Bariatric Surgery: Outcomes and SafetyMISS 2010

Bruce M. Wolfe, MDProfessor of Surgery

Oregon Health & Science University

Iezzoni’s “Algebra of Effectiveness”

Patient Factors+

Effectiveness of Care+

Random Events=

Outcome

Lezzoni. Ann Thorac Surg 1994;58:1822 2

Goals of Databases

• Define volume of care

• Determine outcomes

• Basis for determination of expected outcomes or ratio of actual/expected outcomes

• Risk adjustment

3

Uses of Databases

• Self Assessment• Quality Assurance• Credentialing/Certification• Patient Information• Promotion• Research

4

Types of Databases

• Administrative– Based on claims data– Coding by administrative

personnel

• Clinical– Data collected by clinical

personnel

5

Administrative Database

• Discharge abstract data

• Population based

6

Administrative Database

• Advantages– Completeness of data– Available– Low cost to acquire

7

• Disadvantages– Coding imperfect– Done by

administrative personnel

– Inpatient only– Not procedure or

disease specific– Needed data not

present

Examples of Administrative Databases

• Medicare national claims history• Nationwide Inpatient Sample (NIS)• University Healthsystem Consortium (UHC)• Patient discharge database (states)

8

Charlson Comorbidity IndexJAMA October 2005

0 1 2 3

Flum 94% 6% 0.5% 0.1%

Santry 64 29 6 1.4

Zingmond 56 31 9 4

9

Society of Thoracic Surgeons Database (STS)

• Variations of outcomes in cardiac surgery

• Hospital/surgeon volume an important factor

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Society of Thoracic Surgeons Database (STS)

• Prospective clinical data

• Multiple parameters– Possible risk factors– Outcomes

• Voluntary, self-reported

• Agree to audit

11

Society of Thoracic Surgeons Database (STS)

• 1989 – Data collection begun

• Present > 2 million cases

12

Compare STS with Administrative Database CABG

Source of Data

Risk-Adjusting Algorithm

Reported Volume

In-Hospital Mortality Rate

Predicted Mortality

Risk-Adjusted Rate

STS

Database

STS 505 4.2 5.4 3.1

Medicare None 423 4.7 N/A N/A

13

Mack. J Thoracic Cardiovascular Surgery 2005;129:1309

Reasons for Data Variation

• Medicare not primary payer

• Coding problems

• Variations of definitions

• No risk adjustment

14

STS Controversies

• Low numbers at a site limit identification of variance from expected outcomes

• Volume-outcomes relationship is inconsistent• Factors involved include:

– High volume team at low volume center– Past experience– Process of care

15

Bariatric Surgery Databases

• Single institution reports• Multiple institution reports• Meta-analysis• LABS: NIH multicenter consortium• BOLD: ASMBS/SRC• Bariatric NSQIP: ACS

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Obesity Surgery Mortality Risk Score

• BMI > 50kg/m²• Male• Hypertension• DVT/PE risk• Age ≥ 45y

DeMaria: SOARD 2007;3:34-30 17

Obesity Surgery Mortality Risk Score

1 point for each risk factor:

0-1 A : Lowest risk2-3 B : Intermediate risk4-5 C : Highest Risk

DeMaria: SOARD 2007;3:34-40 18

Obesity Surgery Mortality Risk Score: Validation

• 4 centers, 4431 patients

DeMaria: Ann Surg 2007;246:578 19

Class Mortality

A 0.2%

B 1.1%

C 2.4%

All 0.7%

Development of Bariatric Surgery-Specific Risk Assessment Tool

Databases:

• National Hospital Discharge Summary

• AHRQ/NIS

• 25,000+ bariatric surgery cases

20Livingston: SOARD 2007;3:14-20

Correlated with Adverse Event

• Chronic Pulmonary Disease• Hypertension• Diabetes Complications• Deficiency Anemia• Depression• Age• Male

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