor or major, where major events resulted in permanent morbidity,

invasive intervention or prolongation of hospital stay.

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Table 5-6. Assessing Outcome in Patients with CSM: Advantages and Disadvantages of the mJOA, SF-36, NDI, Nurick and 30-meter

walking test

Outcome Measure

Description Advantages Disadvantages

mJOA Assesses a patient’s functional status on an 18-point scale: 5-points for upper extremity function 7-points for lower extremity function 3-points for sensory function 3-points for bladder function The lower the score, the greater the disability.

-Commonly used in research studies, allowing for comparison between our results and existing evidence -Separately addresses upper and lower extremity function, sensation and micturition -Clinician-administered -CSM-specific index -Valid -Responsive to change

-Ceiling effect: Difficult to detect minor improvements in milder patients. -The four categories are not equally weighted. -Reliability has not been established.

SF-36v2 Assesses a patient’s general health disability using 8 subscales (100 points): Physical Functioning Bodily Pain Physical Role Limitations General Health Vitality Social Functioning Emotional Role Limitations Mental Health. The lower the score, the greater the disability

-Although not CSM-specific, this scale can effectively distinguish between myelopathy patients and controls -Good internal consistency -Valid -Moderate test-retest reliability for all 8 subscales (0.60-0.81)

-Patient reported, subjective outcome measure. -Not CSM-specific. -Floor effect for physical role limitations, emotional role limitations, physical functioning, bodily pain and social functioning. -Ceiling effect for social functioning, physical role limitations, and emotional role limitations. -Dependent on patient state when he/she fills out questionnaire. -Preoperatively, a patient’s score may depend on how urgently he/she believes they need surgery.

NDI Assesses a patient’s neck disability using 10 subscales (0=no disability, 5=complete disability): Pain Intensity Personal Care Lifting Reading Headaches Concentration Work Driving

-Incorporates several activities from day to day life. -Fair inter-observer reliability in patients with cervical radiculopathy -Responsive to change

-Patient reported, subjective outcome measure. -Not CSM-specific. -Validity and reliability have only been evaluated in patients with cervical radiculopathy and those undergoing neck surgery. -Dependent on patient state when he/she fills out questionnaire. -Preoperatively, a patient’s score may depend on how urgently he/she believes they need surgery.

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Sleeping Recreation Scores are summated across categories and multiplied by 2 for a total score out of 100. The higher the score the greater the disability.

Nurick Assesses a patient’s myelopathy severity using a 6 point scale: 0-Root involvement without SCD I-signs of SCD without difficulty in walking II-difficulty in walking without effect on employment III-difficulty in walking with effect on full-time employment IV-can walk only with aid or walker V-chair bound or bedridden

-Commonly used in research studies, allowing for comparison between our results and existing evidence -Clinician administered -CSM-specific index -Consists of both impairment and disability components -Valid

-Insensitive scale, difficult to detect improvements. -Solely based on gait impairment and employment status. -Largely weighted towards lower limb function.

30-m Walking Test

Patients begin seated in a chair and are asked to stand up, walk on a flat surface for 15 m, turn around and then walk back. The time required to complete this distance and the number of steps taken are recorded.

-Measures leg function and agility -Quantitative, objective measure -Clinician administered -CSM-specific index -Reliable -Valid -Bridges the gap between impairment and disability

-Exclusively measures lower limb function. -Walking may be impaired because of other reasons. -Some patients are unable to walk due to spinal cord injury.

CSM: cervical spondylotic myelopathy; mJOA: modified Japanese Orthopaedic Association; SF-36v2: short form-36 version 2; NDI: neck disability index; SCD: spinal cord dysfunction

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Table 5-7. A List and Description of Anticipated Surgery-Related Complications

Complication Description on Case Report Forms

Pseudoarthrosis Non-union

Hardware failure Implant loosening or breakage

Screw malposition Screw in non-tolerable/non-anatomic position (e.g. screw penetrating spinal canal)

C5 radiculopathy Clinical signs and symptoms (w/o confirmatory EMG) of C5 radiculopathy after surgery: C5 dermatome hypoesthesia, diminished or absent bicipital reflex (not present before surgery), postoperative deltoid and/or bicep muscle paresis

Axial pain Nuchal or periscapular pain or neck fatigue

New intractable neck pain Onset or reappearance of cervical pain not responding to oral pain treatment with non-steroid anti-inflammatory drugs, 3 months after surgery

Adjacent segment degeneration

Development of spondylotic changes in proximal or distal segments adjacent to treated segment, evaluated in cervical spine radiographies or MRI

Instability Sagittal plane translation equal to or over 3.5mm between 2 consecutive cervical levels in dynamic or static lateral cervical spine radiography; sagittal plane rotation over 20⁰ between two adjacent levels in dynamic lateral cervical spine radiography; relative sagittal plane angulations over 11⁰ between two adjacent cervical spine levels in static lateral cervical spine radiography, when compared to upper or lower cervical spine levels

Dural tear Intraoperative, iatrogenic dural tear which may or may not require repair

Epidural hematoma Hematoma occurring after surgery in epidural space

Deep infection Infection penetrating or developed under muscular fascia

Superficial infection Infection above muscular fascia

Dysphagia Patient reported difficulty with liquid or solid deglutition

Dysphonia Changes or difficulty in vocal sound production by the patient

Progression of myelopathy Increase of myelopathic signs and symptoms

New radiculopathy Signs and symptoms of other cervical spine root lesion

Perioperative worsening of myelopathy

Signs and symptoms due to cervical myelopathy worsening during the initial 4 week postoperative period

Graft dislodgement/migration

Graft migration shown in follow-up cervical spine radiographs as compared to the immediate postoperative radiography

Graft site pain Patient reported pain at the site of harvesting a bone graft 6 months after surgical treatment

Postoperative kyphosis Increased kyphosis at the operative segment/intervened segment of 5⁰ or more

Cardiopulmonary event Medical diagnosis of heart or lung disease (e.g. myocardial infarction, pulmonary embolism)

Stroke -

Deep venous thrombosis -

Cortical blindness -

Other -

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Chapter 6: The Minimal Clinically Important Difference of the modified

Japanese Orthopedic Association Score and Establishing a Cut-Off Point

6.1 Introduction

This chapters describes the methodology used to determine the minimal clinically

important difference (MCID) of the mJOA scale and summarizes our results. Furthermore, it

discusses the rationale behind using a cut-off of 16 to distinguish between an “optimal” and

“suboptimal” surgical outcome.

In both the CSM-North America and International studies, surgery was effective at

arresting disease progression and improving neurological outcomes, functional status and

quality of life.315 These improvements were evaluated using a wide variety of functional and

patient-reported outcome measures, including the mJOA, Nurick score, 30-meter walking test,

NDI and SF-36. The changes observed between preoperative and 1-year postoperative status

were statistically significant across all scales (p<0.001); however, for some of the measures, it is

unclear whether these improvements translate to clinically meaningful gains.

The MCID is the smallest change in a treatment outcome that a patient or clinician

would define as meaningful.316-319 For patients with degenerative spine conditions undergoing

cervical spine fusion, the MCID of the NDI is 7.5.320 In the AOSpine CSM-North America study,

patients improved by 8.72 on the NDI following surgery, reflecting substantial improvements in

disability. In terms of change in functional impairment, patients improved, on average, by 2.88

on the mJOA.4 However, since the MCID for the mJOA has yet to be established, investigators

cannot make strong conclusions with respect to meaningful gains in functional status following

surgery. This is a critical knowledge gap since the mJOA has become widely used to assess

treatment outcomes and functional improvements in patients with CSM.

This study aims to estimate the MCID of the mJOA using data from patients enrolled in

the AOSpine CSM-North America and International studies as well as results from a survey of

international spine professionals. This knowledge will enable clinicians to identify meaningful

functional improvements following intervention and will allow for better interpretation of

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previous research studies that used the mJOA as an outcome measure. A secondary objective

of this study is to establish a cut-off point between an “optimal” and “suboptimal” surgical

outcome that will be used in the prediction studies described in later chapters. This will be done

using the MCID as an anchor and comparing the postoperative mJOA scores of patients who

achieved this MCID and those who did not.

6.2 Methods

6.2.1 Patient Sample

Our sample consisted of 517 symptomatic CSM patients enrolled in either the CSM-

North America or CSM-International study at 26 global sites.

6.2.2 The MCID of the mJOA

We used three different methods to determine the MCID of the mJOA: 1) distribution-

based, 2) anchor-based and 3) professional opinion.

6.2.2.1 Distribution-based methods

Distribution-based methods compare change in outcome to a measure of variability

such as the standard deviation, effect size or standard error of measurement (SEM). The

rationale behind using these methods is to detect how much change between baseline and

post-treatment exceeds what is expected from chance alone. Norman et al (2003) proposed the

standard deviation method and reported that, in patients with chronic disease, estimates of

minimal change approximate half a standard deviation of baseline scores.321 Using our dataset,

the MCID was determined by computing one-half the standard deviation at baseline. We also

calculated the SEM using equation 6-1:

𝑆𝐸𝑀 = 𝑆𝐷 × √(1 − 𝑅) (Equation 6-1)

where SD is the standard deviation of the baseline score and R is the reliability of the mJOA.

The reliability was assumed to be 0.8 as this is the estimate for the JOA.322 A sensitivity analysis

was performed using an estimate of 0.7 and 0.9.

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6.2.2.2 Anchor-based methods

Anchor-based methods compare changes in the outcome measure to some other

measure of change or an “anchor.” In a study by Carreon et al (2010), the Health Transition

Item of the SF-36 was used as an anchor to determine the MCID of the NDI in a degenerative

spine population.320 The MCID was taken to be the change score from baseline with even

sensitivity and specificity to distinguish between patients who reported their outcome as “the

same as baseline” and those who reported their status as “somewhat better than baseline.”320

According to this study, change scores on the NDI of 7.5 reflect improvements in quality of life

detected by the patient.320 We used this established MCID as an anchor to determine what

changes in functional status translate to minimal improvements identified by the patient.

The sample was divided into four groups: patients who were “worse” (NDI<-7.5),

“unchanged” (-7.5≤NDI<7.5), “slightly improved” (7.5≤NDI<15.0), and “markedly improved”

(NDI≥15). The change in the mJOA between baseline and 12-months was compared between

the “unchanged” and the “slightly improved” groups and taken to be the MCID. ROC analysis

was also performed to compute a discrete value for the MCID by evaluating the threshold Δ in

mJOA (i.e. 1, 2, etc.) that yielded the smallest difference between sensitivity and specificity. In

this context, sensitivity is the proportion of patients who were classified as “slightly improved”

on the NDI score and had a Δ mJOA score above the MCID threshold. Specificity, on the other

hand, is the proportion of patients who were “unchanged” on the NDI and had a mJOA score

below the MCID threshold.

The sample was stratified into patients with mild (mJOA=15-17), moderate (mJOA=12-

14) and severe (mJOA<12) disease based on criteria established by the AOSpine study group.

Our anchor-based methods were repeated for each severity group to evaluate whether the

MCID differs based on the severity of myelopathy.

6.2.2.3 Survey of AOSpine International

The MCID of an outcome measure can also be calculated using consensus or Delphi

methods. By this approach, members of an expert panel are asked to independently define

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what constitutes a clinically meaningful change. This method was first used by Bellamy et al

(1992) to determine the MCID for a pain assessment scale.323

An English language 2-question survey was created to confirm the MCID obtained using

previously-described methods. An email request to participate in this survey was sent to 5,774

members of AOSpine International with a cover letter outlining the objectives of this study and

an attached link to Survey Monkey (USA, https://www.surveymonkey.com). This link was

available electronically for 60 days, with two reminders sent out during this period.

The two survey questions were 1) In your practice, do you use the mJOA scale to assess

functional status and surgical outcome in patients with CSM? (Yes or No); and 2) In your

opinion, what is the MCID of the mJOA (0.5, 1.0, 1.5, 2.0, 2.5, or 3.0). The results from questions

1 and 2 were reported as frequencies and percentages of responses. The mean and the

standard deviation of the responses from question 2 was also computed.

6.2.3 MCID translated to a cut-off point

The second objective of this study was to define a cut-off value for the mJOA that could

effectively distinguish an “optimal” from a “suboptimal” surgical outcome. This cut-off will be

used in our prediction studies.

Using the results from section 6.2.2, patients were divided into two groups based on

whether or not they achieved the MCID on the mJOA. The average mJOA at 1-year was

computed for each group and the postoperative mJOA of the patients who exhibited clinically

significant improvements (≥MCID) was deemed an appropriate cut-off.

For further validation, the sample was divided at this cut-off. For each patient, the

number of MCIDs gained or lost was calculated by dividing the change in mJOA by our

computed MCID (equation 6-2):

# 𝑜𝑓 𝑀𝐶𝐼𝐷𝑠 𝑙𝑜𝑠𝑡 𝑜𝑟 𝑔𝑎𝑖𝑛𝑒𝑑 =𝑚𝐽𝑂𝐴 𝑎𝑡 1−𝑦𝑒𝑎𝑟−𝑚𝐽𝑂𝐴 𝑎𝑡 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒

𝐶𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒𝑑 𝑀𝐶𝐼𝐷 (Equation 6-2)

A score ≥1 was considered improvement; a score ranging from >-1 to <1 was defined as no

change; and a score ≤-1 was considered deterioration. The mean number of MCIDs gained or

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lost was compared between the two groups (≥cut-off, <cut-off) and the distribution of scores

was visualized. We also compared the mean change in mJOA at 1-year between the two groups.

6.3 Results

Five hundred and seventeen patients had complete mJOA and NDI data at baseline and

at 12-months following surgery. Patients with a preoperative mJOA score of 18 were excluded

from the analysis, as they had no room for improvement. Table 6-1 displays demographic

information, baseline status and 12-months outcome data for the entire sample and for

patients with mild, moderate and severe myelopathy.

Our cohort consisted of 315 men and 202 women, with ages ranging from 21 to 86 years

(mean age: 56.37±11.60). The mean baseline mJOA score was 12.48±2.71. One hundred and

twenty-nine patients were classified as mild (mJOA=15-17) preoperatively, 208 as moderate

(mJOA=12-14) and 180 as severe (mJOA<12). Based on the NDI at 12-months following surgery,

76 (14.70%) patients worsened (NDI<-7.5), 130 (25.15%) were unchanged (-7.5≤NDI<7.5), 87

(16.83%) slightly improved (7.5≤NDI<15) and 224 (43.33) showed marked improvements

(15≤NDI). Patients, on average, improved by 2.48±2.68 (-6-12) on the mJOA and reached a final

postoperative score of 14.96±2.68.

Table 6-1. A Summary of Demographics, Baseline Status and Surgical Outcomes of 517 Patients Enrolled in the AOSpine CSM-North America or CSM-International Multicenter Studies

All (n=517) Mild (mJOA=15-17) (N=129)

Moderate (mJOA=12-14) (N=208)

Severe (mJOA<12) (N=180)

Gender 315 M, 202 F 81 M, 48 F 126 M, 82 F 108 M, 72 F

Age (years) 56.37±11.60 52.32±10.29 55.31±11.31 60.51±11.57

Baseline mJOA 12.48±2.71 15.76±0.78 13.04±0.80 9.48±1.67

mJOA at 1-year 14.96±2.68 16.49±1.88 15.26±2.45 13.50±2.91

Change in mJOA 2.48±2.76 0.73±1.89 2.22±2.21 4.03±3.00

Baseline NDI 38.99±20.24 31.28±16.29 37.12±19.27 46.67±21.34

NDI at 1-year 26.91±20.51 20.36±17.12 26.32±20.60 32.29±21.26

Change in NDI NDI<-7.5 -7.5≤NDI<7.5 7.5≤NDI<15 NDI≥15

12.07±19.35 76 (14.70%) 130 (25.15%) 87 (16.83%) 224 (43.33%)

10.91±17.45 16 (12.40%) 42 (32.56%) 18 (13.95%) 53 (41.09%)

10.80±17.47 32 (15.38%) 53 (25.48%) 38 (18.27%) 85 (40.87%)

14.37±22.38 28 (15.56%) 35 (19.44%) 31 (17.22%) 86 (47.78%)

mJOA: modified Japanese Orthopedic; NDI: neck disability index

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6.3.1 What is the MCID of the mJOA?

6.3.1.1 Distribution-based methods

The standard deviation of the preoperative mJOA was 2.71. Based on the half standard

deviation method, the MCID is estimated as 1.36. The SEM was calculated as 1.21 using the

following equation:

𝑆𝐸𝑀 = 2.71 × √(1 − 0.8) =1.21 (Equation 6-3)

Using sensitivity estimates of 0.7 and 0.9 for reliability, the SEM-computed MCID was 1.48 and

0.86, respectively.

6.3.1.2 Anchor-based methods

Patients classified as “unchanged” according to the NDI improved on average by

1.56±2.37 on the mJOA, whereas patients who “slightly improved” exhibited an average gain of

2.67±2.50 (Table 6-2). The MCID based on this approach is 1.11 (2.67-1.56). Using ROC analysis,

the discrete value of the mJOA that yielded the smallest difference between sensitivity and

specificity was 2 (Figure 6-1).

Table 6-2. The mJOA Change Scores in Patients Classified as “Worsened,” “Unchanged,”

“Slightly Improved” and “Markedly Improved” based on the NDI.

Change in mJOA

Worsened (NDI<-7.5)

Unchanged (-7.5≤NDI<7.5)

Slightly Improved (7.5≤NDI<15)

Markedly Improved (15≤NDI)

All Patients 1.71±2.87 1.56±2.37 2.67±2.50 3.20±2.82

Mild (mJOA: 15-17)

-0.56±2.34 0.57±1.64 1.00±2.22 1.15±1.67

Moderate (mJOA: 12-14)

1.56±2.37 1.77±2.45 2.21±1.53 2.76±1.94

Severe (mJOA<12)

3.18±2.84 2.43±2.62 4.19±2.34 4.90±3.11

mJOA: modified Japanese Orthopaedic Association scale; NDI: Neck Disability Index; SF-36 PCS: Short-Form-36 Physical Component Score. The minimal clinically important difference (MCID) is 7.5 for the NDI. The difference in ΔmJOA (between baseline and 12-months after surgery) between patients who were “unchanged” and those who “slightly improved” was taken to be the MCID.

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Figure 6-1. ROC Analysis: Difference between Sensitivity and Specificity for All Patients mJOA2_0: change in mJOA between preoperative visit and 12-months postoperative. An ROC curve plots the true positive rate (sensitivity) against the false positive rate (1-specificiy) (left). Sensitivity is the proportion of patients who were “slightly improved” and had a mJOA score above the MCID threshold. Specificity is the proportion of patients who were classified as “unchanged” and had a mJOA score below the MCID threshold. The “Δ in mJOA” that yielded the smallest difference between sensitivity and specificity was taken as the MCID (right).

6.3.1.3 Survey of AOSpine International

Four hundred and sixteen members of AOSpine International completed the survey,

reflecting a 7.2% response rate. Fifty-three percent of respondents (n=220) answered that they

routinely use the mJOA in their clinical practice to evaluate functional status. For question 2,

the mean response was 1.65±0.66. A MCID of 2 was the most commonly selected answer

(n=164, 39.42%). However, this was not a convincing majority as 104 (25.00%) and 69 (16.59%)

participants chose 1 and 1.5, respectively. Figure 6-2 displays the frequency of each answer.

Based on these three methods, the MCID was estimated to be 1.5.

6.3.2 Does the MCID of the mJOA differ based on severity?

6.3.2.1 Mild Patients (mJOA=15-17)

Mild patients classified as “unchanged” according to the NDI improved by 0.57±1.64

points on the mJOA, whereas patients considered “slightly improved” exhibited a 1.00±2.22

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gain (Table 6-2). The MCID based on this difference is 0.43. The change value of the mJOA that

yielded the smallest difference between sensitivity and specificity was 1 for patients with mild

myelopathy (Figure 6-3).

Figure 6-2. Results from a Survey of AOSpine International An electronic survey was distributed to members of AOSpine International to ask “what is the minimal clinically important difference of the modified Japanese Orthopaedic Association scale?” This bar graph summarizes the frequencies of responses. The red bar indicates the most frequently selected answer.

Figure 6-3. ROC Analysis: Difference between Sensitivity and Specificity for Mild Patients mJOA2_0: change in mJOA between preoperative visit and 12-months postoperative.

0

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80

100

120

140

160

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0.5 1 1.5 2 2.5 3

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6.3.2.2 Moderate Patients (mJOA=12-14)

Moderate patients who were “unchanged” on the NDI score improved by 1.77±2.45

points on the mJOA, whereas those classified as “slightly improved” exhibited a 2.21±1.53 gain

(Table 6-2). The MCID based on this difference is 0.44. The difference between sensitivity and

specificity for moderate patients was the lowest at a MCID threshold of 2 (Figure 6-4).

Figure 6-4. ROC Analysis: Difference between Sensitivity and Specificity for Moderate Patients mJOA2_0: change in mJOA between preoperative visit and 12-months postoperative.

6.3.2.3 Severe Patients (mJOA<12)

Severe patients who were “unchanged” on the NDI score improved by 2.43±2.62 points

on the mJOA, whereas those classified as “slightly improved” exhibited a 4.19±2.34 gain (Table

6-2). The MCID based on this difference is 1.76. The difference between sensitivity and

specificity for severe patients was the lowest at a MCID threshold of 3 (Figure 6-5).

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Figure 6-5. ROC Analysis: Difference between Sensitivity and Specificity for Severe Patients mJOA2_0: change in mJOA between preoperative visit and 12-months postoperative.

6.4 MCID translated to a cut-off point

Patients with a preoperative score of 17 and 18 were excluded as they could not

improve by 1.5 points on the mJOA. The sample was divided into two groups: patients who

exhibited a ΔmJOA ≥1.5 and those who did not (ΔmJOA<1.5).

Of the 490 patients with 1-year follow-up, 329 improved by ≥1.5 points. Postoperative

mJOA at 1-year in this group was 15.82±2.19. In the group of patients (n=161) who did not

improve by at least 1.5 points on the mJOA, the mean mJOA at 1-year was 12.86±2.51. Patients

who reached a score of 16 on the mJOA likely demonstrated clinically significant gains. (Table 6-

3).

The mean number of MCIDs gained was 1.75±1.82. Twenty-nine patients demonstrated

deterioration, 132 stayed the same and 329 showed clinically meaningful improvements.

Patients with a score ≥16 at 1-year follow-up improved by 2.47±1.62 MCIDs, whereas those

with a score <16 did not exhibit clinically significant improvements (# of MCIDs=0.87±1.70). This

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result is emphasized by a significantly larger change in mJOA in the ≥16 group (3.70±2.42) than

in the <16 group (1.31±2.54) (p<0.0001, t-test). (Table 6-3).

Table 6-3. Establishing a Cut-off to Distinguish between Patients with an “Optimal” and “Suboptimal” Surgical Outcome

ΔmJOA ≥ 1.5 (n=329) ΔmJOA<1.5 (n=161)

mJOA at 1-year 15.82±2.19 12.86±2.51

mJOA at 1-year ≥ 16 (n=264) mJOA at 1-year < 16 (n=253)

# of MCIDs Lost/Gained 2.47±1.62 0.87±1.70

ΔmJOA 3.70±2.42 1.31±2.54

mJOA: modified Japanese Orthopaedic Association; Δ (mJOA at 1-year – mJOA at baseline); 1.5 was taken to be the minimal clinically important difference of the mJOA.

Figure 6-6 illustrates the distribution of the number of MCIDs gained or lost in the ≥16

and <16 groups. It is evident that patients achieving an outcome ≥16 make marked

improvements whereas those with a mJOA <16 at 1-year do not, on average, demonstrate

clinically meaningful gains.

Figure 6-6. The Distribution of the Number of MCIDs Gained or Lost in two Outcome Groups (mJOA≥16 and mJOA<16 at 1-year) mJOA: modified Japanese Orthopedic; MCID: minimal clinically important difference

6.5 Discussion

In this study, we first calculated the MCID of the mJOA using distribution- and anchor-

based methods and then confirmed our estimate by surveying spine professionals from

0

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-6.67 -3.3 -2 -0.67 0.67 2 3.3 4.67 6 7.3

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mJOA < 16

mJOA ≥ 16

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AOSpine International. Using distribution-based methods, the MCID was calculated as either

1.36 (standard deviation based) or 1.21 (SEM based). These estimates were higher than what

was computed using anchor-based methods (MCID=1.11) and ROC analysis (MCID=2). Finally,

although 2 was the most commonly selected MCID by spine professionals (29.42%), a range of

1-2 encompassed the opinion of the majority surveyed (81.01%). From these three methods,

we can estimate the MCID of the mJOA to be between 1 and 2 points. This information enables

clinicians to qualify outcomes following surgery as well as interpret results from clinical studies

in terms of minimum clinically important gains rather than statistical significance. For example,

in the AOSpine CSM-North America study, patients improved on average by 2.88 points on the

mJOA. We can now confirm that these changes translate to gains that a patient can identify and

likely reflect substantial improvements in functional status.

There is little consensus as to the optimal way to calculate the MCID. In this study, we

used a wide range of literature-supported methods, including SEM, one half standard deviation,

ROC analysis and professional opinion.316-318, 321 The smallest estimate for MCID was 1.11 and

derived from the anchor approach. ROC analysis yielded the largest estimate of 2 points; this

was the value of ΔmJOA that resulted in the smallest difference between sensitivity and

specificity. The ROC approach is the preferred method for the mJOA as it yields an integer value

for the MCID rather than a decimal number. Since the mJOA is an ordinal scale, a discrete value

for the MCID has greater clinical relevance and applicability for individual patients than a range

or a decimal estimate.

Given the variability of our estimates across methods, we speculated that the MCID of

the mJOA may be dependent on myelopathy severity. This is likely the case as the mJOA

exhibits a strong celling effect at the upper end of the scale and because each point increase is

not of equal weight. For patients with “mild” myelopathy, defined as a mJOA≥15, the MCID was

1 point. This finding has strong face validity as patients with subtle deficits in gait or hand

dexterity are likely to appreciate an improvement by 1 point as this gain could translate to a

perfect or near-perfect score. In contrast, for patients with moderate disease (mJOA=12-14),

the MCID was 2 and for those with severe myelopathy, the MCID was 3. Again these results are

intuitive: patients who are heavily debilitated and have lost social independence would likely

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require a 2 to 3 point improvement before identifying meaningful gains in functional status and

quality of life. The performance of the logistic regression model between the NDI (improved or

unchanged) and the change in mJOA was much higher for severe and mild patients than it was

for moderate patients. This likely explains the discrepancy between the MCID computed by

anchor-based methods (MCID=0.44) and ROC analysis (MCID=2) for moderate patients.

The mJOA criteria for mild, moderate and severe disease based was developed by the

AOSpine CSM study group for the purpose of the CSM-North America multicenter trial (2005-

2007). These definitions were agreed upon by spine professionals across the continent but

were never validated. Our current findings support that patients who are “mild” (mJOA=15-17)

differ from those who are “moderate” (mJOA=12-14) who differ from those who are “severe”

(mJOA<12) as the MCID of the mJOA increases by 1-point per severity group. This is a first step

to validating these severity definitions.

Our second objective was to determine an appropriate cut-off value for our prediction

model that could effectively distinguish an “optimal” from a “suboptimal” surgical outcome.

This cut-off was defined as 16; on average, patients who improved by 1.5 or more points on the

mJOA reached a final postoperative score of approximately 16. A score of 16 is also in the in-

range of mild impairment and can effectively differentiate between patients with mild

myelopathy and those with substantial residual neurological deficit.

6.6 Strengths and Limitations

This study used a wide range of methods to compute the MCID of the mJOA. Each

technique has certain limitations. The standard deviation method was discovered by Norman et

al (2003) when comparing the MCID estimates from studies that used a variety of assessment

tools. Thirty-two out of 36 studies yielded values that approximated one half a standard

deviation of the baseline score. This finding, although remarkably consistent across studies,

may not be universal and may not apply to the mJOA. The SEM is a superior distribution-based

approach; however, this calculation requires knowledge of the scale’s reliability which has yet

to be established for the mJOA. We used a value of 0.8 as this is the reliability of the JOA, and

also conducted two sensitivity analyses using 0.7 and 0.9 as the estimate. For the anchor-based

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methods, the MCID of the NDI was used as the “anchor” as this value was also established in a

degenerative spine population. The NDI itself is not highly correlated to the mJOA and is

primarily used to evaluate quality of life in patients with neck pain. Therefore, this scale is

relevant to the neck pain component of CSM but may not be applicable to other neurologic

signs and symptoms. However, since our anchor was the MCID of this scale, we are computing

what changes on the mJOA translate to detectable improvements in quality of life. A final

limitation to this study is the low response rate for our survey, although these methods

primarily served to confirm our previous estimates.

6.7 Conclusion

On average, the MCID of the mJOA is 1.5 points. For milder patients, the threshold

decreases to 1 and for moderate and severe patients, the threshold increases to 2 and 3 points,

respectively. This information can be used by clinicians to identify clinically meaningful

improvements in functional status following surgery. Furthermore, these findings can enable

investigators to better interpret results from previous studies that discuss the impact of surgery

on functional impairment. A score of 16 was deemed to be an appropriate cut-off to distinguish

between patients who achieve clinically important differences and those who do not. This

finding provides rationale for using a score of 16 as a cut-off for logistic regression predictive

modeling.

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Chapter 7: A Clinical Prediction Model to Determine Outcomes in

Patients with Cervical Spondylotic Myelopathy undergoing Surgical

Treatment: Data from the Prospective, Multicenter AOSpine North

American Study

7.1 Introduction

This chapter summarizes the methodology and results from a study conducted to

develop a North American prediction model. This study represents part 1 of a 4-part study

designed to create a globally-relevant and valid prediction model that could be implemented

into clinical practice.

7.2 Methods


Our sample consisted of 278 symptomatic CSM patients enrolled in the CSM-North

America study from 12 North American sites.

7.2.2 Statistical Analysis

Continuous predictors were described using means, standard deviations and ranges.

Categorical variables were summarized using percentages. Duration of symptoms was divided

into five groups as this variable could not be transformed without having remaining outliers: 1)

≤3 months, 2) >3, ≤6 months, 3) >6, ≤12 months, 4) >12, ≤24 months, 5) >24 months. Missing

follow-up data and MRI/CT measurements were assumed to be missing at random and were

replaced with a set of plausible values using a multiple imputation procedure with 10 iterations.

As suggested by the Food and Drug Administration, multiple imputation is the preferred

method for handling missing data in a therapeutic trial. Using this procedure, the results are

likely to be less susceptible to bias and more efficient than removing patients with incomplete

variables.324-326

Using imputed data, simple logistic regression analyses were conducted to evaluate the

association between surgical outcome (mJOA≥16, mJOA<16) and various clinical and imaging

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factors. Predictors that yielded a p-value <0.2 in univariate analyses were evaluated in

multivariate analysis.327 In addition, variables that had a p-value >0.2 but were considered to be

clinically relevant were also assessed in multivariate analysis.

Collinearity of all variables was evaluated by calculating tolerance. Manual backward

stepwise regression was used to determine the best combination of predictors. Variables were

included if they a) were statistically significant (p<0.05); b) enhanced the predictive

performance of the model, as determined by the area under the ROC curve; and/or c) were

considered clinically relevant by existing literature or through author consensus. Logistic

regression was used to formulate the final prediction model and compute odds ratios, 95%

confidence intervals and parameter estimates of each covariate. The prediction equation is

given by equation 7-1:

𝑃 =𝑒

𝛽0+𝛽1𝑋1+𝛽2𝑋2+𝛽3𝑋3+⋯𝛽𝑗𝑋𝑗

1+𝑒𝛽0+𝛽1𝑋1+𝛽2𝑋2+𝛽3𝑋3+⋯𝛽𝑗𝑋𝑗

(Equation 7-1)

where P is the probability of achieving an “optimal” outcome (mJOA≥16), β0 is the estimate of

the intercept and β(1,2,3,j) are the parameter estimates of the predictor variables X(1,2,3,j).

7.2.3 Secondary Analysis

Given the non-normal distribution of our data and the limitations of the mJOA scale,

logistic regression analysis was identified as the best statistical model for this patient

population. Similar steps, however, were taken to create a multiple linear regression model

using postoperative mJOA score at 1-year as the dependent variable.

7.3 Results


Of the 278 patients, six had a preoperative mJOA score of 18 and were excluded from

the analysis. All 272 patients were assessed preoperatively. Two hundred and seventeen of

these patients (79.78%) were assessed for improvements in functional status at 1-year

following surgery. The other 55 patients either withdrew from the study, did not attend their

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scheduled appointment or died prior to their 1-year postoperative visit. We imputed missing

transverse area measurements and follow-up scores to obtain a complete dataset. Table 7-1

provides a summary of baseline and follow-up characteristics for the entire study population.

The sample consisted of 160 men (58.85%) and 112 women (41.18%), with ages ranging

from 29 years to 86 years (mean 56.64±11.75 years). Patients presented with a wide range of

preoperative severity (3-17) and a mean baseline mJOA score of 12.71 ± 2.64. The mean

duration of symptoms was 26.14±45.89, with a range from 0.50 to 432 months. With respect to

degenerative diagnosis, 78.68% of patients displayed evidence of spondylosis, 69.49% of disc

herniation and 22.43% of a hypertrophied ligamentum flavum. A minority of patients presented

with OPLL (8.82%) and subluxation (4.78%). The most common signs on clinical assessment

were hyperreflexia (73.43%), a positive Hoffman’s sign (63.47%) and broad-based unstable gait

(52.40%) and the most common symptoms were numb hands (89.30%), clumsy hands (78.60%),

and impaired gait (76.01%).

Table 7-1. Patient Baseline Demographic Information and 1-year Functional Outcomes following

Surgery: CSM-North America Study

Variable Descriptive Statistics

Baseline severity score (mJOA) 12.71±2.64 (3-17)

Age (years) 56.64±11.75 (29-86)

Gender (%) 58.85 M, 41.18 F

Duration of symptoms (n=271) (months) 26.14±45.89 (0.5-432)

Smoker (%) 26.10

Co-morbidities (%) Cardiovascular Respiratory Gastrointestinal Renal Endocrine Psychiatric Rheumatologic Neurological

47.06 13.97 17.65 4.41 21.69 24.26 8.82 9.56

Diagnosis (presence, %) Spondylosis Disc herniation OPLL HLF Congenital stenosis Subluxation Other

78.68 69.49 8.82 22.43 15.81 4.78 2.57

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Symptoms (n=271) (%) Numb hands Clumsy hands Impaired gait Bilateral arm paresthesia L’Hermitte’s phenomena Weakness

89.30 78.60 76.01 52.03 31.37 87.82

Signs (n=271) (%) Corticospinal motor deficits Atrophy of intrinsic hand muscles Hyperreflexia Positive Hoffman’s sign Upgoing plantar responses Lower limb spasticity Broad-based unstable gait

50.18 39.85 73.43 63.47 28.41 38.75 52.40

Transverse area (n=139) (mm2) 45.89±14.13 (16.02-84.48)

mJOA score at 1-year 15.69±2.54 (6-18)

mJOA≥16 (n, (%)) mJOA<16

132 (60.83%) 85 (39.17%)

Means are given with standard deviations; mJOA: modified Japanese Orthopaedic Association; OPLL: ossification of the posterior longitudinal ligament; HLF: hypertrophy of the ligametum flavum

At 1-year following surgery, the mean mJOA score was 15.69±2.54 and 77 (35.48%)

patients achieved a perfect score of 18. One hundred and thirty-two (60.83%) patients were

classified as having an “optimal” surgical outcome and were mild postoperatively (mJOA≥16)

whereas 85 (39.17%) still had substantial residual neurological impairment (mJOA<16).

7.3.2 Univariate Analysis

The results from univariate analysis are summarized in Table 7-2. The significant

predictors of a mJOA≥16 were younger age (OR: 0.96, p=0.0004); a higher mJOA score (OR:

1.32, p<0.0001); the absence of broad based, unstable gait (OR: 2.72, p=0.0018), impaired of

gait (OR: 3.56, p=0.0005) and psychiatric co-morbidities (OR: 0.51, p=0.024); and a shorter

duration of symptoms (OR: 0.80, p=0.030). The associations between a mJOA≥16 and

cardiovascular co-morbidities (p=0.076), smoking status (p=0.057), transverse area (p=0.11),

weakness (p=0.16) and number of levels (p=0.12) yielded p-values <0.2 and were also assessed

in multivariate analysis. The authors confirmed that no additional clinical or imaging factors

should be further examined.

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and a mJOA Score ≥16 at 1-year following Surgery

Predictor Odds Ratio 95% C.I. p-value

General Characteristics

Baseline severity score (mJOA) 1.32 1.18, 1.48 <0.0001

Age (years) 0.96 0.93, 0.98 0.0004

Duration of symptoms 0.80 0.65, 0.98 0.030

Smoking Status* 0.53 0.27, 1.30 0.057

Co-morbidities (REF=absence)

Psychiatric 0.51 0.29, 0.92 0.024

Cardiovascular* 0.62 0.36, 1.05 0.076

Symptoms (REF=presence)

Impaired gait 3.56 1.75, 7.22 0.0005

Weakness* 1.83 0.78, 4.26 0.16

Signs (REF=presence)

Broad-based unstable gait 2.72 1.47, 5.06 0.0018

Other

Transverse area (mm2)* 1.02 0.995, 1.05 0.11

Number of levels* 0.84 0.68, 1.04 0.12

CI: confidence interval; mJOA: modified Japanese Orthopaedic Association; baseline mJOA: 0-18; symptom duration (1-5): 1) <3 months, 2) >3, ≤6 months, 3) >6, ≤12 months, 4) >12, ≤24 months, 5) >24 months. A * indicates variables that did not have p<0.1 but were included in multivariate analysis because p<0.2

7.3.3 Multivariate Analysis

Table 7-3 displays the final logistic regression model and summarizes the odds ratios,

95% confidence intervals and parameter estimates for each included variable. This model

yielded an area under the ROC area of 0.79 and consisted of six statistically significant clinical

variables and one imaging variable deemed clinically relevant (Figure 7-1). According to the

final model, patients are more likely to achieve a mJOA≥16 if they have a higher preoperative

mJOA score (OR: 1.22, p=0.0084); do not smoke (OR: 0.46, p=0.043), have psychiatric co-

morbidities (OR: 0.33, p=0.0035) or impaired gait (OR: 2.66, p=0.020); are younger in age (OR:

0.97, p=0.017); have a shorter duration of symptoms (OR: 0.78, p=0.048); and have a larger

transverse area (OR: 1.02, p=0.19). The final logistic regression model is given by equation 7-2

𝑃 =𝑒−0.028+(−1.12)𝑃𝑠+(0.20)𝑚𝐽𝑂𝐴0+(−0.035)𝐴+(0.98)𝐼𝐺+(−0.78)𝑆+(−0.25)𝐷𝑆+(0.020)𝑇𝐴

1+𝑒−0.028+(−1.12)𝑃𝑠+(0.20)𝑚𝐽𝑂𝐴0+(−0.035)𝐴+(0.98)𝐼𝐺+(−0.78)𝑆+(−0.25)𝐷𝑆+(0.020)𝑇𝐴 (Equation 7-2)

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where Ps is presence (2) or absence (1) of psychiatric co-morbidities, mJOA0 is baseline severity

score (0-18); A is age; IG is presence (1) or absence (2) of impaired gait; S is smoker (2) or non-

smoker (1); DS is duration of symptoms (1-5, see methods); and TA is transverse area.

With respect to odds ratios, the odds of a successful outcome i) decrease by 22% when

a patient moves from a shorter to a longer duration of symptoms group (i.e from ≤3 to 3-6

months); ii) are 1.22 times greater for every one point increase in preoperative mJOA; iii)

decreases by 3% for every one year increase in age; iv) are approximately half for patients who

smoke compared to non-smokers; v) are 2.66 times greater for patients without impaired gait;

vi) are 67% lower for patients with depression or bipolar disorder; and vii) are 1.02 times

greater for every one point increase in transverse area (Table 7-3).

Figure 7-1. Receiver Operating Curve for the Final Clinical Prediction Model A ROC curve plots the true positive rate (sensitivity) against the false positive rate (1-specificiy). The predictive performance of this model can be quantified by calculating the area under the ROC curve. An area of 1 indicates a perfect test (100% specific and 100% sensitive), whereas an area of 0.5, displayed by the linear diagonal line, indicates no discriminative value. An area of 0.79 suggests that this model has near excellent discrimination, defined as an area between 0.80-0.90.

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Table 7-3: A Clinical Prediction Model to Determine Functional Status and Predict an “Optimal” Surgical Outcome (mJOA≥16)


Psychiatric Disorders (REF=absence) 0.33 0.15-0.69 0.0035

Baseline mJOA 1.22 1.05-1.41 0.0084

Age (years) 0.97 0.94-0.99 0.017

Impairment of Gait (REF=presence) 2.66 1.17-6.06 0.020

Smoking (REF=non-smoker) 0.46 0.21-0.98 0.043

Symptom Duration 0.78 0.61-0.997 0.048

Area (mm2) 1.02 0.99-1.05 0.19

CI: confidence interval; mJOA: modified Japanese Orthopaedic Association; baseline mJOA: 0-18; symptom duration (1-5): 1) <3 months, 2) >3, ≤6 months, 3) >6, ≤12 months, 4) >12, ≤24 months, 5) >24 months

7.3.4 Secondary Analysis

A secondary analysis using linear regression modeling yielded similar results to logistic

regression: the same set of predictors were identified as the most clinically and statistically

significant (Table 7-4). The model had a R2 of 0.26 and is given by equation 7-3:

𝐹𝑖𝑛𝑎𝑙 𝑚𝐽𝑂𝐴 = 14.34 + (−1.23)𝑃𝑠 + (0.21)𝑚𝐽𝑂𝐴0 + (−0.022)𝐴 + (0.96)𝐼𝐺 + (−0.73)𝑆 + (0.024)𝑇𝐴 +

(−0.31)𝐷𝑆 + (0.024)𝑇𝐴 (Equation 7-3)



smoker (1); DS is duration of symptoms (1-5, see methods); and TA is transverse area.

Table 7-4. Final Linear Regression Model using Postoperative mJOA at 1-year as the Dependent Variable

Predictor β-estimate 95% C.I. of Parameter Estimates

p-value

Psychiatric Disorders β=-1.23 -1.92, -0.55 p=0.0005

Baseline mJOA β=0.21 0.076, 0.34 p=0.0020

Symptom Duration β=-0.31 -0.53, -0.091 p=0.0057

Impairment of Gait β=0.96 0.27, 1.64 p=0.0062

Smoking β=-0.73 -1.45, -0.015 p=0.045

Area (mm2) β=0.024 -0.0031, 0.050 p=0.081

Age (years) β=-0.022 -0.047, 0.0030 p=0.084

CI: confidence interval; mJOA: modified Japanese Orthopaedic Association; baseline mJOA: 0-18; symptom duration (1-5): 1) <3 months, 2) >3, ≤6 months, 3) >6, ≤12 months, 4) >12, ≤24 months, 5) >24 months

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Similar to the logistic regression model, a higher postoperative mJOA score is associated

with the absence of impaired gait (p=0.0062) and psychiatric co-morbidities (p=0.0005); a

higher baseline mJOA score (p=0.002); younger age (p=0.084); non-smoking (p=0.045); a larger

transverse area (p=0.081); and a shorter duration of symptoms (p=0.0057) (Table 7-4).

7.4 Discussion

This study represents the first prospective, multicenter study and the largest analysis of

important predictors of surgical outcome in patients with symptomatic CSM. These results

should be generalizable to future CSM populations as our sample consisted of patients with a

wide range of ages, baseline severity scores and duration of symptoms as well as an adequate

percentage of patients presenting with various signs, symptoms and co-morbidities.

Based on our results, patients are more likely to achieve an “optimal” surgical outcome

(mJOA≥16) if they have milder myelopathy preoperatively and a shorter duration of symptoms;

are younger; do not smoke; do not have psychiatric disorders or impaired gait; and have a

larger transverse area. These seven variables were included in the final logistic regression

equation that can be used to accurately and objectively quantify a patient’s probability of

obtaining a score ≥16 on the mJOA. The patients who achieve this score will either be

asymptomatic or exhibit mild symptoms of myelopathy and will likely be socially independent.

This is in contrast to patients with a mJOA score <16 who will have substantial residual

neurologic impairment and may require assistance to perform simple, daily activities. Our

prediction model yielded an area under the ROC curve of 0.79, indicating a good ability to

discriminate between patients who will and will not achieve an “optimal” surgical outcome.

The multiple linear regression equation consisted of the same seven predictors as the

logistic regression equation. All of the variables, including transverse area, were statistically

significant (p<0.1). This model is more clinically useful as it allows clinicians to calculate an exact

postoperative mJOA score instead of the odds of achieving a mJOA score ≥16. This model,

however, violates a key assumption of multiple linear regression analysis because the mJOA at

1-year was not normally distributed: 50% of patients had a final mJOA of 17 or 18, with 35%

achieving a perfect score.

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It is important to interpret our results in the context of existing evidence. Several

previous studies have reported that both baseline severity score and duration of symptoms are

important predictors of surgical outcome.97, 129, 138, 143, 228, 229, 300, 328 The rationale behind these

two findings is that severe and chronic, longstanding compression of the spinal cord may lead

to irreversible histological damage such as cavitation, demyelination and necrosis of the gray

matter.

There is no consensus in the literature as to whether age is a significant predictor of

surgical outcome. Based on this North American prediction study, older patients have a

decreased odds of achieving an “optimal” outcome. We do not, however, recommend that

surgeons discriminate on the basis of age but rather be aware that their elderly patients may

not be able to translate neurological recovery to functional improvement as effectively as their

young patients. Possible explanations for this reduced recovery include 1) the elderly

experience age-related changes of their spinal cord, including a decrease in γ-motorneurons,

number of anterior horn cells and number of myelinated fibers in the corticospinal tracts and

posterior funiculus; 2) older patients are more likely to have co-morbidities that may affect

outcome; and/or 3) the elderly may not be able to effectively conduct all activities on the mJOA

due to these co-morbidities.124, 157, 170, 216, 217 It is important that a clinician distinguish between

a patient’s chronological age and his/her physiological age when predicting surgical outcome. In

general, however, age is associated with poorer surgical outcomes and so the expectations of

elderly patients should be managed accordingly.

This study also found that smokers are less likely to achieve an “optimal” outcome than

non-smokers. Previously, it was suggested that smoking negatively affects outcomes in lumbar

spine surgery as it is correlated with lower rates of fusion and a higher risk of wound infections.

Hilibrand et al. (2001) investigated the impact of smoking in CSM patients following multilevel

anterior cervical decompression with autogenous bone grafting and reported a higher rate of

solid osseous union in non-smokers compared to smokers.302 These increased fusion rates

resulted in better surgical outcomes in the non-smoking population. In our study, however,

there were no significant differences in rates of pseudoarthrosis or non-union between patients

who did and did not smoke. Therefore we do not believe the unfavorable outcome in the

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smoking population is due to low fusion rates. Instead, we speculate that smoking is a surrogate

for poorer lifestyle choices, lower socioeconomic status and an increased number of co-

morbidities. Further research is required to validate these conclusions and to determine exactly

why smoking leads to unfavorable outcomes.

Single studies have reported that both impaired gait and the presence of psychological

co-morbidities have a negative impact on surgical results. Wang et al. (2003) studied a series of

patients undergoing revision laminectomy following failed ACDF and reported that patients

with more severe gait impairment had a poorer surgical outcome as assessed by the Nurick

score.203 Kumar et al (1999) evaluated the association between emotional and psychological

issues and surgical outcome.206 The major finding of this study was that there was a significantly

greater occurrence of depression, evaluated by the SF-36, in the “poor” outcome group

compared to the “good” outcome group. However, it is challenging to draw conclusions on the

predictive value of psychological co-morbidities based on patient-generated outcome measures

such as the SF-36. Our current study is the first known study to identify a significant association

between clinically diagnosed depression/bipolar disorder and functional outcomes.

The last predictor included in the model was transverse area. In a study by Okada et al

(1993), there was a significant association between preoperative transverse area and recovery

rate in patients with OPLL and CSM.132 This finding is consistent with results from other studies

that suggested transverse area is an important predictor of recovery rate and postoperative

functional status at long-term follow-up.129, 329 In our analysis, however, transverse area was

not significantly associated with outcome. This variable was still included in the final model as it

was identified as a clinically important predictor by all authors based on past experience and

findings from the literature.117

7.5 Study Strengths and Limitations

The data for this analysis was prospectively collected at 12 surgical spine centers across

North America. Since there were multiple recruitment sites, we were able to accrue a sample of

278 patients, which is greater than three times the size of any other CSM prediction study.

Since the patients enrolled were treated surgically at hospitals across the continent, the

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findings from this study should be more generalizable and applicable to future populations than

conclusions from previous single center studies. One of the major limitations of this study is the

20% drop off rate at 1-year follow-up and the 50% missing transverse area data. The missing

data, however, was accounted for using a multiple imputation procedure as statistically

recommended. Another limitation is the violation of normality in the response variable which

could affect the results of multiple linear regression. Both models will need to be validated on a

second external dataset to confirm their predictive ability in future populations.

7.6 Conclusions

This study represents part 1 of a 4-part study designed to develop a globally valid

prediction model that can be used by clinicians to quantify a patient’s likely surgical outcome

and help manage expectations. Based on this model, patients were more likely to achieve an

“optimal” outcome, or a score ≥ 16 on the mJOA if they were younger; had milder myelopathy

and a shorter duration of symptoms; did not have impaired gait or clinically diagnosed bipolar

or depression; and had a larger transverse area.

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Chapter 8: A Clinical Prediction Model to Assess Surgical Outcome in

Patients with Cervical Spondylotic Myelopathy: Internal and External

Validation using the Prospective Multicenter AOSpine North American

and International Datasets in 743 Patients

8.1 Introduction

This chapter summarizes the methodology and results from an external validation study

designed to test the predictive performance of our North American model in an international

population. This chapter represents part 2 of our 4-part prediction study.

The prediction rule developed in Chapter 7 cannot be implemented into clinical practice

until its predictive performance is assessed on an independent, external population. This is

referred to as geographic validity and is evaluated by testing the model in a similar sample of

patients (i.e. symptomatic, surgical CSM patients) at institutions in other cities, countries and

even continents. The original prediction model was derived using data from 272 patients

enrolled in the CSM-North America study and therefore reflects the culture, population

characteristics and medical system of North America. There may exist international variations in

the management of CSM that may decrease the generalizability of our model and render it

invalid in regions outside of North America. These include differences in 1) disease definitions

and presentation, 2) average demographics, 3) interpretation of predictors or outcome, 4)

access to care, and 5) management strategies. The following examples provide reasons for why

these differences may or may not affect the predictive performance of our North American

model in an international population.

1) Differences in disease definitions and presentation

In both the North America and International study, the definition of CSM was all-

encompassing and included myelopathy secondary to spondylosis, chronic disc herniation,

OPLL, hypertrophy of the ligamentum flavum, subluxation and congenital stenosis. This broader

definition was used to prevent discrepancies in disease definitions across centers and as a first

step to standardize nomenclature at an international level.3

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Differences in disease presentation across countries may influence prognosis and result

in disappointing generalizability of our clinical prediction model. Specifically, these include a

higher incidence of OPLL and congenital stenosis in East Asia and a larger proportion of patients

with hypertrophy of the ligamentum flavum in Latin America.18, 330 Several previous studies

have separated their patient sample into groups depending on whether the primary

degenerative diagnosis was CSM, OPLL or disc herniation, whereas others have analyzed the

sample as a whole. To the best of our knowledge, the form of degenerative cervical myelopathy

is not a significant predictor of outcome and therefore differences in disease causation

worldwide should not affect the results of external validation.

2) Differences in demographic characteristics

Regional differences in patient demographics may also influence surgical outcome and

affect the validity of our model. In the CSM-International study, key differences included a

younger population in Asia and Latin America, a longer duration of symptoms in Latin

Americans, and a lower frequency of psychiatric disorders in sites outside of North America.

These regional variations in demographics could result in international differences in prognosis

and impact the external validity of our clinical prediction model.

3) Differences in reliability of predictors and outcome

Measurement error affects the reliability of the predictors and outcome assessment

tools. With respect to our prediction model, age, smoking status and impaired gait are assumed

to be reliable variables. In contrast, psychiatric disorders is a less reliable factor as there may be

cultural reluctance to report mental illness and variations in diagnosis across regions. The

reliability of “duration of symptoms” is unknown; however, the exact symptom duration may

be difficult for a patient to accurately recall and may be influenced by how urgently a patient

believes he needs surgery. Finally, the reliability of the mJOA has not been tested, resulting in

potential measurement biases. Even if the reliability of the English version of the mJOA was

known, the scale’s translatability would need to be evaluated as the International study was

conducted in several different languages.

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4) Differences in socioeconomic status and access to care

Access to surgical and post-surgical care could affect a patient’s outcome. Patients who

are required to pay for their medical care may be reluctant to see a physician at a milder

disease stage. This means that these patients may present with more severe myelopathy and a

longer duration of symptoms, both of which negatively impact outcome. Post-surgical

rehabilitation is also essential for optimal results and recovery. Again, patients who have less

access to this type of care or who have to pay for it out of pocket may be less likely to follow

the exact post-operative management program.

5) Differences in management strategies

Surgeons have different technical preferences. For example, the anterior approach was

more commonly used by surgeons from Asia, Europe and North America, whereas Latin

American surgeons preferred the posterior approach. With respect to specific posterior

techniques, surgeons in Asia typically performed a laminoplasty instead of a laminectomy with

fusion. Patients in North America had a greater number of operated levels than the other three

regions. Although these differences in management strategies seem significant, surgical

approach, technique and number of decompressed levels are not important predictors of CSM

outcome. Therefore, these differences are not likely to influence prognosis in this setting.

The objective of this study was to assess the internal and external validity of the clinical

prediction rule described in Chapter 7. The external validation was completed using a

prospective cohort of 479 patients enrolled in the AOSpine CSM-International trial from 16

international sites. Given the nature of the derivation and validation datasets, these two studies

represent the largest and most comprehensive evaluation of important clinical predictors of

surgical outcome in patients with CSM.308 The validation of this model will allow it to be

implemented in clinical practice, enabling surgeons to accurately quantify outcome, manage

their patient’s expectations and improve treatment strategies for CSM.

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8.2 Methods


Our sample consisted of 479 symptomatic CSM patients participating the CSM-

International study from 16 global sites.


Continuous predictors were summarized using means, standard deviations and ranges.

Categorical variables were described using frequencies. Demographics and surgical outcomes

were compared between the derivation (CSM-North America) and validation (CSM-

International) groups and across four geographical subsamples (Asia, Europe, Latin America and

North America).

Internal validity was assessed using a bootstrap re-sampling procedure with 200

individual bootstrap replicates. The original model was fitted to each replicate and mean odds

ratios and 95% confidence intervals were generated for each predictor. The area under the

receiver operating curve (AUC) was computed across the bootstrap replicates and compared to

the AUC for the original model. A Chi-square test was used to statistically evaluate the

differences in areas. The model is internally valid if the mean odds ratios for the replicates are

contained within the confidence intervals of the original estimates and if there is no significant

difference between the AUCs.

The external validity of the prediction rule was evaluated by determining the

performance of the original model on a second dataset collected from the CSM-International

study. A model’s discrimination refers to how accurately it can distinguish between two groups:

in the case of this study, between patients with a postoperative score ≥16 and those with a

score <16. A model’s calibration reflects how closely the predicted probabilities match the

observed outcomes.

The discrimination of the model was assessed by computing the AUC for the validation

model and statistically comparing it to the AUC for the original model. If there is no significant

difference between the AUCs then we can conclude the model is equally valid internationally as

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it is in North America. An AUC of 1.0 reflects perfect discrimination and 0.5 indicates zero

discrimination.

The model’s calibration was evaluated by first predicting the probabilities of an

“optimal” outcome and then dividing the patients into groups of ten based on their rank-order.

The mean predicted probability was computed for each group of ten and compared to the

proportion of patients that actually achieved a score ≥16 in that group. The observed versus

predicted probabilities were graphed to create a calibration plot and the slope and y-intercept

were calculated. A calibration plot with a slope of one and a y-intercept of zero represents

perfect calibration.331 The value of the intercept indicates whether the predictions are too high

or too low.332, 333 A slope value smaller than one reflects overfitting and can indicate a need to

shrink regression coefficients.333 The observed:expected ratios of probabilities with 95%

confidence intervals were averaged across the groups to determine whether there was a

significant difference between observed and expected outcomes. Finally the Hosmer-

Lemeshow test was conducted to evaluate the “goodness-of-fit” of the model. The model has

excellent fit in the international population if it has a calibration slope and an

observed:expected ratio close to one and a non-significant goodness of fit test. This indicates

that the model is able to successfully predict outcome in an external population.

The model was then refitted on the CSM-International dataset to examine differences in

parameter estimates, odds ratios, and model performance.334 Differences in the AUC were

compared using a Chi-square test.

8.3 Results


Of the 479 participants of the CSM-International study, eight had a pre-operative mJOA

score of 18 and were excluded from this analysis. Three hundred and ninety-seven patients

were evaluated at 1-year postoperatively, yielding an overall follow-up rate of 84.5%. Missing

follow-up data were assumed to be missing at random and were replaced with a set of

plausible values using a multiple imputation procedure with ten iterations. This procedure was

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used as the results are likely to be less susceptible to bias and it is more efficient than removing

patients with incomplete variables.324-326 Table 8-1 provides a summary of relevant baseline

characteristics and 1-year post-operative mJOA scores for the CSM-North America and CSM-

International study populations and for the geographic subsamples of the International dataset.

There was no difference in mean baseline severity score between the CSM-North

America derivation group, the CSM-International validation cohort and the regional subgroups.

North American patients from the CSM-International study were, on average, older

(59.60±11.63) than the derivation group (56.46±11.75) and those from the Asian (54.18±12.13)

and Latin American (54.14±10.81) subsamples were, on average, younger. Similar proportions

of patients smoked and had impaired gait in all groups. Interestingly, the percentage of patients

with psychiatric co-morbidities was significantly smaller in the CSM-International dataset than

in the CSM-North America study: 24% of patients in North America were diagnosed with

depression or bipolar disorder whereas only 7.9% of the international sample had clinically

defined psychiatric disease. Of the 37 patients in the CSM-International population that were

identified as having psychiatric co-morbidities, 31 were from North American sites,

representing a similar prevalence to what was reported in the derivation group (25%). Finally,

with respect to outcome, the mean post-operative score was significantly lower in the

validation population (14.91±2.68) than in the derivation group (15.69±2.54). Patients from

European (14.21±2.75) and Latin American (14.45±2.86) had, on average, a lower postoperative

mJOA score than patients from the derivation group.

8.3.2 Original Model

The original North American prediction model, derived from the CSM-North American

dataset, consisted of seven predictors: baseline mJOA, duration of symptoms, smoking status,

psychiatric co-morbidities, age, impaired gait and transverse area. Based on recent findings

from a systematic review (Chapter 3), transverse area was removed from the model as there is

insufficient evidence supporting its importance as a predictor and because it was also

statistically insignificant in our analysis.335 A summary of the modified model along with odds

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ratios and 95% confidence intervals for each covariate are provided in Table 8-2. The new

model used to quantify the probability of a mJOA≥16 is presented in equation 8-2:

(Equation 8-2)



smoker (1); and DS is duration of symptoms (1-5, see chapter 5/7).

Table 8-1. General Characteristics of the CSM-North America and the CSM-International study

Derivation Group (n=272)

Temporal Validation Group

North America Subsample

Europe Subsample

Asia Pacific Subsample

Latin America Subsample

Setting (sites per country or region)

11 United States, 1 Canada

2 North America, 3 Latin America, 6 Asia, 5 Europe

1 United States, 1 Canada

2 Italy, 1 Ireland, 1 Turkey, 1 Netherlands

1 China, 1 India, 1 Singapore, 3 Japan

2 Brazil, 1 Venezuela

Inclusion Period Sept. 2005-Dec. 2007

Oct. 2007- Jan. 2011

Oct. 2007- Jan. 2011

Oct. 2007- Jan. 2011

Oct. 2007- Jan. 2011

Oct. 2007- Jan. 2011

Age (range) 56.46±11.75 (29-86)

56.49±11.92 (21-87)

59.60±11.63 (37-87)*

57.64±11.88 (26-86)

54.18±12.13 (28-86)*

54.14±10.81 (21-78)*

Duration of symptoms (range)

26.14±45.89 (0.5-432)

27.17±34.90 (0.25-240)

28.29±36.60 (0.25-240)

25.16±32.78 (0.5-240)

22.17±35.88 (0.25-182)

38.25±31.38 (1-144)*

Baseline severity score (range)

12.71±2.64 (3-17)

12.41± 2.79 (3-17)

12.26±2.39 (6-17)

12.80±2.87 (6-17)

12.22±2.91 (3-17)

12.38±.02 (4-17)

Psychiatric Disorders† Grade 1 Grade 2 Grade 3 Unspecified

66/206 44 1 0 21

37/432** 33 3 0 1

31/92 27 3 0 1

2/121** 2 0 0 0

3/143** 3 0 0 0

1/76* 1 0 0 0

Smoking Status (Y/N) 71/201 128/343 37/86 38/85 32/116 21/56

Impaired Gait (Y/N) 206/65 362/109 95/28 90/33 114/34 63/14

Postoperative severity score (range)

15.69±2.54 (6-18)

14.91±2.68 (5-18)*

15.29±2.41 (9-18)

14.21±2.75 (6-18)*

15.39±2.63 (6-18)

14.45±2.86 (5-18)*

The derivation group is the sample from the CSM-North American study. The validation group consists of all participants from the CSM-International study. *Means and **proportions were significantly different than in the derivation set. †Grade 1: Major depression or bipolar disorder controlled with medication; grade 2: uncontrolled major depression or bipolar disorder or schizophrenia controlled with medication.

𝑃 =𝑒1.59+(−0.81)𝑃𝑠+(0.19)𝑚𝐽𝑂𝐴0+(−0.036)𝐴+(0.91)𝐼𝐺+(−0.69)𝑆+(−0.27)𝐷𝑆

1 + 𝑒1.59+(−0.81)𝑃𝑠+(0.19)𝑚𝐽𝑂𝐴0+(−0.036)𝐴+(0.91)𝐼𝐺+(−0.69)𝑆+(−0.27)𝐷𝑆

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8.3.3 Internal Validation

The AUC for the original model was 0.77 (95% CI: 0.711, 0.824) and across the bootstrap

replicates was 0.77 (95% CI: 0.762, 0.772), reflecting good discrimination and internal validity

(Figure 8-1). The chi-square test, assessing the difference between the original and bootstrap

areas, yielded a p-value of 0.99. For each predictor, the mean odds ratios closely approximated

the ratios from the original model. In addition, the 95% confidence intervals generated from

the bootstrapping data were narrow and contained the original odds ratio for each covariate

(Table 8-2). Each variable was equally predictive in the original and bootstrapped models.

Table 8-2. Odds Ratios for Original North American Model and Bootstrap Model

Predictor Original Model Odds Ratio

Original Model 95% Confidence

Bootstrap Odds Ratio

Bootstrap 95% Confidence

Baseline mJOA 1.21 1.07, 1.37 1.21 1.20, 1.22

Duration of symptoms 0.76 0.59, 0.99 0.77 0.75, 0.78

Smoking Status 0.50 0.22, 1.14 0.52 0.49, 0.55

Psychiatric Disorders 0.44 0.22, 0.88 0.45 0.42, 0.47

Age 0.96 0.94, 0.99 0.96 0.96, 0.97

Impaired Gait 2.48 1.10, 5.57 2.48 2.32, 2.65

mJOA: modified Japanese Orthopaedic Association

Figure 8-1. Receiver Operating Curves for Original and Bootstrap Models A ROC curve plots the true positive rate against the false positive rate. An area under the curve of 1 indicates a perfect test, whereas an area of 0.5 (green line) indicates no discriminative ability. The area under the curves for the original (purple) and bootstrap (red) models were not significantly different from each other.

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8.3.4 External Validation

Figure 8-2. Receiver Operating Curves for the Original North American Model (red) and the Model Validated on the International Population (blue) The area under the curves for both the original and validated models were significantly different from 0.5 but were not significantly different from each other. Both models had adequate discrimination.

The AUC was reduced from 0.77 (95% CI: 0.711, 0.824) to 0.74 (95% CI: 0.691, 0.781)

when the model was fitted on the external validation dataset. Figure 8-2 illustrates the

difference between the two ROC curves.

As displayed by Figure 8-3-A, the original model was well calibrated: the

observed/expected points closely followed the 45⁰ line and the slope was 0.81 with an

intercept of 0.11. The calibration of the model run on the CSM-International dataset was

inferior to that of the original model: the points were more clustered and tended to deviate

upward from the 45⁰ line. Furthermore, the slope was 0.75 and the intercept was 0.23,

indicating that our model predicts a slightly better outcome than what is actually observed.

The observed:expected ratios were very close to one for the original model, with

confidence intervals spanning one (1.02, 95%CI: 0.91, 1.15). This indicates that there was no

significant difference between observed and expected results. For the CSM-I validation set,

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however, the observed:expected ratio was statistically significant (0.79 (95% CI: 0.72, 0.86)),

indicating discrepancies between observed and expected outcomes. Analysis of goodness-of-fit

by the Hosmer-Lemeshow test suggested that both the original and validated model had good

fit (p>0.05) (Table 8-3).

Figure 8-3: Calibration Plots. A (top): Original model; B (bottom): Validated Model Each red dot reflects the predicted versus observed probability for each group of ten. The vertical stripes at the lower horizontal border represent the predicted probabilities of patients who did not achieve a mJOA≥16. The vertical stripes at the upper horizontal border reflect the predicted probabilities of the patients who achieved a successful surgical outcome. The 45⁰ black line indicates perfect calibration.

y = 0.747x + 0.2295

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Table 8-3. Calibration of the Original and CSM-International Validation Models

Observed:Expected Ratio (95% CI)

Calibration Slope, y-intercept

Hosmer-Lemeshow Chi-Square Test (Chi-square, p-value)

Original Model 1.02 (0.91, 1.15) 0.811, 0.11 2.85-12.99 (0.94-0.11)

CSM-I Validation Model 0.79 (0.72, 0.86) 0.75, 0.23 5.09-14.75 (0.75-0.064)

CSM: cervical spondylotic myelopathy; CI: confidence interval

With the exception of smoking status (p=0.097), the odds ratios for the modified original

model were all statistically significant, with 95% confidence intervals that did not span one.

When rerunning the multivariable model on the International dataset, the odds ratios for

baseline severity score (ORCSM-I: 1.26, 95%CI: 1.15, 1.39), age (ORCSM-I: 0.97, 95%CI: 0.95, 0.99),

impairment of gait (ORCSM-I: 2.67, 95%CI: 1.45, 4.92) and smoking status (ORCSM-I: 0.55, 95%CI:

0.34, 0.90) were very similar, with confidence intervals containing the original odds ratio. The

major difference between the original and International models was the impact of psychiatric

co-morbidities (ORCSM-I: 1.00, 95%CI: 0.46, 2.17 versus OROriginal: 0.44, 95%CI: 0.22, 0.88) and

duration of symptoms (ORCSM-I: 0.94, 95%CI: 0.80, 1.11 versus OROriginal: 0.76, 95%CI: 0.59, 0.99).

The odds ratios for these two predictors were not significantly different from one. However,

when comparing the AUC between the two models, there was no significant difference in the

predictive performance (AUCOriginal: 0.77, AUCCSM-I: 0.76, p=0.88). The confidence intervals for

the AUC of the International model contained the area yielded by the original North America

model (Table 8-4).

Table 8-4. Refitting the Original Logistic Regression Model on the CSM-International Sample

Predictor Original Model CSM-International

Odds Ratio (95% CI) Baseline mJOA Duration of symptoms Smoking Status Psychiatric Disorders Age Impaired Gait

1.21 (1.07, 1.37) 0.76 (0.59, 0.99) 0.50 (0.22, 1.14) 0.44 (0.22, 0.88) 0.96 (0.94, 0.99) 2.48 (1.10, 5.57)

1.26 (1.15, 1.39) 0.94 (0.80, 1.11) 0.55 (0.34, 0.90) 1.00 (0.46, 2.17) 0.97 (0.95, 0.99) 2.67 (1.45, 4.92)

p-values Baseline mJOA Duration of symptoms Smoking Status Psychiatric Disorders Age Impaired Gait

0.0026 0.0392 0.0972 0.0202 0.0081 0.0285

<0.0001 0.497 0.0179 0.998 0.0026 0.0017

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Area under ROC curve (95% CI) 0.77 (0.71, 0.82) 0.76 (0.72, 0.81)

Chi-square test, p-value AUC (Ref=Original Model)

N/A 0.88

CI: confidence interval; ROC: receiver operating curve; N/A: Not applicable

8.4 Discussion

A clinical prediction model should not be used to guide clinical practice until it has been

validated in other populations. This study aimed to evaluate the external validity of our North

American prediction model and assess whether it could be implemented into clinical practice.

The model was originally constructed using data on 272 patients enrolled in the prospective,

multicenter CSM-North America study and was later tested on a second dataset of 471

international patients. The performance of a model in another population, especially one from

a different region, may be inferior to that of the original model due to differences in patient

characteristics, health care systems, management strategies and methods of outcome

assessment. Although the inclusion criteria was identical for the North American and

International studies, there may exist regional differences in the causative pathology of

myelopathy, frequency of co-morbidities, and average ages, baseline severity scores and

duration of symptoms that could negatively impact our model’s validity. For example, in the

context of CSM, epidemiological studies have revealed that the prevalence of OPLL and

congenital stenosis is significantly higher in the Japanese population than in North America.

Therefore the pathology of the disease may differ across international subsamples and could

significantly influence surgical results. Furthermore, as reflected by our descriptive statistics,

there are significantly fewer patients with reported psychiatric co-morbidities in Asia, Latin

America and Europe than in North America. This low incidence likely reflects reporting bias,

cultural reluctance to admit to mental illness or surgical selection rather than actual incidence

of depression/bipolar disorder.

The predictive performance of the model when applied to the International dataset

decreased from 0.77 to 0.74. This difference was minimal. In addition, an area of 0.74 still

indicates that the model has a “good” ability to discriminate between patients who will achieve

an “optimal” surgical outcome and those who will not. Despite a decrease in predictive

performance, this model is still clinically useful, especially since it was originally constructed

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using both statistical and clinical criteria. Although most of the variables included in our

prediction model were statistically significant, the majority were also deemed clinically relevant

by existing evidence in the literature and consensus among professionals. When the model was

rerun on the International dataset, the odds ratio for duration of symptoms was 0.94 and not

significant different than one (p=0.497). However, there is ample evidence suggesting a longer

duration of symptoms is associated with a worse outcome. We therefore suggest that duration

of symptoms should still be included in our international prediction model regardless of its p-

value and that clinicians should always consider this factor when evaluating their patients.

The calibration of the original model was excellent, indicating that the predicted

probabilities closely match the actual outcomes. The Hosmer-Lemeshow test yielded

insignificant results for the model built on the original dataset and on the validation cohort,

reflecting overall “goodness-of-fit.” This demonstrates a strong ability of this clinical prediction

model to accurately predict outcome in future populations.

Based on this study, the key predictors of surgical outcome at a global level are age,

baseline severity score, smoking status and presence of impaired gait. Psychiatric disorders and

duration of symptoms were less important in the international sample than in North America.

This finding, however, is likely due to the low reliability of these two variables and to possible

underreporting of psychiatric disorders in all other locations except for North America. This

knowledge should promote more timely management of CSM, preoperative smoking cessation

and control of co-morbidities and operative intervention on milder patients. In addition, this

information can be used by clinicians to manage their patients’ expectations and provide

counsel where necessary.

8.5 Applying the Model

In a surgical CSM population, a clinical prediction model can be used by surgeons to 1)

manage patients’ expectations; 2) counsel patients and their families about potential treatment

options; 3) identify ways to optimize outcomes; and 4) align their perceptions of outcome with

more objective evidence. In addition, this information can be used by healthcare providers to

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anticipate future costs and allocate resources according. The following cases highlight how this

clinical prediction model can be used in these four ways to improve patient care.

8.5.1 Managing Expectations

To achieve optimal results, patients must have appropriate and realistic expectations of

their outcome.114, 336-338 Patients who achieve their expected outcome are likely to be more

satisfied with their treatment than those with unrealistic expectations. Expectations are often

dictated by knowledge obtained from healthcare providers, the internet or personal contacts

and may be influenced by psychological factors, educational level and a patient’s trust for his

surgeon.114 A prediction model can help surgeons quantify a patient’s likely outcome and

provide an accurate and objective estimate of how that patient is expected to fare. This will

help manage expectations and ultimately improve overall satisfaction.

The following two cases demonstrate how predicting outcome before surgery can aid in

expectation management.

Case 1: A 49-year old non-smoking male presented with moderate myelopathy (mJOA=14)

secondary to spondylosis, disc herniation and congenital stenosis. This patient had numb and

clumsy hands, muscular weakness, corticospinal motor deficits, hyperreflexia and upgoing

plantar responses. The duration of symptoms was two months. The patient also had coexisting

moderate hypertension, mild respiratory disease and mild diabetes.


secondary to spondylosis, disc herniation and hypertrophied ligamentum flavum. This patient

had numb and clumsy hands, impaired gait, muscular weakness, corticospinal distribution

motor deficits, hyperreflexia, positive Hoffman sign, upgoing plantar responses and broad-

based unstable gait. The duration of symptoms was 120 months. The patient had a mild stroke.

(Figure 8-4).

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Figure 8-4. Applying the Clinical Prediction Model in a Surgical Setting: Case 2

Based on our prediction model, case 1 has a 92.7% probability of improving to a mJOA

score ≥16 whereas case 2 only has a 41.0% chance of achieving this outcome. These patients

should be managed differently during the surgical consent process. A surgeon should inform

both patients that they are likely to improve following surgery but should notify case 2 that he

will still have residual neurological impairment. This information will help manage case 2’s

expectations and will likely improve his overall satisfaction. With respect to actual outcomes,

case 1 was neurologically normal postoperatively (mJOA=18) whereas case 2 improved from 13

to 15 but did not reach a score ≥16.

8.5.2 Counseling Patients

Patients with mild myelopathy with few symptoms may be hesitant to undergo surgery.

This prediction model can be used to counsel these patients and convince them that surgery is

likely their best treatment option. A surgeon can inform concerned patients that they have a

progressive, degenerative spine disease and that their outcome of surgery is optimal if they are

operated on earlier and at a milder disease stage.

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Case 3 provides an example how a surgeon can use a clinical prediction rule to counsel patients

during the surgical consent process.

Case 3: A 53-year old non-smoking male presented with mild myelopathy (mJOA=17)

secondary to spondylosis and disc herniation. This patient had numb and clumsy hands,

bilateral arm paresthesia, muscular weakness and atrophy of intrinsic hand muscles. The

duration of symptoms was four months. The patient had unspecified endocrine co-morbidities.

(Figure 8-5).


This case is an example of a patient who has mild myelopathy, a short duration of

symptoms and an excellent surgical prognosis. However, he may be reluctant to consent to

neurosurgery for such mild symptoms. This prediction rule can help surgeons counsel this

patient and inform him that if we wait to operate, his symptoms will likely progress and his

outcome will be suboptimal.

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8.5.3. Influencing Practice

In order for this prediction model to influence practice, we must distinguish between

the variables clinicians have control over and those that cannot be changed. In our prediction

model, the only predictor that is unchangeable is a patient’s age. The other five factors can be

modified:

1) Duration of Symptoms: A longer duration of symptoms is associated with a worst

postoperative outcome. However, this can be changed if clinicians choose to operate

earlier on CSM patients rather than waiting for the disease to progress. This requires

early disease recognition at the level of primary care physicians and early referral for

surgical consultations by either neurologists, rheumatologists or general practitioners.

There should also be an increased emphasis at the medical school level on how to

identify patients with CSM and differentiate between it and other diagnoses such as

carpal tunnel syndrome, multiple sclerosis and vitamin B12 deficiency.48

2) Baseline Severity Score: More severe preoperative myelopathy is predictive of a worse

outcome. This too can be changed through early detection of CSM and by choosing to

operate on milder patients.

3) Smoking Status: It is unclear why smokers have a worse postoperative outcome

compared to non-smokers. Possible explanations include that smoking results in

decreased fusion rates302 and poor healing capacity or that it is a surrogate for lower

socioeconomic status, poorer dietary choices and less access to postsurgical care.

Although the reasoning behind this predictor is unclear, surgeons should still promote

smoking cessation prior to surgery to optimize outcomes.

4) Psychiatric Disorders: Patients with depression or bipolar disorder do not perform as

well following surgery. Some surgeons will preselect their patients based on their

mental state and will choose not to operate on those with psychiatric co-morbidities.

Given the impact on outcome, it may be valuable to ensure that these disorders are

controlled or improved prior to operation.

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5) Impaired Gait: Impaired gait typically reflects a more severe state of myelopathy and is

predictive of a worse outcome. This factors is related to duration of symptoms and

baseline severity score: if a patient is operated on earlier and at a milder stage, he/she

would likely not have progressed to presenting with more severe symptoms of gait

dysfunction.

From this example, it is clear that clinical prediction models can influence practice,

inform policy changes and impact medical school curriculum. In the context of CSM, it is

essential that clinicians be able to identify this disease at early stages and that surgeons

consider operating on milder patients with a shorter duration of symptoms.

8.5.4 Aligning Surgeon Perceptions with Objective Evidence

Surgeons often have different perceptions as to how a patient will fare following

surgery. As the surgeon often influences a patient’s expectations, it is important that his

estimate of outcome is based on objective evidence and is as accurate as possible. A clinical

prediction rule can help a surgeon predict outcome and use this information during the surgical

consent discussion. Case 4 provides an example of how a clinical prediction rule is useful in this

regard.


secondary to spondylosis and disc herniation. This patient had numb and clumsy hands,

impaired gait, muscular weakness and atrophy of intrinsic hand muscles. The duration of

symptoms was 60 months. The patient had coexisting mild respiratory disease. (Figure 8-6).

For each patient, the surgeon was asked how he expected the subject to do following

surgical intervention: improve from baseline status, remain the same or worsen. For case 4, the

surgeon believed the subject would be the same as baseline. Our prediction model predicted an

83.2% chance the patient would achieve a score ≥16 and therefore improve by at least two

points on the mJOA. The patient did indeed improve following surgery and was neurologically

normal at 1-year follow-up. This example demonstrates how a prediction rule can align

surgeons’ perceptions with more objective evidence.

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The major strength of this study is that the case report forms for the CSM-North

American and CSM-International studies were identical. Data for the variables included in the

original model were available for all patients enrolled in the CSM-International study. This

enabled validation to be done without having to develop proxy variables.331 The presence of 12

North American and 14 International recruitment sites allowed for the accrual of 272 patients

for model building and internal validation and 471 patients for external validation. These large

sample sizes, the consecutive recruitment pattern and the prospective nature of the datasets

distinguish this current study from other past CSM studies. Finally, the presence of ongoing

external monitoring for both the International and North American studies has ensured

collection of high quality and reliable data.

One of the limitations associated with our data was that we were not able to test the

validity of the model in each geographic subsample due to a low frequency of psychiatric co-

morbidities at sites outside of North America. This low prevalence could be due to

underreporting and poor reliability of this variable or may reflect sociocultural perceptions of

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illness. In addition, there was a 20% and 18% attrition rate observed in the North America and

International studies, respectively at the one-year time point. There were, however, no key

demographic differences between the patients who dropped out and those who did not.

Furthermore, we accounted for missing data using a multiple imputation procedure which is

statistically recommended and the preferred method for handling missing data in therapeutic

trials. Finally, the reliability of the mJOA has not been tested. Although the interobserver and

intraobserver reliabilities of the JOA are high, it cannot be assumed the mJOA carries the same

psychometric properties given the substantial differences between the two scales. Low

reliability of any variable, including baseline mJOA, can lead to large measurement error and a

poor ability to predict outcome. Furthermore, the mJOA has only demonstrated translatability

between English and Dutch.339 Given that the participants did not necessarily speak English,

accurate reporting of outcome data may have been affected by language barriers.

8.7 Conclusions

We have developed a clinical prediction model to determine the probability of an

“optimal” outcome in patients undergoing surgical treatment for CSM. The odds ratios for each

covariate are internally valid as determined by a bootstrap resampling procedure. The

discrimination for the original model was good, with an area under the ROC curve of 0.77. The

calibration was excellent. When testing this model on a second external dataset, it had good

discrimination and adequate calibration. Given its internal and external validity, this model will

be of great value in a clinical setting.

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Chapter 9: Does Magnetic Resonance Imaging Improve the Predictive

Performance of our Validated Clinical Prediction Rule

9.1 Introduction

This chapter provides an overview of the methodology and results of a study that

explored the predictive value of MRI. This chapter is part 3 of our 4-part prediction study.

MRI is the globally accepted method of evaluating patients with CSM as it can determine

the severity of degenerative changes, quantify the degree of cord compression due to canal

stenosis and detect any intrinsic spinal cord abnormalities.33, 34 Furthermore, MRI can

effectively distinguish between CSM and other pathologies such as epidural abscesses,

neoplasms and demyelinating plaques.32

In addition to its diagnostic utility, MRI may play a role as a prognostic indicator for

CSM. In our survey of AOSpine International members, 86% of spine professionals agreed that

MRI is valuable in predicting surgical outcome in these patients.340 Furthermore, our systematic

review341 reported that a hyperintense signal on a T2WI is a valuable predictor when used in

combination with a hypointense signal on a T1WI; as a ratio comparing compressed versus non-

compressed segments; or as a ratio of T2 to T1 signal change.341 In contrast, certain anatomical

measurements were not associated with outcome, including compression ratio at the level of

maximum spinal cord compression (MSCC) or transverse area.

In a study by Nouri et al (2014), a model was developed to predict an “optimal” surgical

outcome (mJOA≥16) using only MRI parameters and preoperative myelopathy severity. This

model was constructed using a subset (n=102) of patients enrolled in the CSM-North America

study and included a hypointense signal change on T1-WI (OR: 0.242, p=0.029), maximum canal

compromise (MCC) (OR: 0.94, p=0.005) and baseline mJOA (OR: 1.74, p<0.001). The area under

the receiver operating curve was 0.845 which is higher than the discriminative ability of our

original North American prediction model. Given these findings, we hypothesized that there

may be potential to further improve the validity of our prediction model by incorporating

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certain imaging factors. This study aims to determine whether the addition of MRI parameters

enhances the predictive performance of our original, validated model.

9.2 Methods


We were only able to obtain images for 149 of the 278 patients as MRI data collection

was not a prerequisite for the original CSM-North America study. Of these 149 patients, 35

were excluded due to incompatible imaging formats, imaging artifacts/poor image quality and

other factors preventing quantitative analysis. Ninety-nine patients were evaluated at 1-year

postoperatively and comprised the cohort for this analysis (Figure 9-1).

Figure 9-1. An Overview of our Patient Sample derived from the CSM-North America Study


The validity of the original model on the smaller dataset (n=99) was evaluated by

computing the AUC and comparing this value to the area of the original model. Each MRI

parameter was added to the model individually and the AUC and 95% confidence intervals were

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computed. This area was compared to the area of the original model using a simple Chi-square

test. A p-value <0.05 indicates that the discriminative ability of the model with the imaging

variable is significantly greater than that of the original model with only clinical predictors. ROC

curves were plotted to visualize the contribution of each MRI parameter to the predictive

performance of the original model.

9.3 Results

Our cohort consisted of 61 men (61.62%) and 38 women (38.38%), with ages ranging

from 29 to 86 years (mean age=55.88±11.73). Patients had a wide range of preoperative

severities (mJOA=5-18), and a mean baseline mJOA score of 12.86±2.80. Sixty-eight percent of

patients displayed a hyperintense region on a T2-WI, whereas only 27% had a hypointense

signal on a T1-WI. Average signal change ratios ranged from 0.43±0.13 using Arvin’s criteria to

1.50±0.42 using Wang’s criteria. The mean MSCC was 33.78%±15.35% and MCC was

48.55%±13.22%. Table 9-1 presents a complete summary of demographic and imaging

information for these patients. As illustrated by Figure 9-2, sixty percent achieved an outcome

≥16 and were either asymptomatic or mild post-operatively, whereas 40% still had substantial

residual neurological impairment (mJOA<16).

Figure 9-2. Summary of Functional Outcome at 1-year Post-Surgery Red indicates a score of greater than or equal to 16 (60%) and blue represents a score less than 16 (40%).

0

5

10

15

20

25

30

35

8 10 11 12 13 14 15 16 17 18

Freq

uen

cy

mJOA at 1-year postoperatively

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Table 9-1. Demographic and MRI Information of a Subset of 99 Patients Enrolled in the CSM-

North America Study


General Characteristics

Baseline severity score (mJOA) (n=99) 12.86±2.80 (5-18)

Age (n=99) (years) 55.88±11.73 (29-86)

Gender (n=99) (%) 61.62 M, 38.38 F

Duration of Symptoms (n=99) (months) 28.60±35.82 (2-240)

Imaging Characteristics

T1-hypointensity (n=93) (%) 26.88 Y, 73.12 N

T2-hyperintensity (n=96) (%) 67.71 Y, 32.29 N

Combined T1/T2 signal (n=87) (%) 29.89 Normal/Normal 45.98 Normal/High 24.14 Low/High

Height (cm) of T2 signal change (n=96) (%) I: height = 0 II: 0 < height ≤ 0.75 III: 0.75 < height ≤ 1.50 IV: 1.50 < height

32.29 20.83 21.88 25.00

Area (cm2) of T2 signal change (n=96) (%) I: area = 0 II: 0 < area ≤ 0.2 III: 0.2 < area ≤ 0.35 IV: 0.35 < area

32.29 30.21 21.88 15.63

Wang’s Ratio (n=96) 1.50±0.42 (0.91-3.10)

Arvin’s Ratio (n=96) 0.43±0.13 (0.21-0.92)

Aria’s Ratio (n=96) 1.43±0.38 (0.90-2.97)

Spinal Canal Compromise (n=94) (%) 48.55±13.22 (16.12-75.33)

Spinal Cord Compression (n=94) (%) 33.78±15.35 (-4.60-64.86)

The original model was valid on the subsample and had an AUC of 0.811 (95% C.I.:

0.726, 0.896). The predictive performance of the original model was compared to that of

secondary models that included various imaging parameters. There were no significant

differences in the AUCs between the original model and those that included T2 hyperintensity,

T1 hypointensity, combined T1/T2 signal change, SCR, height or area of T2 signal hyperintensity,

MSCC and MCC (p=0.81-0.99). The largest improvement in discrimination was seen after height

of T2 signal hyperintensity was added to the original model (AUC: 0.826, 95% C.I.: 0.743-0.908,

1.5%). The addition of Wang’s SCR improved the predictive performance of the original model

by 1.2% to yield an AUC of 0.823 (95% C.I.: 0.739-0.907). Anatomic characteristics, such as

MSCC (-0.3%) and MCC (-1.5%) did not contribute to the discrimination of the original model.

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Table 9-2 provides details on the AUC, 95% confidence intervals and the percent change in

predictive performance of each modified model. Figures 9-3, 9-4, 9-5 and 9-6 illustrate the

differences in ROC curves between models.

Table 9-2. Predictive Performance of Original Model with the Addition of Various MRI Parameters

MRI Parameter Predictive Performance of Model (AUC)

95% Confidence Intervals of AUC

Change from Original Model AUC=0.811

P-value from Chi-square test

Cord Properties

T1-hypointensity 0.815 0.728-0.902 0.4% 0.95

T2-hyperintensity 0.815 0.729-0.901 0.4% 0.95

Combined T1/T2 signal 0.811 0.720-0.901 0.0% 0.99

Height of T2 signal change 0.826 0.743-0.908 1.5% 0.81

Area of T2 signal change 0.824 0.740-0.907 1.3% 0.84

Wang’s Ratio 0.823 0.739-0.907 1.2% 0.84

Arvin’s Ratio 0.813 0.727-0.899 0.2% 0.98

Nouri’s Ratio 0.819 0.734-0.904 0.8% 0.90

Anatomic Characteristics

MCC 0.796 0.704-0.887 -1.5% 0.81

MSCC 0.808 0.718-0.898 -0.3% 0.96

MCC: maximum canal compromise; MSCC: maximum spinal cord compression

Figure 9-3. ROC Curves of Original Model + T2 Hyperintensity, T1-Hypointensity or Combined

T1/T2 Signal Change

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Figure 9-4. ROC Curves of Original Model + Height or Area of T2 Signal Change

Figure 9-5. ROC Curves of Original Model + Spinal Canal Compromise or Spinal Cord

Compression

225

Figure 9-6. ROC Curves of Original Model + Signal Change Ratio

9.4 Discussion

In patients with CSM, MRI is primarily used to confirm the clinical diagnosis by providing

clear evidence of anatomical changes to the spine and spinal cord. There is, however, increasing

evidence suggesting the role of MRI goes beyond diagnosis and that some measurements carry

prognostic value. Based on this information, we hypothesized that a clinical prediction rule with

added MRI parameters would have superior predictive performance than either a clinical or

imaging based model. However, our results indicate that, in a cohort of surgical patients with

both neurological and image-evidence of CSM, MRI parameters do not significantly improve the

performance of our North American model (Chapter 8). We speculate that the MRI becomes

less sensitive in predicting outcome in this group of surgical patients as all participants had a

positive MRI and evidence of cord compression.

In our imaging systematic review, low level evidence suggested that only MRI factors

related to cord properties are significant predictors of outcome. Specifically, a combined T1/T2

signal change, a higher SCR, and a greater number of signal intensity segments on T2-WI are

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negatively associated with surgical outcome across several measurement scales. The rationale

behind these findings is that larger and more intense signal changes reflect severe, irreversible

histological damage, including cystic necrosis, secondary syrinx, myelomalacia and cavitation. In

this study, the two predictors that showed the greatest increase in predictive performance

were Wang’s SCR and height of T2-WI signal change. Although these results confirm the

findings of the systematic review, the marginal improvements in the AUCs were not statistically

significantly.

In a recent publication, Nouri et al (2014) determined that a model combining T1-WI

hypointensity, MCC and baseline mJOA had a very good (AUC=0.845) ability to predict outcome

at 6-months following surgery. Furthermore, according to a likelihood ratio test, this model was

superior to a model with only baseline severity score, concluding MRI has a significant role in

predicting outcome. While our current findings do not support these results, it remains

plausible that a combination of the strongest clinical and imaging predictors may provide

comparable or even superior performance to what has been individually reported by Tetreault

and Nouri. On the other hand, it is also possible that outcome prediction using these sets of

parameters reaches a ceiling with respect to discriminative ability.

A number of factors may prevent perfect or near perfect prediction in this setting. These

include 1) the heterogeneity of the CSM population, especially with respect to disease

presentation and causation; 2) the potential of surgical technique and complications to directly

influence surgical outcome; 3) the unknown reliability of certain factors, including myelopathy

severity, various MRI parameters and duration of symptoms; and 4) the use of a single outcome

measure (mJOA) to determine functional status. For future prediction analysis, some of these

factors can be addressed. We suggest all measurements are assessed for reliability and more

sensitive and specific outcome measures are used in addition to the mJOA, such as the GRASSP

or gait analysis.71, 342

It is also possible that additional predictive performance may be garnered from other

preoperative parameters that were not assessed in this study. In particular, up and coming MRI

techniques, such as DTI,239, 343 micro-vascular blood flow, functional MRI and magnetization

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transfer will provide better evaluation of the pathobiology of the spinal cord and an increased

sensitivity to detect histological changes. For example, damage to white matter tracts can be

identified by a DTI before a high signal change appears on a T2WI. In terms of prognostic

importance, fractional anisotropy values and fiber tract ratios have shown significant

correlation to surgical outcome341. The evaluation of CSM patients in upright positions and

using dynamic/kinetic MRI techniques may also be interesting to explore344. In addition, Class II

evidence suggests that SSEPs may have a role in predicting surgical outcome in CSM patients.116

Therefore, we recommend that future studies consider inclusion of more sensitive imaging

modalities as well as electrophysiological measurements.

The results of this study are not intended to devalue the role of MRI. Ultimately, it is the

primary imaging modality used to confirm a clinical diagnosis of CSM and to depict the

presence and extent of spinal canal narrowing, compression of the spinal cord and

degenerative changes of bone and soft tissue structures. Therefore, the inability of MRI

parameters to significantly improve the predictive performance of our original model does not

diminish its overall clinical value but suggests that, alternatively, more sensitive techniques are

necessary to uncover additional prognostic value.


This study explored both qualitative (presence/absence of signal change) and

quantitative imaging factors (height, area and ratios of signal change). The reviewer who

analyzed each MRI was blinded to the neurological and functional status of each patient,

reducing potential sources of bias. Finally, since our clinical prediction model was externally

validated, we could truly evaluate the predictive value of each added MRI parameter.

There are several limitations that need to be taken into consideration. First, our cohort

was only a subset of patients enrolled in the original CSM-North America study. Second, in the

study by Nouri et al (2014), MRI factors were predictive of outcome at 6-months and may be

less significant at 1-year despite the fact that patients are typically stable between 6-months

and 1-year following surgery. Furthermore, the sample only involved patients who were

clinically diagnosed with CSM and treated surgically. Since all participants had a positive image,

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the MRI becomes less sensitive in detecting abnormalities and therefore in predicting outcome.

Lastly, the reliability of some MRI measures is unknown; therefore findings of no significance

may be due to unreliability rather than a reflection of actual predictive value.

9.6 Conclusions

In a preselected sample of surgical patients, with both neurological and image-evidence

of CSM, MRI parameters do not significantly add to the predictive performance of our clinical

prediction model. It remains plausible however, that combinations of the strongest clinical and

MRI predictors may yield a prediction model of comparable or superior efficacy. Therefore, our

findings indicate that our clinical prediction rule and the MRI prediction model developed by

Nouri et al (2014) should be employed independently, and their collective results used for

prognostic guidance and surgical consultation.

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Chapter 10: A Clinical Prediction Rule for Functional Outcomes in

Patients Undergoing Surgery for Cervical Spondylotic Myelopathy:

Analysis of an International AOSpine Prospective Multicentre Dataset of

757 Subjects

10.1 Introduction

This chapter summarizes the methodology and results of part 4 of our 4-part prediction

study.

In part 1, we designed a clinical prediction rule to predict surgical outcomes in patients

with CSM using data on 272 patients enrolled in the prospective, multicenter AOSpine CSM-

North America study. This model was developed to distinguish between patients with mild

myelopathy at 1-year postoperatively (mJOA≥16) and those with substantial residual

neurological impairment (mJOA<16). Based on this model, patients were more likely to achieve

a score ≥16 if they were younger; had a shorter duration of symptoms and milder myelopathy;

did not smoke; and did not have depression/bipolar disorder or impaired gait. In part two, the

external validity of this prediction rule was examined using data from 479 participants of the

AOSpine CSM-International study. This model proved to be externally valid; however, it was

also evident that certain predictors were more relevant in North America than they were

globally. The most significant of these was the presence of psychiatric disorders which was

highly significant in the North American study but was irrelevant in the International study. This

was because there was a low reported incidence of depression and bipolar at sites outside of

North America, reflecting actual incidence, surgical selection bias or cultural reluctance to

report mental illness.

In the first two studies, other limitations were identified. One of the major concerns was

that the model was better suited to predict outcome in moderate (mJOA=12-14) and mild

(mJOA=15-18) myelopathic patients. In severe cases (mJOA<12), patients may exhibit

substantial improvements on the mJOA but are less likely to achieve a final score of 16 at 1-year

postoperatively. These patients would be classified as having a “suboptimal” outcome even if

their gains in functional status were clinically significant. It is the aim of this current study to

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address certain limitations in our prediction rule and to refine the original model to increase its

global validity.

10.2 Methods


Our sample consisted of 757 patients enrolled in either the CSM-North America or CSM-

International study from 26 global sites.


Continuous predictors were summarized using means, standard deviations and ranges.

Categorical variables were described using percentages. Univariate log-binomial regression

analyses were conducted to assess the relationship between various clinical factors and our

primary outcome measure and to estimate relative risks. Predictors that yielded a p-value <0.2

in univariate analysis were further examined in multivariate analysis. Variables that were

considered clinically relevant but had a p-value >0.2 were also assessed in multivariate analysis.

Multicollinearity evaluated by calculating tolerance. Modified Poisson regression using

robust error variances was used to create the final multivariate model and compute the relative

risk for each predictor. Variables were included in the final model if they were statistically

significant (p<0.05) and/or deemed clinically important by existing literature. Logistic regression

analysis was run on the final model to obtain a receiver operating characteristic curve (ROC). A

ROC curve plots the true positive rate against the false positive rate. The area under the curve

(AUC) indicates the predictive performance of the model: an area of 1 reflects a test with 100%

specificity and 100% sensitivity, whereas an area of 0.5 indicates no discriminative value.

These methods were repeated for just severe patients (mJOA<12) using a cut-off mJOA

score at 1-year of 12 (mJOA≥12, mJOA<12). A sensitivity analysis was also conducted defining

“severe” myelopathy as <11.

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10.3 Results


Four-hundred and seventy-nine patients were enrolled in the CSM-International study

and 278 in the CSM-North America study at 26 global sites (Figure 10-1). Of these 757

participants, 14 had a perfect preoperative mJOA score of 18 and were excluded from this

analysis. Six hundred and fourteen patients attended their 1-year follow-up visit and were

evaluated for improvements in functional status (82.6%).

Figure 10-1. Summary of Participating Subjects and Predictors Evaluated in this Study Predictors bolded in red were significant in the CSM-North America prediction study. Duration of symptoms 1) <3 months, 2) >3, ≤6 months, 3) >6, ≤12 months, 4) >12, ≤24 months, 5) >24 months; comorbidity score: mild disease=1, moderate disease=2, severe disease=3, the total score summates severity across all co-morbidity categories; symptoms, signs and co-morbidities were either present or absent. mJOA16: the number of patients who did/did not achieve a score of 16 on the mJOA at 1-year postoperative. mJOA12: the number of patients who did/did not achieve a score of 12 on the mJOA at 1-year postoperative. Change: 1-year postoperative mJOA – baseline mJOA

Our cohort consisted of 463 (62.31%) men and 280 (37.69%) women, with an average

age of 56.48±11.85 years (range 21-87 years). Patients exhibited a wide range of myelopathy

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severities from 3 to 17 points on the mJOA scale and a mean score of 12.52±2.74. One hundred

and ninety-three patients presented with mild (mJOA: 15-17) myelopathy, 296 with moderate

(mJOA: 12-14) and 254 with severe (mJOA<12) disease. With respect to degenerative diagnosis,

77.25% of patients displayed evidence of spondylosis, 71.74% of disc herniation and 24.23% of

a hypertrophied ligamentum flavum. A smaller percentage of patients presented with OPLL

(21.13%) and subluxation (5.79%). The mean duration of symptoms was 26.79±39.25 months,

with a range from 0.25 to 432 months. The most common signs on clinical assessment were

hyperreflexia (77.90%), corticospinal distribution motor deficits (62.80%) and a positive

Hoffman’s sign (62.67%) and the most common symptoms were numb hands (89.49%),

weakness (82.88%) and gait impairment (76.55%).

At 1-year follow-up, the mean mJOA score was 15.18±2.67, reflecting significant

improvements in functional status compared to baseline. Three hundred and twenty-four

(52.77%) patients achieved a score of 16 or greater and were mild postoperatively whereas 290

(47.23%) patients still had substantial residual neurological impairment (mJOA<16). The

majority of patients improved to a score greater than or equal to 12 (90.07%); however, 9.93%

still suffered from severe myelopathy postoperatively. Table 10-1 displays the demographic

information and 1-year outcomes for the entire cohort and for the severe patients (mJOA<12).

10.3.2 Predicting a mJOA score ≥16

10.3.2.1 Univariate Analysis:

Based on univariate analysis, the significant predictors of a mJOA≥16 were a higher

baseline mJOA score (RR: 1.15, 95%C.I.: 1.12-1.18); younger age (RR: 0.84, 95%C.I.: 0.80-0.88);

non-smoking status (RR: 0.82, 95%C.I.: 0.68-0.99); absence of cardiovascular co-morbidities (RR:

0.71, 95%C.I.: 0.60-0.84); a lower co-morbidity score (RR: 0.89, 95%C.I.: 0.83-0.94); absence of

clumsy hands (RR: 0.77, 95%C.I.: 0.66-0.89), impaired gait (RR: 0.57, 95%C.I.: 0.50-0.65),

bilateral arm paresthesia (RR: 0.82, 95%C.I.: 0.71-0.95) and general weakness (RR: 0.70,

95%C.I.: 0.60-0.81); and absence of corticospinal motor deficits (RR: 0.71, 95%C.I: 0.61-0.82),

hyperreflexia (RR: 0.83, 95%C.I.: 0.70-0.97), upgoing plantar responses (RR: 0.75, 95%C.I.: 0.63-

0.89), lower limb spasticity (RR: 0.72, 95%C.I.: 0.61-0.84) and broad-based unstable gait (RR:

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0.57, 95%C.I.: 0.49-0.66). The associations between this outcome and duration of symptoms,

gastrointestinal and endocrine co-morbidities, l’Hermitte’s phenomena and atrophy of intrinsic

hand muscles yielded p-values <0.20 and were evaluated in multivariate analysis. No other

variables with p-values >0.20 were deemed clinically significant and were not examined further.

(Table 10-2).

Table 10-1. Patient Baseline Demographic Information and 1-year Functional Outcomes following surgery for CSM

Variable Total Sample Severe Patients (mJOA<12)

Baseline severity score (mJOA) 12.52±2.74 (3-17) 9.42±1.67 (3-11)

Age (years) 56.48±11.85 (21-87) 60.09±12.06 (28-86)

Gender (male) 463 (62.31%) 153 (60.24%)

Duration of symptoms (months) 26.79±39.25 (0.25-432) 24.43±33.30 (0.25-240)

Smoker 199 (26.78%) 71 (27.95%)

Co-morbidities Co-morbidity Score Cardiovascular Respiratory Gastrointestinal Renal Endocrine Psychiatric Rheumatologic Neurological

459 (61.78%) 1.45±1.82 (0-13) 334 (44.95%) 80 (10.77%) 120 (16.15%) 22 (2.96%) 135 (18.17%) 103 (13.86%) 38 (5.11%) 47 (6.33%)

172 (67.72%) 1.77±1.99 (0-13) 141 (55.51%) 29 (11.42%) 35 (13.78%) 11 (4.33%) 55 (21.65%) 30 (11.81%) 18 (7.09%) 21 (8.27%)

Diagnosis Spondylosis Disc herniation OPLL HLF Subluxation

574 (77.25%) 533 (71.74%) 157 (21.13%) 180 (24.23%) 43 (5.79%)

204 (80.31%) 178 (70.08%) 57 (22.44%) 73 (28.74%) 17 (6.69%)

Symptoms (n=742) Numb hands Clumsy hands Impaired gait Bilateral arm paresthesia L’Hermitte’s phenomena General weakness

664 (89.49%) 556 (74.93%) 568 (76.55%) 422 (56.87%) 198 (26.68%) 615 (82.88%)

240 (94.86%) 225 (88.93%) 244 (96.44%) 169 (66.80%) 74 (39.25%) 235 (92.89%)

Signs (n=742) Corticospinal motor deficits Atrophy of intrinsic hand muscles Hyperreflexia Positive Hoffman’s sign Upgoing plantar responses Lower limb spasticity Broad-based unstable gait

406 (62.80%) 268 (36.12%) 578 (77.90%) 465 (62.67%) 266 (35.85%) 353 (47.57%) 442 (59.57%)

196 (77.47%) 120 (47.43%) 207 (81.82%) 168 (66.40%) 124 (49.01%) 161 (63.64%) 209 (82.61%)

mJOA score at 1-year 15.18±2.66 (5-18) 13.73±2.90 (5-18)

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mJOA≥16 (n, (%)) mJOA<16

324 (52.77%) 290 (47.23%)

64 (31.84%) 137 (68.16%)

mJOA≥12 (n, (%)) mJOA<12

553 (90.07%) 61 (9.93%)

154 (76.62%) 47 (23.38%)

Continuous variables are described using means ± standard deviations and ranges. Baseline categorical variables are summarized as frequencies and percentages. Follow-up scores are given as frequencies and percentages. mJOA: modified Japanese Orthopaedic Association; OPLL: ossification of the posterior longitudinal ligament; HLF: hypertrophy of the ligamentum flavum.

Table 10-2. Univariate Analyses Evaluating the Association between Various Clinical Predictors and a mJOA Score ≥16 at 1-year following Surgery

Predictor

Relative Risk

95% C.I.

p-value


Age (by decade)* 0.84 0.80, 0.88 <0.0001

Gender (REF=Female) 0.91 0.78, 1.06 0.21

Duration of symptoms† 0.96 0.91, 1.01 0.14

Smoking status (REF=No) 0.82 0.68, 0.99 0.038

Co-morbidity Score Co-morbidities (REF=absence) Cardiovascular Respiratory Gastrointestinal Renal Endocrine Psychiatric Rheumatologic Neurological

0.89 0.79 0.71 0.80 1.15 1.01 0.82 0.93 0.91 0.84

0.83, 0.94 0.68, 0.92 0.60, 0.84 0.60, 1.07 0.95, 1.38 0.62, 1.63 0.66, 1.03 0.74, 1.16 0.64, 1.31 0.59, 1.20

<0.0001 0.0017 <0.0001 0.13 0.14 0.96 0.091 0.51 0.63 0.34

Symptoms (REF=absence) Numb hands Clumsy hands Impaired gait Bilateral arm paresthesia L’Hermitte’s phenomena General weakness

0.91 0.77 0.57 0.82 0.87 0.70

0.73, 1.13 0.66, 0.89 0.50, 0.65 0.71, 0.95 0.72, 1.04 0.60, 0.81

0.39 0.0007 <0.0001 0.0094 0.14 <0.0001

Signs (REF=absence) Corticospinal motor deficits Atrophy of intrinsic hand muscles Hyperreflexia Positive Hoffman’s sign Upgoing plantar responses Lower limb spasticity Broad-based unstable gait

0.71 0.90 0.83 0.92 0.75 0.72 0.57

0.61, 0.82 0.76, 1.05 0.70, 0.97 0.79, 1.07 0.63, 0.89 0.61, 0.84 0.49, 0.66

<0.0001 0.18 0.020 0.30 0.0011 <0.0001 <0.0001

Co-morbidity score is comprised of both number and severity of co-morbidities. A 1-point increase reflects either

an increase in disease severity or number of co-morbidities. Relative risk for each variable was calculated using log-

binomial regression. *Relative risk for age is by decade. †Relative risk for duration of symptoms is by group (1) <3

months, 2) >3, ≤6 months, 3) >6, ≤12 months, 4) >12, ≤24 months, 5) >24 months). C.I.: confidence interval; mJOA:

modified Japanese Orthopedic Association

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10.3.2.2 Multivariate Analysis

The final model consisted of six statistically significant and clinically relevant predictors:

baseline severity score (RR: 1.11, 95%C.I.: 1.07-1.15), impaired gait (RR: 0.76, 95%C.I.: 0.66-

0.88), age (RR: 0.91, 95%C.I.: 0.85-0.96), co-morbidity score (RR: 0.93, 95%C.I.: 0.88-0.98),

smoking status (RR: 0.78, 95%C.I.: 0.65-0.93) and duration of symptoms (RR: 0.95, 95%C.I.:

0.90-0.99). (Table 10-3).

Based on relative risks, the probability of achieving a score ≥16 on the mJOA i) decreases

by 5% when a patient has an increased duration of symptoms (i.e moves from the ≤3 month to

the 3-6 month group); ii) is 1.11 times greater for each point increase in baseline mJOA score;

iii) decreases by 9% for every decade increase in age; iv) decreases by 7% for each one point

increase in co-morbidity score (either an increase in disease severity or number of co-

morbidities); v) decreases by 24% when a patient presents with gait impairment (versus no

impairment); and vi) decreases by 22% when a patient smokes. The AUC for this model was

0.77 (95%C.I.: 0.73, 0.80), reflecting good discriminative ability.


following Surgery

Predictor Relative Risk 95% C.I. p-value


Impaired gait (REF=absence) 0.76 0.66, 0.88 0.0002

Age 0.91 0.85, 0.96 0.0015

Co-morbidity score 0.93 0.88, 0.98 0.0067

Smoking status (REF=non-smoker) 0.78 0.65, 0.93 0.0056


This model serves to distinguish between patients with mild myelopathy postoperatively (mJOA≥16) and those with substantial residual neurological impairment (mJOA<16). Relative risk for each covariate was computed using Poisson regression. Baseline severity score: 0-18 points; age is per decade; co-morbidity score is comprised of both number and severity of co-morbidities; duration of symptoms 1) <3 months, 2) >3, ≤6 months, 3) >6, ≤12 months, 4) >12, ≤24 months, 5) >24 months. C.I.: confidence intervals; mJOA: modified Japanese Orthopedic Association.

10.3.3 Predicting a mJOA score ≥12

10.3.3.1 Univariate Analysis

Baseline severity score (RR: 1.07, 95%C.I.: 1.02-1.13), hyperreflexia (RR: 0.83, 95%C.I.:

0.72-0.96), lower limb spasticity (RR: 0.75, 95%C.I.: 0.65-0.86), and age (RR: 0.97, 95%C.I.: 0.95-

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0.99) were significant predictors of a mJOA≥12 following univariate analysis. The relationships

between a score ≥12 and duration of symptoms, co-morbidity score, cardiovascular and

respiratory co-morbidities, numb hands, bilateral arm paresthesia, L’Hermitte’s phenomena,

positive Hoffman’s sign and broad-based unstable gait also yielded p-values <0.20 and were

further evaluated. No other variables with p-values >0.20 were deemed clinically significant and

were not examined in multivariate analysis. (Table 10-4).


and a mJOA score ≥12 at 1-year following Surgery in Patients with Severe CSM (mJOA<12).


Baseline Severity Score 1.07 1.02, 1.13 0.010

Age* 0.97 0.95, 0.99 0.0014

Gender (REF=Female) 0.98 0.84, 1.14 0.81

Duration of symptoms† 0.96 0.92, 1.01 0.087

Smoking status (REF=non-smoker) 1.00 0.85, 1.19 0.97

Co-morbidities (REF=absence) Co-morbidity Score Cardiovascular Respiratory Gastrointestinal Renal Endocrine Psychiatric Rheumatologic Neurological

0.97 0.96 0.89 0.79 0.95 1.15 1.05 0.94 0.93 0.98

0.83, 1.14 0.92, 1.01 0.76, 1.03 0.56, 1.12 0.74, 1.21 0.87, 1.51 0.88, 1.25 0.72, 1.23 0.66, 1.30 0.73, 1.31

0.72 0.10 0.12 0.18 0.67 0.32 0.59 0.67 0.66 0.88

Symptoms (REF=absence) Numb hands Clumsy hands Impaired gait Bilateral arm paresthesia L’Hermitte’s phenomena Weakness

0.83 0.93 0.87 0.90 0.84 0.92

0.68, 1.02 0.75, 1.15 0.66, 1.14 0.77, 1.04 0.70, 1.02 0.73, 1.17

0.080 0.50 0.32 0.15 0.087 0.51

Signs (REF=absence) Corticospinal motor deficits Atrophy of intrinsic hand muscles Hyperreflexia Positive Hoffman’s sign Upgoing plantar responses Lower limb spasticity Broad-based unstable gait

0.93 1.03 0.83 0.88 0.94 0.75 0.89

0.79, 1.10 0.89, 1.20 0.72, 0.96 0.76, 1.03 0.80, 1.09 0.65, 0.86 0.75, 1.06

0.40 0.68 0.010 0.10 0.40 <0.0001 0.19

Co-morbidity score is comprised of both number and severity of co-morbidities. A 1-point increase reflects either an increase in disease severity or number of co-morbidities. Relative risk for each variable was calculated using log-binomial regression. *Relative risk for age is by decade. †Relative risk for duration of symptoms is by group (1) <3 months, 2) >3, ≤6 months, 3) >6, ≤12 months, 4) >12, ≤24 months, 5) >24 months). C.I.: confidence interval; mJOA: modified Japanese Orthopedic Association

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10.3.3.2 Multivariate Analysis

The final model consisted of three statistically significant variables and one clinically

relevant predictor. Three of these were also significant in our first model: baseline severity

score (RR: 1.09, 95%C.I.: 1.03-1.15), duration of symptoms (RR: 0.94, 95%C.I.: 0.89-0.99) and co-

morbidity score (RR: 0.96, 95%C.I.: 0.91-1.00). In addition, the neurological sign “lower limb

spasticity” significantly added to the predictive performance of this model (RR: 0.76, 95%C.I.:

0.66-0.87). Based on relative risks, patients were more likely to achieve a score ≥12 on the

mJOA if they had a higher baseline mJOA score; a lower co-morbidity score (fewer and less

severe concomitant disease); a shorter symptom duration; and if they did not have lower limb

spasticity. The AUC for this model was 0.75 (95%C.I.: 0.67, 0.83) (Table 10-5).


following Surgery in Patients with Severe CSM (mJOA<12)


Lower limb spasticity (REF=absence)

0.76 0.66, 0.87 <0.0001

Baseline severity score (mJOA) 1.09 1.03, 1.15 0.0028



This model serves to distinguish between patients with mild to moderate myelopathy postoperatively (mJOA≥12)

and those with severe neurological impairment (mJOA<12). Relative risk for each covariate was computed using

Poisson regression. Baseline severity score: 0-18 points; co-morbidity score is comprised of both number and

severity of co-morbidities; duration of symptoms 1) <3 months, 2) >3, ≤6 months, 3) >6, ≤12 months, 4) >12, ≤24

months, 5) >24 months. C.I.: confidence intervals; mJOA: modified Japanese Orthopedic Association

10.4 Discussion

This study aimed to develop a clinical prediction rule to determine functional status in

patients undergoing surgery for CSM. This was done using data on 743 patients enrolled in the

prospective multicenter AOSpine CSM-International and CSM-North America studies. We

incorporated results from our initial North American prediction study and external validation

study in order to create a clinically relevant and globally valid model that could be implemented

into surgical practice.308, 345 Based on our findings, patients were more likely to achieve a score

≥16 on the mJOA if they were younger; had milder myelopathy and a shorter duration of

symptoms preoperatively; did not smoke; had fewer and less severe comorbidities; and did not

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present with impaired gait. This model was similar to the one constructed in the North

American population except that the predictor “psychiatric disorders” was replaced with a co-

morbidity score that summarizes overall preoperative health status. Depression and bipolar

disease had a greater predictive value in the North American population because the reported

incidence was significantly higher among these patients than in our international sample. These

differences may indicate regional variations in actual incidence but likely reflect either surgical

selection bias or cultural reluctance to admit to mental illness. Regardless, this low incidence in

populations outside of North America decreases the global validity of this predictor. Instead, we

developed a co-morbidity score to summarize a patient’s overall preoperative general health

status. This score comprises both number of co-morbidities as well as severity of disease. This

predictor was statistically significant in our model and contributed to its overall predictive

performance.

Along with co-morbidity score, the prediction model consisted of age, symptom

duration, preoperative myelopathy severity, smoking status and impaired gait. Elderly patients

may have a reduced ability to translate neurologic recovery into functional improvements.

Potential explanations are 1) the elderly experience modifications to their spinal cord, including

a decrease in γ-motorneurons, number of anterior horn cells and number of myelinated fibers

in the corticospinal tracts and posterior funiculus; 2) as CSM is a progressive disease, older

patients are likely to have more substantial degenerative pathology and may require a more

complex surgery; 3) older patients tend to have reduced physiological reserves and

unassociated co-morbidities that may affect outcome.124, 170, 216, 217 We do not, however,

recommend that surgeons discriminate on the basis of chronological age but instead consider a

patient’s physiological age and co-existing co-morbidities. In general, however, age is

associated with reduced postoperative recovery and so the expectations of elderly patients

should be managed accordingly.

A longer duration of symptoms is significantly associated with a poor surgical outcome.

Chronic compression of the spinal cord for a prolonged duration can result in irreversible

histological changes including cystic necrosis, cavitation and syrinx formation.215 215 Surgical

decompression may not be able to reverse all of these changes and, as a result, patients will not

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achieve optimal recovery. It is therefore essential that primary care physicians are able to

recognize key signs and symptoms of CSM, differentiate between this disease and other

mimicking diagnoses (ex. bilateral carpal tunnel syndrome, multiple sclerosis, and amyotrophic

lateral sclerosis), identify patients at high risk of disease progressive and refer patients early for

surgical consultation. This is especially critical given recent reports surrounding the natural

history of the disease. According to a recent systematic review, between 20 to 60% of patients

with symptomatic CSM will deteriorate over time without surgical intervention.19 As patients

progress, they will exhibit an increase in functional impairment, a decrease in social

independence and more deleterious signs and symptoms such as impaired gait and lower limb

spasticity.346 According to our model, both a severer mJOA score and gait dysfunction are

significant predictors of a worse surgical outcome. This finding confirms the need to detect

these patients at earlier disease stages. Furthermore, surgeons may choose to operate on

milder cases rather than waiting for these patients to progress to a severity where they will no

longer achieve optimal results.

This model is not intended to identify patients who will benefit more from surgery than

from non-operative management. Rather, it serves to predict outcomes in patients with

progressive, symptomatic myelopathy who have failed previous conservative management. A

randomized control trial is required to evaluate the relative efficacy of conservative versus

surgical treatment; however, it would be unethical to deny surgery to patients with

symptomatic progressive myelopathy.

Smokers were also less likely to achieve a score ≥16 on the mJOA at 1-year follow-up.

Although previous studies have suggested that smoking results in higher rates of non-fusion

and wound infections, there were no significant differences between smokers and non-smokers

with respect to these complications in our cohort.302 Instead, we speculate that smoking is a

surrogate for unhealthy lifestyle, presence of co-morbidities, lower socioeconomic status and

poorer dietary choices. All of these variables could impact a patient’s clinical outcomes and

recovery, compliance with postoperative management programs and access to post-surgical

care. Further research is required to confirm these hypotheses; however, until this is done,

smokers should be encouraged to stop their habit prior to surgery.

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This prediction model can effectively discriminate between patients who will achieve an

“optimal” outcome at 1-year postoperatively and those who will not. Predicting a mJOA score

of ≥16 is clinically relevant and especially useful to manage expectations. Although patients

often ask about their chances of improvement, they also want to know whether surgery will

result in greater social independence, an ability to perform day to day activities and the

resolution of their more deleterious signs and symptoms. A score of ≥16 translates to minimal

impairment and functional independence and thus predicting this score is meaningful to

patients. This information should be used by surgeons during the surgical consent discussion to

manage expectations and to counsel concerned patients and their families as to potential

treatment options. Based on two recent studies, preoperative expectations, and whether or not

they are met through treatment, is a significant predictor of overall satisfaction.347 It is

therefore imperative that clinicians use our quantitative prediction tool to more objectively

convey prognostic information and give the patient a better understanding of how he/she

should expect to fare following intervention.

This current study also addresses another limitation from the CSM-North America study.

Patients with severe myelopathy (mJOA<12) have a significantly lower probability of achieving a

score ≥16 on the mJOA. It is unjust to classify a patient who improves from a score of 8 to a

score of 14 as having a “suboptimal” outcome. We developed a second prediction model for

these patients to evaluate their probability of improving to a score ≥12 on the mJOA. Based on

our findings, the most significant predictors of this outcome were baseline severity score,

duration of symptoms, co-morbidity score and lower limb spasticity. Interestingly, even in

patients with severe disease, the earlier surgeons intervene, the better patients are likely to do.

Overall health status is also critical to a patients’ surgical success; specifically, severe

myelopathic patients in good cardiovascular health are expected to fare better than those with

concomitant cardiovascular disease.

10.5 Conclusions

Based on this study results, patients were more likely to achieve a score ≥16 on the

mJOA if they were younger; had milder myelopathy and a shorter duration of symptoms

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preoperatively; did not smoke; had fewer and less severe co-morbidities; and did not present

with gait dysfunction. This information can be used by clinicians to manage patients’

expectations and counsel concerned patients as to potential treatment options. Furthermore,

the results from this study emphasize the importance of accurately detecting CSM at a mild

disease state and referring these patients for early surgical consultation.

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Chapter 11: Clinical and Surgical Predictors of Complications following

Surgery for the Treatment of Cervical Spondylotic Myelopathy: Results

from the Prospective AOSpine International study of 479 Patients

11.1 Introduction

This chapter summarizes the methodology and results from a study designed to

evaluate significant clinical and surgical predictors of perioperative complications.

Surgery, although proven highly effective, is not risk free and is associated with

complications in 11 to 38% of patients. The majority of these are transient, non-neurological

and do not require invasive intervention or prolonged hospital stay. Regardless, surgical

complications still taint a patient’s overall perception of surgery and may often involve

postoperative management, additional follow-up visits and increased associated costs.

Therefore, surgeons should better anticipate these complications, institute preventative

strategies, and closely monitor their patients in the perioperative period.

Predicting intraoperative and postoperative complications is an increasingly important

area of research. In our systematic review of the literature, older age and a longer operative

duration were predictive of overall perioperative complications and a two-stage

anteroposterior procedure was a significant predictor of major complications. Furthermore, low

evidence suggested an association between a diagnosis of OPLL and the occurrence of C5 root

palsy. Other clinical, imaging and surgical factors were not identified as significant predictors.

Further investigation is required to support these conclusions and to develop a complications

prediction model that could objectively identify “high-risk” patients. This knowledge can help

surgeons develop case-specific pre- and post-operative management strategies and inform

patients of their relative risks and benefits during the consent discussion. Furthermore, this

information will enable health care providers to better anticipate hospital utilization costs,

allocate sufficient resources and implement optimal rehabilitation plans.

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This study has two aims: 1) to characterize and quantify perioperative complications in

patient undergoing surgery for the treatment of CSM and 2) to identify important clinical and

surgical predictors of complications and to develop a prediction rule.

11.2 Methods


Our sample consisted of 479 patients enrolled in the CSM-International study from 16

global sites.


The objective of this analysis was to determine significant clinical and surgical predictors

of perioperative complications. Perioperative complications were defined as surgery-related

events occurring with 30 days of surgery.

Descriptive statistics were computed for all clinical and surgical variables. Continuous

predictors were described using means, standard deviations and ranges. Categorical variables

were summarized using percentages. The incidence of perioperative complications was

quantified and the frequency of each type of complication was calculated.

Simple logistic regression analyses were conducted to evaluate the association between

various clinical and surgical factors and perioperative complications. Predictors that yielded a p-

value of <0.20 in univariate analyses were included in multivariate analysis. Based on our

systematic review and a survey of spine care professionals, variables that had a p-value of >0.20

but were considered clinically important were also evaluated in multivariate analysis.

Collinearity of all variables was assessed by calculating tolerance. Manual backward

stepwise logistic regression was used to determine the best combination of surgical and clinical

predictors. Variables were included in the final model if they a) contributed significantly to the

predictive performance of the model, as evaluated by the area under the receiver operating

curve; b) were statistically significant; and/or c) were clinically relevant based on existing

literature or results from an international survey of spine care professionals. Methods like these

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ensure that the model is more generalizable and externally valid than if it was solely based on

statistical findings. Logistic regression was used to formulate the final equation and determine

odds ratios for each covariate. The prediction equation is given by equation 11-1:

𝑃 =𝑒

𝛽0+𝛽1𝑋1+𝛽2𝑋2+𝛽3𝑋3+⋯𝛽𝑗𝑋𝑗

1+𝑒𝛽0+𝛽1𝑋1+𝛽2𝑋2+𝛽3𝑋3+⋯𝛽𝑗𝑋𝑗

(Equation 11-1)

where P is the probability of experiencing a perioperative complications, β0 is the estimate of

the intercept and β(1,2,3,j) are the parameter estimates of the predictor variables X(1,2,3,j).

Sub-analyses were conducted to explore the association between each predictor

included in the multivariate model and specific categories of complications. These categories

included surgical, neurological and pain complications, infection and dysphagia/dysphonia.

Furthermore, rates of these specific complications were statistically compared between 1)

anterior and posterior surgeries, 2) laminectomy with fusion and laminoplasty and 3) 1- and 2-

stage procedures.

11.3 Results


A total of 479 patients were enrolled in the AOSpine CSM-International study from 16

global sites in four continents: 150 (31.32%) were from six Asian Pacific sites, 126 (26.30%) from

five European sites, 123 (25.68%) from two sites in North America and 80 (16.70%) from three

sites in Latin America.

The study cohort consisted of 310 (64.72%) men and 169 (35.28%) women, with ages

ranging from 21 to 87 years (mean age 56.36±11.91 years). The patients had a wide range of

preoperative myelopathy severities (3-18) and a mean baseline mJOA score of 12.50±2.86.

Duration of symptoms ranged from 0.25 to 240 months (mean duration 27.04±34.67 months).

Sixty percent of patients were diagnosed with one or more co-morbidities before surgery; the

most common type of co-morbidities were related to the cardiovascular system (56.49%). Sixty

patients (12.53%) had pre-existing diabetes: 42 were mild, 16 moderate, 1 severe and 1 had an

unspecified severity. The average body mass index of this population (n=332) was 25.78±4.55

kg/m2 (range 14.20-41.09 kg/m2), which is classified as “overweight” according to criteria set by

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the World Health Organization. The most common diagnosis was spondylosis (75.99%) but a

large number of patients also had disc herniation (72.65%), OPLL (28.18%) and hypertrophy of

the ligamentum flavum (25.47%) (Table 11-1).

With respect to surgical technique, 57.74% of patients were treated anteriorly and

39.96% posteriorly. A minority (2.30%) underwent a two-stage anteroposterior surgery. The

mean number of levels decompressed was 3.66±1.28 (range: 1-7) and the mean operative

duration was 178.07±80.20 minutes (range: 45.00-495.00 minutes).

Table 11-1. General Characteristics, Signs and Symptoms, Co-Morbidities, Diagnosis and Surgical Summary of CSM patients enrolled in the CSM-International Study


Baseline severity score (mJOA) 12.50±2.86 (3-18)

Age (years) 56.36±11.91 (21-87)

Gender (%) 64.72 M, 35.28 F

Duration of symptoms (n=477) (months) 27.04±34.67 (0.25-240)

Smoking (%) 27.35 Y, 72.65 N

Body Mass Index (n=332) (kg/m2) 25.78±4.55 (14.20-41.09)

Co-morbidities (n=477-479) (%) Cardiovascular (%) Respiratory (%) Gastrointestinal (%) Diabetes (%) Psychiatric (%) Rheumatologic (%) Co-morbidity score Number of co-morbidities

59.83 43.51 8.81 15.09 12.53 7.97 2.94 1.38±1.74 (0-13) 1.19±1.30 (0-6)

Diagnosis (%) Spondylosis Disc Herniation OPLL HLF Subluxation

75.99 72.65 28.18 25.47 6.26

Surgical Approach (%) Anterior (one-stage) Posterior (one-stage) Circumferential (two-stages)

57.74 39.96 2.30

Operative duration (mins) 178.07±80.20 (45-495)

Number of decompressed levels 3.66±1.28 (1-7)

Means are given with standard deviations. Categorical variables are described using frequencies. mJOA: modified Japanese Orthopaedic Association; OPLL: ossification of the posterior longitudinal ligament; HLF: hypertrophy of the ligamentum flavum

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11.3.2 Complications

Seventy-eight patients experienced 89 complications, yielding an overall incidence of

16.25%. Sixty-eight patients had a single complication, nine had two and one experienced

three. Thirty-four (38.20%) of these complications occurred on the day of surgery. The most

common complications were dysphagia (4.38%), dural tear (2.92%) and superficial infection

(2.09%). Very few patients suffered from hardware failure (0.21%), graft dislodgement (0.21%)

or graft pain (0.21%) within the perioperative period. With respect to neurological

complication, 4 (0.84%) patients had C5 radiculopathy, 3 (0.63%) suffered perioperative

worsening of myelopathy, 3 (0.63%) exhibited symptoms of new radiculopathy and 1 (0.21%)

experienced progression of myelopathy (0.21%). Figure 11-1 provides an overview of the types

of complications experienced by patients enrolled in the CSM-International study. The majority

of these were defined as minor and did not result in permanent morbidity, prolongation of

hospital stay or invasive intervention. Furthermore, these complications did not affect

functional or neurological status at 1-year: patients who experienced a complication improved

on average by 2.13±2.60 on the mJOA, whereas patients without a perioperative complication

improved by 2.48±2.74 (p=0.33).

11.3.3 Univariate Analysis

11.3.3.1 Clinical Factors

Patients with complications were on average older (57.91±10.90 years) and had a higher

BMI (26.71±4.57 kg/m2) compared to patients without complications (56.06±12.09 years,

25.60±4.54 kg/m2), although these relationships did not reach statistical significance (age, OR:

1.01, p=0.21; BMI, OR: 1.05, p=0.10). Patients with more severe myelopathy (OR: 0.94, p=0.14)

or a longer duration of symptoms (OR: 1.14, p=0.14) were not at a higher risk of experiencing a

complication perioperatively. Univariately, the major clinical risk factors for perioperative

complications were a diagnosis of OPLL (OR: 1.65, p=0.055), a greater number of co-morbidities

preoperatively (OR: 1.32, p=0.0018), a higher co-morbidity score (OR: 1.19, p=0.0060), diabetes

(OR: 2.83, p=0.0008) and co-exiting cardiovascular (OR: 1.64, p=0.046) and gastrointestinal

disorders (OR: 1.92, p=0.034). Of the cardiovascular diseases, hypertension was associated with

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the greatest risk of complications (OR: 1.74, p=0.026). Smoking status and gender were not

predictive of complications (Table 11-2).

Figure 11-1. An Overview of the Types of Complications experienced by CSM Patients in the Perioperative Period. Red: surgical complications; blue: neurological complications; green: pain-related; purple: infection; orange:

dysphagia and dysphonia; black: other complications. DVT: deep venous thrombosis.

Table 11-2. Univariate Analysis assessing the Relationship between Various Clinical Factors and Perioperative Complications

Clinical Predictor

Odds Ratio

95% C.I.

p-value

Gender (REF=male) 1.18 0.71, 1.94 0.52

Age 1.01 0.99, 1.03 0.21

Duration of symptoms 1.14 0.96, 1.36 0.14*

Smoking (REF=non-smoker) 1.22 0.72, 2.07 0.46

Body Mass Index (n=332) 1.05 0.99, 1.12 0.10*

Baseline severity score (mJOA) 0.94 0.86, 1.02 0.14*

OPLL (REF=other forms of DCM) 1.65 0.99, 2.74 0.055

Co-morbidities (REF=absence) 2.03 1.18, 3.47 0.01

Number of co-morbidities 1.32 1.11, 1.56 0.0018


Cardiovascular (REF=absence) 1.64 1.01, 2.68 0.046

Respiratory (REF=absence) 1.69 0.79, 3.59 0.18*

Gastrointestinal (REF=absence) 1.92 1.05, 3.49 0.034

Diabetes (REF=absence) 2.83 1.54, 5.20 0.0008

Psychiatric (REF=absence) 0.17 0.50, 2.76 0.72

Rheumatologic (REF=absence) 0.85 0.19, 3.87 0.83

CI: confidence interval; mJOA: modified Japanese Orthopedic Association; DCM: degenerative cervical myelopathy

0 5 10 15 20 25

Other

Cardiopulmonary event

DVT

Dysphonia

Dysphagia

Deep infection

Superficial infection

Graft site pain

New neck pain

Perioperative worsening of myelopathy

New radiculopathy

Progression of myelopathy

C5 radiculopathy

Graft dislodgement

Dural tear

Screw malposition

Hardware failure

Pseudoarthrosis

Number of Patients

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11.3.3.2 Surgical Factors

There was no significant difference in complication rates between patients treated

anteriorly (8.99%) and those treated posteriorly (6.42%) (p=0.88). Patients undergoing a two-

stage circumferential surgery, however, were at a greater risk of perioperative complications

than patients treated with either a single-stage anterior or posterior surgery (OR: 6.58,

p=0.0023). Patients with complications had a greater number of decompressed levels

(3.90±1.33) than those without complications (3.62±1.26), although this relationship did not

reach statistical significance (p=0.075). Finally, a longer operative duration was a significant

predictor of perioperative complications (OR: 1.005, p=0.0002) (Table 11-3).

Table 11-3. Univariate Analysis assessing the Relationship between Various Surgical Factors and Perioperative Complications

Surgical Predictor Odds Ratio 95% C.I. p-value

Anterior vs. Posterior (REF=anterior) 1.04 0.62, 1.73 0.88

1-stage vs. 2-stage (REF=1-stage) 6.58 1.96, 22.14 0.0023

Operative duration 1.005 1.003, 1.008 0.0002

Number of decompressed levels 1.19 0.98, 1.43 0.075

CI: confidence interval

11.3.4 Multivariate Analysis

Assessment of tolerance indicated collinearity between cardiovascular co-morbidities,

co-morbidity score and the number of co-morbidities. We evaluated each of these three

predictors independently in multivariate analysis rather than choosing one. The final logistic

regression model consisted of one statistically significant clinical variable, one significant

surgical variable and two factors that approached significance and were deemed clinically

relevant. According to the final model, patients were at an increased risk of perioperative

complications if they had a diagnosis of OPLL (OR: 1.75, p=0.040), concomitant diabetes (OR:

1.96, p=0.060), a greater number of co-morbidities (OR: 1.20, p=0.069) and a longer operative

duration (OR: 1.005, p=0.0015) (Table 11-4).

Equation 11-2 displays the final logistic regression model:

𝑃 =𝑒−4.30+0.67(𝐷𝑖𝑎𝑏𝑒𝑡𝑒𝑠)+0.0048(𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑣𝑒 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛)+0.56(𝑂𝑃𝐿𝐿)+0.18(𝑁𝑢𝑚𝑏𝑒𝑟)

1+𝑒−4.30+0.67(𝐷𝑖𝑎𝑏𝑒𝑡𝑒𝑠)+0.0048(𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑣𝑒 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛)+0.56(𝑂𝑃𝐿𝐿)+0.18(𝑁𝑢𝑚𝑏𝑒𝑟) (Equation 11-2)

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Where number is number of co-morbidities, diabetes=2 if diabetes is present, OPLL=2 if OPLL is

present and operative duration is given in minutes.

Table 11-4. Final Complications Prediction Model: Significant Clinical and Surgical Predictors of

Perioperative Complications


OPLL (REF=other forms of DCM) 1.75 1.03, 2.98 0.040

Number of co-morbidities 1.20 0.99, 1.47 0.069

Operative duration 1.005 1.002, 1.008 0.0015

Diabetes (REF=absence) 1.96 0.97, 3.94 0.060

CI: confidence interval; OPLL: ossification of the posterior longitudinal ligament; DCM: degenerative cervical myelopathy

11.3.5 Sub-Analyses

A higher percentage of patients with OPLL experienced a wound infection (6.67%),

surgical (2.22%) and pain-related (2.22%) complications than patients with other forms of

degenerative cervical myelopathy (infection: 1.16%, p=0.0022; surgical: 3.20%, p=0.043; pain:

0%, p=0.022). Diabetic patients were at a greater risk of dysphagia/dysphonia (18.33%) than

patients without diabetes (2.63%, p<0.0001). Surgical complications were associated with a

longer operative duration whereas a higher number of co-morbidities was predictive of

dysphagia/dysphonia (p=0.0012) (Table 11-5).

Table 11-6 compares rates of specific complications between a) anterior and posterior

surgery, b) laminectomy and fusion and laminoplasty and c) 1- and 2-stage surgery. The

anterior approach was associated with a higher incidence of dysphagia/dysphonia (6.88%) than

posterior surgery (0%, p=0.0002). Infection rates were higher in patients treated posteriorly

(4.19%) compared to those treated anteriorly (1.09%), although this relationship did not reach

statistical significance (p=0.057%). A 2-stage surgery was associated with significantly higher

rates of infection (18.18%) and dysphagia/dysphonia (27.27%) than either a single stage

anterior or posterior surgery (infection: 2.36%, p=0.033; dysphagia/dysphonia: 4.0%, p=0.011).

Finally there was a higher incidence of wound infection in patients treated with laminectomy

and fusion (6.48%) than those receiving laminoplasty (0%, p=0.045).

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11.4 Applying the Model to Two Cases

Case 1: A 54-year old non-smoking female presented with moderate myelopathy (mJOA=12)

secondary to spondylosis and disc herniation. This patient had bilateral arm paresthesia,

L’Hermitte’s phenomena, atrophy of intrinsic hand muscles and a broad-based unstable gait.

The duration of symptoms was reported as 50 months. The patient also had coexisting mild

depression. She was treated with a 3-level anterior discectomy with fusion and her operative

duration was 160 minutes.

Case 2: A 78-year old non-smoking male presented with severe myelopathy (mJOA=7)

secondary to spondylosis. This patient had numb and clumsy hands, impaired gait, weakness,

corticospinal distribution motor deficits, hyperreflexia, lower limb spasticity and broad-based

unstable gait. The duration of symptoms was reported as 72 months. The patient also had

concomitant moderate hypertension, moderate diabetes and severe coronary arterial disease.

He was treated with a 6-level posterior laminectomy and fusion and his operative duration was

420 minutes.

Figure 11-2. Applying the Complications Prediction Model in a Surgical Setting: Case 1

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Table 11-5. The Association between Important Predictors and Specific Type of Complications

Table 11-6. The Relationship between Type of Complications and Surgical Approach, Number of Stages and Posterior Technique

Means were compared using the appropriate t-test and frequencies using the Chi-square test. LAMP: laminoplasty; LMF: laminectomy with fusion; OPLL: ossification of the posterior longitudinal ligament. Surgical: pseudoarthrosis, hardware failure, screw malposition, dural dear; Neurological: C5 radiculopathy, progression of myelopathy, new radiculopathy, perioperative worsening of myelopathy; Pain: new neck pain, graft pain; Infection: superficial or deep wound infection

Type of Complication

Present? OPLL p-value

Diabetes p-value Operative Duration

p-value

Number of Co-morbidities

p-value

Yes No Yes No

Surgical Yes 7.41% 3.20% 0.043 0% 5.01% 0.092 176.1±79.26 0.017 1.191.31 0.63

No 92.59% 96.80% 100% 94.99% 221.9±89.60 1.00±1.10

Neurological Yes 1.48% 2.33% 0.73 3.33% 1.91% 0.36 176.8±79.07 0.068 1.18±1.30 0.78

No 98.52% 97.67% 96.67% 98.09% 236.5±112.0 1.40±1.58

Pain Yes 2.22% 0% 0.022 0% 0.72% 1.00 178.3±80.32 0.49 1.19±1.30 0.060

No 97.78% 100% 100% 99.28% 146.7±56.86 0

Infection Yes 6.67% 1.16% 0.0022 6.67% 2.15% 0.067 177.8±80.12 0.75 1.17±1.30 0.12

No 93.33% 98.84% 93.33% 97.85% 185.9±85.80 1.62±1.19

Dysphagia, dysphonia

Yes 4.44% 4.65% 0.92 18.33% 2.63% <0.0001 177.6±79.45 0.88 1.14±1.26 0.0012

No 95.56% 95.35% 81.67% 97.37% 188.0±95.97 2.23±1.69

Type of Complication Present? Approach p-value Stages p-value Posterior Technique p-value

Anterior Posterior 1-Stage 2-Stage LAMP LMF

Surgical Yes 4.35% 4.19% 0.93 4.18% 9.09% 0.39 2.99% 4.63% 0.71

No 95.65% 95.81% 95.72% 90.91% 97.01% 95.37%

Neurological Yes 1.09% 3.66% 0.10 2.14% 0% 1.00 1.49% 4.63% 0.41

No 98.91% 96.34% 97.86% 100% 98.51% 95.37%

Pain Yes 0.72% 0.52% 1.00 0.64% 0% 0.79

No 99.28% 99.48% 99.36% 100%

Infection Yes 1.09% 4.19% 0.057 2.36% 18.18% 0.033 0% 6.48% 0.045

No 98.91% 95.81% 97.64% 81.82% 100% 93.52%

Dysphagia, dysphonia Yes 6.88% 0% 0.0002 4.07% 27.27% 0.011 2.99% 6.48% 0.48

No 93.12% 100% 95.93% 72.73% 97.01% 93.52%

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Using equation 2, case 1 had a 10.69% (95%C.I.: 7.71-14.65%) probability of

experiencing a complication whereas case 2 had a much greater risk (P=58.42%, 95%C.I.: 37.02-

77.05%). Case 2 should be informed of this risk during the surgical consent process and his

attending surgeon should develop a case-specific preventative plan. Furthermore, case 2 should

be closely monitored in the postoperative period.

11.5 Discussion

This study aimed to first identify key clinical and surgical predictors of perioperative

complications in patients with CSM and then to develop a complications prediction rule. Based

on our results, patients were at a higher risk of perioperative complications if they had a

greater number of co-morbidities, coexisting diabetes, myelopathy secondary to OPLL and a

longer operative duration. This model can help a surgeon identify patients who are at an

increased risk of experiencing a complication and encourage them to modify their surgical

strategies accordingly. For example, patients at a higher risk of 1) dysphagia/dysphonia (i.e.

those with a greater number of co-morbidities or diabetes) should be evaluated preoperatively

by speech pathologists to ensure no subclinical dysphagia, 2) wound infections (i.e. those with

OPLL) should receive vancomycin treatment to reduce this risk; 3) neurological complications

may be given neuroprotective agents or monitored using intraoperative evoked potential

recordings. Furthermore, surgeons can use this knowledge to discuss the risks and benefits of

surgery with their patients during the consent process and to ensure appropriate monitoring

and management in the postoperative period. Furthermore, health care providers can use a

patient’s relative risk to anticipate hospital utilization costs, allocate resources accordingly and

optimize postoperative recovery.

This study has also identified key differences in the complication profile between the

anterior and posterior approach, laminectomy with fusion and laminoplasty and 1- and 2-stage

surgery. Based on our findings, 1) the anterior approach is associated with higher rates of

dysphagia/dysphonia; 2) there is a higher incidence of infection in patients treated by

laminectomy and fusion; and 3) 2-stage procedures are accompanied by increased risk of

infection and dysphagia/dysphonia. This knowledge will inform surgeons of the likely

253

complications associated with each procedure and allow them to implement appropriate

intraoperative preventive strategies as well as effective postoperative management plan.

This model was first conceptually developed using evidence from the literature and

results from a survey of 916 spine professionals from AOSpine International. These methods

will serve to improve the generalizability and validity of this prediction rule in future

populations.

Our results must be interpreted in the context of existing literature. With respect to co-

existing diseases, a single study assessed complication rates between patients with and without

diabetes while controlling for several key confounders including age and number of fused

levels.218 According to Cook et al (2008), patients with diabetes were at a higher risk of cardiac

complications (OR: 1.57, p=0.01) and “other complications” (OR: 1.54, p=0.01) than patients

without diabetes. In addition, cardiac complications (OR: 2.82, p=0.03), hematomas (OR: 5.13,

p<0.01) and postoperative infection (OR: 7.46, p=0.02) were more common in patients with

uncontrolled diabetes than those with controlled disease.218 In surgical lumbar studies, diabetes

was also a significant risk factor of non-unions and wound infections.291-294 Based on our survey

results, spine professionals agreed that a patient’s general preoperative health status was a

significant predictor of perioperative complications. Specifically, if a patient had co-existing

diabetes, he/she would be at a greater risk of perioperative complications, cardiac

complications, and wound infections.

Several studies explored various clinical and imaging risk factors of upper extremity

palsy following surgical decompression247, 254, 256, 261, 348, 349; however, only two of these

conducted a well-powered multivariate analysis.242, 243 In our systematic review of the

literature, low level evidence suggested that patients with a diagnosis of OPLL were at a higher

risk of developing upper extremity or C5 nerve palsy postoperatively. Low level evidence,

however, indicates low confidence that the evidence reflects the true effect and that further

research is likely to change the confidence in the estimate of effect or the estimate itself. In the

CSM-International study, there were no significant differences in rates of neurological

complications (p=0.73) or, more specifically, C5 radiculopathy (p=0.32) between diagnosis

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groups. Potential explanations for this discrepancy include differences in definitions across

studies, small sample sizes or reporting differences between investigator- and coordinator-

collected data. Patients with OPLL, however, had a higher incidence of wound infection

(p=0.0022), pain-related complications (p=0.022) and surgical complications (p=0.043) than

patients with other forms of DCM. Possible explanations for these increased rates include that

the surgery for OPLL is technically challenging and that the ossification process often involves

the dura.

Finally, moderate evidence suggests that operative duration is associated with

perioperative complications. This surgical variable is a surrogate for case complexity as patients

with increased degenerative pathology will likely undergo a longer surgery or possibly a two-

stage circumferential procedure. In addition, a longer operative duration is associated with

higher rates of infection following surgery.

The non-significant findings in this current study also confirm the results of existing

literature. Based on our analysis, there was no difference in overall complication rates between

patients treated anteriorly and those treated posteriorly. This conclusion is in line with the

results from the CSM-North America study that identified no significant differences in overall

rates of perioperative complications between approach groups.115 Studies by Ghogawala et al

(2011) and Kristof et al (2009) also reported similar rates of complications between posterior

laminectomy and either anterior discectomy or corpectomy.268, 269 Baseline severity score,

smoking status, duration of symptoms and gender were also not significant predictors of

complications in this study. According to our systematic review of the literature, there is

moderate evidence confirming no relationship between gender and complications and low

evidence suggesting baseline myelopathy severity, smoking status and preoperative duration of

disease are not associated with perioperative complications.

Imaging factors were not evaluated in this study but will be a topic of future research.

Lubelski et al (2014) developed a model to predict C5 palsy following decompression surgery

using preoperative anatomic measurements from magnetic resonance images (MRI).350 Based

on this study, a combination of anteroposterior diameter, foraminal diameter and cord-lamina

255

angle could accurately identify (95%) patients who would experience symptoms of C5

radiculopathy postoperatively. This high discriminative ability provides strong evidence that

imaging factors do play a significant role in predicting neurological complications and likely

overall complication rates. Imagama et al (2010) also reported that nerve root palsy is

associated with a smaller width of the intervertebral foramen of C5 and a higher degree of

anterior protrusion of its superior articular process.254 In addition, several studies have

recognized that a larger occupying ratio of OPLL is significantly predictive of upper extremity

paresis246 and of major intraoperative blood loss.244 Other factors have been reported as

insignificant predictors of complications: preoperative lordosis,248, 254 signal intensity on a T2-

weighted MRI251, 254, C2-C7 angle and cervical curvature.249, 351 Future prospective research,

however, is required to truly evaluate the predictive value of these MRI factors.


To date, this study represents the largest prospective analysis of important clinical and

surgical predictors of complications and the first one using international data. The major

limitations of this study are that there are no standardized definitions of complications and that

surgeons often have different perceptions of what constitutes a surgery-related event. In this

study, all adverse events were collected throughout the study and classified as either related to

CSM, related to surgery or unrelated by a central panel of investigators. These investigators

were blinded to the patients’ demographic information, surgical summary and neurological

status. This method like reflects the most consistent, unbiased and comprehensive method of

identifying complications in a surgical cohort; however, regional variations in definitions may

still affect data collection and reporting.

11.7 Conclusions

The main focus of this study was to evaluate predictors of overall perioperative

complications in patients undergoing surgery for CSM. Based on our model, patients were at a

higher risk of complications if they had a greater number of co-morbidities, co-existing

diabetes, a diagnosis of myelopathy secondary to OPLL and a longer operative duration. This

knowledge can be used by surgeons to objectively quantify a patient’s risk of complications and

256

discuss these risks during the surgical consent process. Furthermore, surgeons should use this

information to institute case-specific preventative plans and to strategize appropriate

postoperative care. Future studies will focus on predicting neurological complications as well as

major complications that require reoperation.

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Chapter 12: Summary of Findings, General Discussion, Thesis

Limitations and Future Directions

12.1. An Overview: Predicting Surgical Outcome

There is an increasing need for clinicians to accurately and objectively quantify a

patient’s likely surgical outcome in order to appropriately manage patient expectations and

improve overall satisfaction. In the United States of America, recent legislative developments

require clinicians to incorporate patient satisfaction into their assessment of overall treatment

outcomes.352 Centers for Medicare and Medicaid services (CMS), hospitals and insurance

providers are searching for ways to better evaluate care and have identified patient satisfaction

as a major component and predictor of overall quality.352 As a result, as part of a CMS hospital

inpatient value-based purchasing program, Medicare reimbursements are increasingly linked to

patient satisfaction. Furthermore, as of 2013, CMS have provided “value-based” incentive

payments to acute care hospitals according to, in part, patient satisfaction surveys.353

Patient satisfaction is not solely based on clinical outcomes and can be strongly

influenced by several other factors, including the politeness of the staff, the cleanliness of the

facilities, surgical wait times and the physician’s communication skills. Furthermore, according

to a study by Hamilton et al (2013), preoperative expectations, and whether or not they are

met through treatment, is also a significant predictor of overall satisfaction.347

Expectations are “the yardstick by which our patients measure the course of recovery,

occurrence of complications and the outcome.”354

Patients’ expectations, however, are often influenced by anecdotal evidence provided

by friends and family, information obtained from the internet and various news sources and

past surgical experiences. The attending surgeon must use the consent discussion as an

opportunity to outline risks and benefits of the procedure, provide accurate outcome

information and correctly manage patients’ expectations. However, these expectations are also

affected by how the patient understands the information provided by the surgeon and how

he/she translates it into “actionable beliefs.”354 This is further dependent on several extraneous

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factors such as mental state, attitude and individual or cultural values and, as such, patients

may form different expectations than those the surgeon is attempting to communicate.

In this thesis, we hypothesized that a quantitative tool used to predict surgical outcome

can help a surgeon more effectively convey prognostic information and give the patient a

better understanding of how he/she should expect to fare following intervention. Patients’

expectations will therefore be more appropriately managed and, accordingly, overall

satisfaction will likely improve. There is also substantial variability across centers with respect

to the conversations surgeons have with their patients during the preoperative visit. It is

imperative that guidelines are developed to standardize the prognostic information clinicians

provide their patients at the time of surgical consent. A quantitative tool can ensure that

consistent, accurate and objective information is being conveyed to these patients and can help

align surgeons’ perceptions of outcomes across hospitals, regions and even countries.

The first objective of our thesis was to evaluate important clinical and imaging

predictors of functional status at 1-year following surgery. As presented in Chapters 3 and 4, a

systematic review and a survey of international spine professionals were conducted to develop

a theoretical framework for a prediction model. Although numerous studies have examined the

predictive value of various clinical factors, only 24 used a multivariate analysis and controlled

for potential confounders. The majority of these were retrospective cohort studies and had

several methodological flaws. In addition, a variety of outcome measures or forms of outcome

measures were used, preventing easy comparison across studies and effective synthesis of the

evidence. Very few conclusions could be made as to significant clinical predictors of outcome.

The baseline level of evidence for most associations started at “low” and was further

downgraded as effect estimates or confidence intervals of these estimates were not reported.

However, based on the consistency of results across studies, we concluded that durations of

symptom and preoperative myelopathy severity score are significant predictors of surgical

outcome as longstanding and chronic compression of the spinal cord can lead to irreversible

histological damage. This review identified several knowledge gaps and confirmed the need for

a prospective multicenter prediction study.

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In systematic review B, we aimed to identify significant imaging predictors of outcome.

We focused on studies that conducted a multivariate analysis and controlled for two of the

following three covariates: age, baseline severity score and duration of symptoms. Again, there

was a paucity of information in the form of high quality prospective studies with well powered

statistical analyses. In addition, the methodology used to measure certain imaging parameters

varied across studies, emphasizing the need to develop quantitative guidelines for the

assessment of MRI. In general, based on our review, factors that could distinguish between

irreversible and reversible damage were the most important predictors of outcome. The

rationale behind this finding is that, if cord compression results in mild damage such as edema

or ischemia, then surgical decompression can reverse these histological changes. The key

imaging predictors of surgical outcome were combined T1/T2 signal change; signal change ratio

comparing compressed vs. non-compressed segments or T2 vs. T1 intensity; and the number of

SI segments. A combined T1/T2 signal change, a higher signal change ratio and multilevel SI are

all indicative of severe, irreversible damage to the spinal cord, including cystic necrosis,

secondary syrinx and cavitation.

The survey of AOSpine International was used to bridge the gap between current

practice and existing evidence. Based on our results, spine professionals agreed and confirmed

that baseline severity score and duration of symptoms are the most significant predictors of

outcome. Similar to our systematic review, there was a lack of consensus surrounding the

predictive value of age. Finally, although smoking status was not ranked highly by spine

professionals, a large proportion of the sample indicated that current smoking status does carry

prognostic value.

Spine professionals agreed that MRI was a valuable prognostic tool and that cord

properties were more important predictors of outcome than canal dimensions. However, it was

also evident that clinicians did not know what specific parameters of the MRI should be

measured for outcome prediction. The presence of high SI on a T2WI was deemed to be the

most important imaging factor and had the highest mean ranking. However, based on our

systematic review, a high SI on T2WI reflects a broad spectrum of compressive pathologies and

a wide range of recuperative potentials. T2WI SI is non-specific and may indicate either

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reversible damage including edema and ischemia or irreversible changes similar to T1WI, such

as necrosis, myelomalacia and cavitation. If the SI reflects more minor pathological changes

that will likely diminish post-surgery, then it is not an important prognostic factor. There is a

definite need to address this discrepancy between literature findings and professional opinion

and to establish how clinicians should use the MRI as a prognostic tool.

The results from the systematic review and survey guided the development of our

clinical prediction model (Figure 12-1). We first constructed a preliminary model using data on

278 patients enrolled in the CSM-North America study at 12 North American sites.

Postoperative mJOA at 1-year was selected as the primary outcome measure as this assessment

tool addresses all components of CSM and is routinely used to evaluate functional status in

these patients. The mJOA was dichotomized for the purpose of logistic regression analysis and a

score of 16 was deemed an appropriate cut-off to distinguish between patients with mild

myelopathy postoperatively and those with substantial residual neurologic impairment. This

cut-off was also validated using the MCID of the mJOA as an anchor: patients who improved by

greater than or equal to the MCID scored, on average, 15.82±2.19 and demonstrated

significantly larger gains in functional status. Predicting a score ≥16 is also clinically relevant and

especially useful to manage expectations. Although patients often ask about their chances of

improvement, they also want to know whether surgery will result in greater social

independence, an ability to perform day to day activities and the resolution of their more

deleterious signs and symptoms. A score ≥16 on the mJOA translates to mild impairment and

functional independence and thus predicting this score is meaningful for patients.

Based on the North American model, patients were more likely to achieve a score ≥16 if they

were milder preoperatively and had a shorter duration of symptoms; were younger; did not

smoke; and did not have co-existing psychiatric disorders or impaired gait. These findings

provide additional support to the results from our systematic review and survey and confirm

that patients with severe degenerative changes and chronic, longstanding compression of the

cord have less recuperative potential due to irreversible histological damage. It is necessary

that primary care physicians accurately detect CSM at early disease stages, differentiate

between it and mimicking diagnoses and refer these patients immediately for surgical

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consultation. A first step to ensure timely management of CSM is to better educate medical

students to identify key indicators of CSM on the MRI and physical examination and effectively

rule out differential diagnoses such as carpal tunnel syndrome, MS, ALS and vitamin B

deficiency. Furthermore, guidelines for CSM management need to be developed and shared

among primary care physicians, neurologist and rheumatologists to outline that patients should

be referred to a neurosurgeon or orthopaedic surgeon upon diagnosis. In addition to duration

of symptoms and baseline severity score, impaired gait is also a significant predictor of

outcome. This result provides further incentive for early diagnosis and referral as 20-60% of

patients with symptomatic CSM will deteriorate over time and exhibit signs and symptoms of

gait impairment. At this point in the disease progression, it is difficult to guarantee a patient will

experience optimal recovery following surgery.

Figure 12-1. A Theoretical Framework of the Prediction Model The dependent variable is functional status at 1-year follow-up as assessed by the mJOA (rounded-edge rectangular box). The key predictors or independent variables are contained in rectangular boxes and are connected to the outcome by one-directional black arrows. The thickened arrows represent a confirmed relationship between duration of symptoms and baseline severity score and surgical outcome. Red arrows indicate potential confounders:

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Older patients were less likely to achieve a score ≥16 on the mJOA. This finding will help

resolve the controversy surrounding age and will contribute to the overall body of evidence as

it is based on results from a level I prospective cohort study with a follow-up rate ≥80%. We do

not, however, recommend that surgeons discriminate on the basis of age but rather be aware

that their elderly patients may not be able to translate neurologic recovery into functional

improvements as effectively as their younger patients. Potential explanations for this reduced

recovery include 1) the elderly experience age-related changes in their spinal cord, including a

decrease in γ-motorneurons, number of anterior horn cells and number of myelinated fibers in

the corticospinal tracts and posterior funiculus; 2) as CSM is a progressive disease, older

patients are likely to have more substantial degenerative pathology and may require a more

complex surgery; and 3) older patients are more likely to have reduced physiological reserves

and unassociated co-morbidities that may affect outcome. It is important that a clinician

distinguish between a patient’s chronological age and his/her physiological age when predicting

surgical outcome. In general, however, age is associated with poorer surgical outcomes and so

the expectations of elderly patients should be managed accordingly.

Smoking was also a negative predictor of outcome. Although previous studies have

suggested that smoking results in higher rates of non-fusion and wound infections, there were

no significant differences in our cohort between smokers and non-smokers with respect to

these complications. Instead, we speculate that smoking is a surrogate for an unhealthy

lifestyle, presence of co-morbidities, lower socioeconomic status and poorer dietary choices. All

of these variables could impact a patient’s clinical outcomes and recovery, compliance with

rehabilitation programs and access to post-surgical care. Further research is required to confirm

these hypotheses; however, until this is done, smokers should be encouraged to stop their

habit prior to surgery.

Finally, patients with depression and bipolar disorders were less likely to achieve an

“optimal” surgical outcome. Preoperative mental state has been strongly associated with

surgical outcomes following other forms of neuro- and orthopedic surgeries, including total

knee replacement, lumbar discectomy and revision surgery for adjacent segment degeneration,

recurrent stenosis and pseudoarthrosis.355, 356 Our study is the first prospective study to report

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that patients with controlled, type I psychiatric disorders are more likely to have a suboptimal

outcome. It is essential that clinicians carefully evaluate a patient’s mental status and take

record of prior or current use of anti-depressants. Some patients may have undiagnosed

psychiatric disorders; the Zung depression scale or other similar tools may be useful to evaluate

a patient’s preoperative depression status. Surgeons may recommend that these patients seek

counseling prior to intervention in order to improve outcomes, recovery and treatment

satisfaction.

The original model demonstrated good predictive performance and had strong internal

validity. However, since the model was developed using patients from North America, it truly

reflects the demographics, disease characteristics, medical system and management strategies

of North America. Consequently, performance of the model must be assessed on an external

population to test its value in a different setting and determine whether it can be implemented

into clinical practice. Our model was externally validated using data on 479 patients enrolled in

CSM-International study from 16 global sites. Although the model proved to be valid, certain

clinical factors were identified as more significant predictors in North America than in global

settings. The most important of these was psychiatric disorders: depression and bipolar disease

had a greater predictive value in the North American population because the reported

incidence was significantly higher among these patients than in our international sample. These

differences may indicate regional variations in actual incidence but likely reflect either surgical

selection bias or cultural reluctance to admit to mental illness. Regardless, this low incidence in

populations outside of North America decreases the global validity of this predictor. For our

final, global prediction model, we developed a co-morbidity score to summarize a patient’s

overall preoperative general health status. This score is comprised of both number of co-

morbidities as well as severity of disease. This predictor was statistically significant in our model

and contributed to its overall predictive performance.

In chapter 9, we evaluated whether certain MRI parameters would enhance the

predictive performance of our validated clinical prediction rule. Given the results of our

systematic review, we hypothesized that signal change ratio, combined T1/T2 signal change or

number of SI segments (extent of signal change) would improve the discriminative ability of our

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model. Signal change ratio and height of signal change were the two factors that increased the

AUC the most, although these improvements were not statistically significant. Ultimately, in our

cohort of surgical patients with both neurological and image-evidence of CSM, MRI parameters

did not significantly improve the performance of our North American model. We speculate that

the MRI becomes less sensitive in predicting outcome in this group of surgical patients as all

participants had a positive MRI and evidence of cord compression. The results of this study are

not intended to devalue the role of MRI but rather suggest that, alternatively, more sensitive

techniques are necessary to uncover additional prognostic value.

In conclusion, the main predictors of an “optimal” outcome are a younger age; milder

preoperative myelopathy; a shorter duration of symptoms; non-smoking; absence of impaired

gait; and better general health status as evaluated by number and severity of co-morbidities.

Our final global prediction model can be implemented into clinical practice as a quantitative

tool used to predict a patient’s likely surgical outcome.

12.2 An Overview: Predicting Complications

Surgeons must also inform patients as to their risk of surgical complications during the

consent discussion.

“Informed patients are a lot more understanding…”

Dr. Hecht, an orthopaedic surgeon at Mount Sinai hospital in New York City, discussed the need

to appropriately manage a patient’s expectations of surgical complications at the Annual

Meeting of the Cervical Spine Research Society. In the United States of America, the

consequences for poorly communicating this information are far more substantial than in

Canada. A patient who is unaware of his/her risk and subsequently suffers a complication is

more likely to be dissatisfied with their surgery regardless of other clinical outcomes. Given the

“pay-per-performance” policies in the United States, there may be financial implications for

neglecting to inform a patient of their complication risk. Furthermore, in addition to being

dissatisfied, patients who experience a complication may choose to file a claim and sue their

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surgeon for medical malpractice. Interestingly, medical claims for spine surgery represent the

highest compensation payments and defense costs out of all forms of orthopaedic surgery.357

The second objective of this thesis was to evaluate important clinical and surgical

predictors of complications and to develop a preliminary prediction model to quantify patient

risk. Such a tool will help surgeons identify high risk patients and allow them to more effectively

and accurately communicate this risk to their patients. Accordingly, patients will have a better

understanding of their surgical risks and benefits; appreciate the thoroughness of the surgeon,

thereby enhancing the ever-so important patient-physician relationship; and be less surprised

and likely to file a medical claim if a complication occurs. In addition, knowledge of a patient’s

complication risk can help surgeons develop and institute case-specific preventative plans and

strategize postoperative care. Furthermore, surgeons should also be encouraged to closely

monitor their high risk patients in the perioperative period and educate them to recognize

future signs of complications. Finally, health care providers can use this information to

anticipate future hospital utilization costs and allocate resources accordingly.

Similar to our outcome study, we conducted a systematic review and surveyed

international spine professionals to first develop a conceptual complications model. Based on

our systematic review, older patients are less tolerant to surgery and are at a higher risk of

complications due to poorer overall health status, co-morbidities and reduced physiological

reserves. A longer operative duration and a two-stage surgery are also important predictors of

outcome as both reflect substantial degenerative pathology and increased case complexity. In

addition, patients with OPLL are at a higher risk of C5 nerve root palsy than patients with other

forms of DCM.

There was discrepancy between literature findings and surgeons’ perceptions of

important predictors of complications. According to spine professionals, the presence of co-

morbidities is the most important risk factor of perioperative complications; specifically, there

is a significant association between diabetes and various types of complications including

wound infections, cardiac complications and non-unions. In our literature review, low level

evidence, based on the findings of two prospective cohort studies, suggested co-morbidities are

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not predictive of perioperative complications. Cook et al (2008), however, determined that

patients with diabetes (compared to non-diabetics) are at a higher risk of cardiac complications

and those with uncontrolled disease are more likely to experience hematomas and

postoperative wound infections. With respect to surgical predictors, professionals supported

some of the results from our review and agreed that 2-stage surgeries are associated with

higher rates of complications than single stage surgeries. A prospective cohort study is required

to address controversy in the literature and among professionals and to properly define the

most critical predictors of complications.

Our model was developed using data on 479 patients participating in the CSM-

International study. Each investigator was responsible for recording all adverse events that

occurred throughout the study period. Some of these included a fall in the postoperative ward,

a nose bleed, an intraoperative cardiopulmonary, C5 nerve root palsy or pseudoarthrosis. These

adverse events were collected by a data processing center and adjudicated by a panel of

physicians as either related to CSM, related to surgery or unrelated. Any discrepancies across

the panel were resolved by consulting the source documents as well as the attending surgeons.

These methods ensured that complication definitions were standardized and that only “real”

surgery-related events were included in our analyses. Based on our model, patients were at a

higher risk of perioperative complications if they had a greater number of co-morbidities,

coexisting diabetes, myelopathy secondary to OPLL and a longer operative duration. This model

represents a first step to quantifying a patient’s risk of surgical complications. Future studies

are required to validate this model and assess predictors of specific complications such as C5

nerve root palsy, progression of myelopathy and wound infections.

12.3 Thesis Limitations

This thesis combines evidence from existing literature, professional opinion and

statistical findings to develop a clinical and complications prediction rule using prospectively

collected data. Given our high follow-up rate (>80%), our prognostic cohort studies reflect level

1 evidence according to criteria set by the Journal of Bone and Joint Surgery. One of the

limitations of this thesis is the 15-20% attrition rate at 1-year follow-up. For parts 1 and 2 of our

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outcomes prediction study, we accounted for this missing data using a multiple imputation

procedure as is statistically recommended. This is the preferred method for handling missing

data and is less susceptible to bias than removing patients with incomplete variables. In

addition, there were no significant demographic differences between patients who did and did

not attend their follow-up visit. Regardless, complete datasets would have been preferred for

our analyses. In the complications analysis, a significant proportion of BMI data was missing; as

a result, we were unable to effectively evaluate its predictive value. Patients with a high BMI

may be malnourished, have coexisting co-morbidities such as hypertension and diabetes and

may be more challenging to operate on. All of these factors may increase a patient’s risk of

complications; unfortunately we were unable to include this factor in our model due to missing

data. MRI collection was not a prerequisite for the CSM-North America study and so we were

only able to obtain useable images for 114 patients (41%). Fortunately, this was all that was

needed to evaluate whether MRI parameters contributed to the overall predictive performance

of our model.

It is necessary to know the reliability of our variables before forming strong conclusions

about their predictive value. With respect to our final model, age, impaired gait and smoking

status were assumed to be reliable predictors. On the other hand, the reliability of duration of

symptoms is unknown; however, the exact symptom duration may be difficult for a patient to

accurately recall and may be influenced by how urgently a patient believes he/she needs

surgery. To mitigate recall bias, we divided duration of symptoms into five groups (≤3 months;

>3, ≤6 months; >6, ≤12 months; >12, ≤24 months; >24 months); a patient only has to estimate

within three months of their actual duration. The reliability of our co-morbidity score is also

unknown and may be affected by cultural variations in diagnoses such as psychiatric disorders

and in prescription habits. Finally, the reliability of the mJOA has not been tested, resulting in

potential measurement biases. Even if the reliability of the English version of the mJOA was

known, the scale’s translatability would need to be evaluated as the International study was

conducted in several different languages. Various MRI parameters have also not been validated

or assessed for reliability.

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Surgeons have different perceptions of complications. For example, some surgeons

believe that every patient treated anteriorly will suffer “dysphagia” whereas others do not

classify “trouble swallowing” as a complication but rather a normal event after anterior

operation. To account for this variation, a panel of physicians sorted through all adverse events

and classified each as related or unrelated to surgery based on specific criteria.

As specified in Chapter 10, we also identified a limitation in our final prediction model: it

was more efficient at predicting outcome in mild and moderate patients than in severe

patients. It is unjust to classify a patient who improves from 8 to 13 on the mJOA as having a

“suboptimal” outcome. To rectify this problem, we developed a second prediction model and

used a score of 12 as our cut-off for regression analysis. Interestingly, an outcome ≥12 was also

predicted by a higher baseline severity score, a shorter duration of symptoms and better

general health status. A final limitation is the ceiling effect of the mJOA. Future studies should

use more sensitive scales that can detect improvements in milder disease states.

12.4 Future Directions

12.4.1 Standardizing Nomenclature

The definition of CSM needs to be internationally standardized and expanded to

encompass all forms of degenerative myelopathies including OPLL. There is controversy in the

literature as to whether OPLL should be characterized under the same term as CSM or if it

should be considered a separate entity. OPLL typically occurs in younger patients, is more

prevalent in the Japanese population and is suspected to have a familial predisposition and

genetic association.358 Given that the occurrence and progression of OPLL is tied to the aging of

the spine, and because it presents with similar signs and symptoms as CSM, we believe it is

reasonable to include both CSM and OPLL under the single term “degenerative cervical

myelopathy.” This definition encompasses both osteoarthritic changes including spondylosis,

disc herniation and facet arthropathy as well as ligamentous aberrations such as hypertrophy or

calcification of the ligamentum flavum and OPLL.

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12.4.2 The Reliability of the mJOA and MRI Factors

All psychometric properties have been established for the mJOA except for its reliability.

Currently, we are evaluating the intra-rater reliability by having two independent assessors

score the same CSM patient preoperatively. We will then calculate the intraclass correlation

coefficient for the association between the scores of the two raters. A detailed instruction

sheet is provided to each evaluator that outlines precisely how the mJOA should be measured.

The reliability of the MRI parameters must also be determined. We hope to recruit

approximately five neurosurgical or orthopedic spine fellows to assess the intra-rater reliability

of 1) identifying the mid-sagittal slide, 2) anatomical measurements such as the transverse

area, maximal canal compromise and cord compression, 3) the presence and absence of signal

change on both the T1-WI and T2-WI, and 4) signal change ratios. The inter-rater reliability of

these parameters can also be calculated by having the same reviewer re-measure each MRI at a

later time.

12.4.3 Guidelines for the Management of CSM

A team, led by Dr. Fehlings, is currently working on developing guidelines for the

management of CSM to ensure adequate patient support, appropriate treatment strategies and

optimal outcomes. We intend for these guidelines to appeal to a broad audience and, as such,

will involve experts from all specialties that encounter patients with CSM, including

neurosurgeons, orthopedic surgeons, neurologists, rheumatologists, physiatrists and

rehabilitation specialists. The introduction of these guidelines will serve as a way to define the

disease and relevant nomenclature, discuss the incidence of CSM and its global and regional

burden and highlight the differential diagnoses.

These guidelines will focus on three key topics:

1) How to diagnosis CSM and effectively quantify impairment in this population?

a. What are the “gold standard” diagnostic tests that should be used to identify

CSM?

b. What are the best tools to evaluate impairment in this population?

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2) How to manage patients with evidence of myelopathy?

a. What is the natural history of CSM?

b. Is non-operative intervention effective for the treatment of CSM?

c. Is surgery effective for the treatment of CSM?

d. What is the role of preoperative severity and duration of symptoms on

treatment outcomes? Howe should patients with mild myelopathy be managed?

Should surgical treatment be prescribed for mild patients?

3) How to manage minimally symptomatic cervical degenerative disease?

a. How should clinicians manage patients with minimally symptomatic cervical

degenerative disease but image evidence of cord compression? What are the

predictors of neurological deterioration and progression? How do we effectively

monitor these patients?

b. Is non-operative treatment effective in these patients?

12.4.4 Predicting Surgical Outcomes

In this thesis, we have summarized the most significant predictors of surgical outcome

and have developed a globally relevant and valid tool that can be implemented into clinical

practice. The next step is to create a smart phone application that clinicians can readily use to

quantify a patient’s probability of an “optimal” outcome by simply plugging in information on a

patient’s age, duration of symptoms, baseline severity score, smoking status, gait dysfunction

and general health status. This would be easier than having to use the lengthy equation

presented in Chapter 10. We also need to encourage surgeons to use our model in a clinical

setting as a decision-making aid and as a means to manage expectations, individualize

preoperative counseling and improve satisfaction. To do this, we need to promote our “app” at

key international neurosurgical and spine conferences.

A future study should test the hypothesis that our prediction model can improve overall

satisfaction by accurately managing expectations. To do this, I propose a randomized controlled

trial in which, for 50% of the sample, we would quantify a patient’s likely outcome using our

model and share these results with the patient during the surgical consent discussion. The

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remaining 50% of patients would have a regular patient-surgeon conversation and would not

receive an estimate of their outcome. Data will be collected at 1-year on patient satisfaction

and compared between the two groups.

12.4.5 Predicting Complications

For our complications model, the next step in to evaluate its external validity. However,

before this is done, definitions of complications need to be standardized across surgeons and

centers. At the Annual Meeting of the Cervical Spine Research Society, Justin Smith highlighted

that some of the key issues in the collection of complication data include 1) prospective

collection has higher rates than retrospective collection; 2) studies where complications are

recorded by surgeons have lower rates than studies where research coordinators are

responsible for data collection; and 3) it is unclear what actually constitutes a complication and

how to distinguish between minor and major events. In our complications study, all adverse

events were collected throughout the study and then adjudicated by a central panel of

investigators as related to CSM, related to surgery or unrelated. This likely represents the most

consistent, unbiased and comprehensive method of identifying complications in a CSM surgical

cohort. However, the heterogeneity of complications and regional variations in definitions may

still affect data collection and reporting.

At the 2014 Annual Meeting of the Cervical Spine Research Society, Dr. Justin Smith

proposed a classification system to better define surgical complications. Once this is done, we

can conduct another prospective complications study to validate our prediction model. Future

studies should also consider malnourishment using albumin as a marker, BMI and imaging

factors as predictors of perioperative complications.

In CSM, there are several types of complications categories (Figure 12-2). Given the

heterogeneity of complications, it is necessary to explore important predictors of specific types

of complications such as hardware failure, progression of myelopathy, dural tear and C5

radiculopathy. We speculate that significant predictors will vary depending on what

complication is used as the dependent variable. For some of these, it will also be critical to

incorporate various imaging findings.

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Figure 12-2. A Summary of Complications seen in Patients undergoing Surgery for CSM

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Publications Arising from this Thesis

Chapter 1: Derived from Tetreault L, Goldstein CL, Arnold P, Harrop J, Hilibrand A, Nouri A, Fehlings MG. Degenerative Cervical Myelopathy: A Spectrum of Related Disorders Affecting the Aging Spine (Neurosurgery) Kim HJ, Tetreault L, Massicotte EM, Arnold PM, Skelley AC, Brodt ED, Riew KD. Differential diagnosis for cervical spondylotic myelopathy: literature review (Spine) Singh A, Tetreault L, Casey A, Laing R, Statham P, Fehlings MG. A Summary of assessment tools for patients suffering from cervical spondylotic myelopathy: a systematic review on validity, reliability and responsiveness (European Spine Journal) Chapter 2: Derived from Tetreault L, Le D, Cote P, Fehlings MG. The Importance of Clinical Prediction Rules and External Validation: A commentary using an example in surgical patients with cervical spondylotic myelopathy (Evidence Based Spine Journal) Chapter 3: Derived from Tetreault L, Karpova A, Fehlings MG. Predictors of outcome in patients with degenerative cervical myelopathy undergoing surgical treatment: results of a systematic review (European Spine Journal) Tetreault L, Dettori JR, Wilson JR, Singh A, Nouri A, Fehlings MG, Brodt ED, Jacobs WB. Systematic review of magnetic resonance imaging characteristics that affect treatment decision making and predict clinical outcome in patients with cervical spondylotic myelopathy (Spine) Tetreault L, Ibrahim A, Côté P, Singh A, Fehlings MG. A Systematic Review of Clinical and Surgical Predictors of Complications following Surgery for Degenerative Cervical Myelopathy (JNS: Spine) Chapter 4: Derived from Tetreault L, Nouri A, Singh A, Fawcett M, Fehlings MG. Predictors of outcome in patients with cervical spondylotic myelopathy undergoing surgical treatment: A survey of members from AOSpine International (World Neurosurgery)

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Tetreault L, Singh A, Nater A, Fawcett M, Fehlings MG. An Assessment of Key Predictors of Perioperative Complications in Patients with Cervical Spondylotic Myelopathy Undergoing Surgical Treatment: Results from a Survey of 916 AOSpine International Members (World Neurosurgery) Chapter 6: Derived from Tetreault L, Nouri A, Côté P, Fehlings MG. The Minimal Clinically Important Difference of the modified Japanese Orthopaedic Association Scale in Patients with Degenerative Cervical Myelopathy undergoing Surgical Intervention (Spine) Chapter 7: Derived from Tetreault L, Kopjar B, Vaccaro A, Yoon ST, Arnold PM, Massicotte EM, Fehlings MG. A clinical prediction model to determine outcomes in patients with cervical spondylotic myelopathy undergoing surgical treatment: Data from the prospective, multicenter AOSpine North America study (Journal of Bone and Joint Surgery) Chapter 8: Derived from Tetreault L, Côté P, Kopjar B, Arnold P, Fehlings MG. A Clinical Prediction Model to Assess Surgical Outcome in Patients with Cervical Spondylotic Myelopathy: Internal and External Validation using the Prospective Multlicenter AOSpine North American and International Datasets (The Spine Journal) Chapter 9: Derived from Tetreault L, Nouri A, Côté P, Zamorano JJ, Dalzell K, Fehlings MG. Does Magnetic Resonance Imaging Improve the Predictive Performance of a Validated Clinical Prediction Rule Used to Evaluate Surgical Outcome in Patients with Cervical Spondylotic Myelopathy (Spine) Chapter 10: Derived from Tetreault L, Kopjar B, Côté P, Arnold P, Fehlings MG. A Clinical Prediction Rule for Functional Outcomes in Patients Undergoing Surgery for Degenerative Cervical Myelopathy: Analysis of the International AOSpine Prospective Multicentre Dataset of 757 Subjects (Journal of Bone and Joint Surgery) Chapter 11: Derived from Tetreault L, Tan G, Kopjar B, Côté P, Arnold P, Nugaeva N, Fehlings MG. Clinical and Surgical Predictors of Complications following Surgery for the Treatment of Cervical Spondylotic Myelopathy: Results from the Multicenter, Prospective AOSpine International study on 479 Patients (Under Review – Neurosurgery)

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Chapter 12: Derived from Tetreault L, Le D, Cote P, Fehlings MG. The Importance of Clinical Prediction Rules and External Validation: A commentary using an example in surgical patients with cervical spondylotic myelopathy (Evidence Based Spine Journal)

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