on behalf of china national stroke registry investigators yongjun

on behalf of China National Stroke Registry InvestigatorsYongjun Wang

Ruijun Ji, Haipeng Shen, Yuesong Pan, Panglian Wang, Gaifen Liu, Yilong Wang, Hao Li andNovel Risk Score to Predict Pneumonia After Acute Ischemic Stroke

Print ISSN: 0039-2499. Online ISSN: 1524-4628 Copyright © 2013 American Heart Association, Inc. All rights reserved.

is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231Stroke doi: 10.1161/STROKEAHA.111.000598

2013;44:1303-1309; originally published online March 12, 2013;Stroke.

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Stroke-associated pneumonia (SAP) is a common medical complication after stroke, with an estimated incidence of

5% to 30%.1–16 Evidence showed that SAP not only increase the length of hospital stay and medical cost,4,17,18 but also is an impor-tant risk factor of mortality and morbidity after stroke.10,15,16,19–24

Several risk factors for SAP have been identified, such as older age,4,8,9,11,21,25–27 male sex,4,5,8,26,27 diabetes mellitus,8 hypertension,3 atrial fibrillation,9,26 congestive heart failure,5,9,23 chronic obstructive pulmonary disease,3,5,11 pre-existing dependency,5,7,11 stroke severity,4,8,10–12,25–27 stroke subtypes,5,6,8 and dysphagia.3,6,11,12,25–28 However, no reliable scoring system is currently available in routine clinical practice or clinical tri-als. An effective risk stratification and prognostic model for SAP would be helpful to identify vulnerable patients, allocate relevant medical resources, and implement tailored preventive

strategies. In addition, for clinical trial, it could be used in nonrandomized studies to control for case-mix variation and in controlled studies as a selection criterion.

The goal of the study is 2-fold as follows: (1) to develop and validate a risk score (acute ischemic stroke-associated pneu-monia score [AIS-APS]) for predicting in-hospital SAP after AIS; and (2) to compare the discrimination of the AIS-APS and prior scores with regard to in-hospital SAP after AIS.

MethodsDerivation and Internal Validation CohortThe derivation and internal validation cohort originated from the largest stroke registry in China, the China National Stroke Registry (CNSR), which was a nationwide, multicenter, and prospective registry of consecutive patients with acute cerebrovascular events.29 Briefly,

Background and Purpose—To develop and validate a risk score (acute ischemic stroke-associated pneumonia score [AIS-APS]) for predicting in-hospital stroke-associated pneumonia (SAP) after AIS.

Methods—The AIS-APS was developed based on the China National Stroke Registry, in which eligible patients were randomly classified into derivation (60%) and internal validation cohort (40%). External validation was performed using the prospective Chinese Intracranial Atherosclerosis Study. Independent predictors of in-hospital SAP after AIS were obtained using multivariable logistic regression, and β-coefficients were used to generate point scoring system of the AIS-APS. The area under the receiver operating characteristic curve and the Hosmer–Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively.

Results—The overall in-hospital SAP after AIS was 11.4%, 11.3%, and 7.3% in the derivation (n=8820), internal (n=5882) and external (n=3037) validation cohort, respectively. A 34-point AIS-APS was developed from the set of independent predictors including age, history of atrial fibrillation, congestive heart failure, chronic obstructive pulmonary disease and current smoking, prestroke dependence, dysphagia, admission National Institutes of Health Stroke Scale score, Glasgow Coma Scale score, stroke subtype (Oxfordshire), and blood glucose. The AIS-APS showed good discrimination (area under the receiver operating characteristic curve) in the internal (0.785; 95% confidence interval, 0.766–0.803) and external (0.792; 95% confidence interval, 0.761–0.823) validation cohort. The AIS-APS was well calibrated (Hosmer–Lemeshow test) in the internal (P=0.22) and external (P=0.30) validation cohort. When compared with 3 prior scores, the AIS-APS showed significantly better discrimination with regard to in-hospital SAP after AIS (all P<0.0001).

Conclusions—The AIS-APS is a valid risk score for predicting in-hospital SAP after AIS. (Stroke. 2013;44:1303-1309.)

Key Words: acute ischemic stroke ■ China National Stroke Registry ■ stroke-associated pneumonia

Novel Risk Score to Predict Pneumonia After Acute Ischemic Stroke

Ruijun Ji, MD, PhD; Haipeng Shen, PhD; Yuesong Pan, PhD; Panglian Wang MD, PhD; Gaifen Liu, MD, PhD; Yilong Wang, MD, PhD; Hao Li, PhD; Yongjun Wang, MD;

on behalf of China National Stroke Registry Investigators

Received December 24, 2012; accepted January 29, 2013.From the Tiantan Comprehensive Stroke Center, Tiantan Hospital, Capital Medical University, Beijing, China (R.J., Y.P., P.W., G.L., Y.W., H.L., Y.W.);

and Department of Statistics and Operation Research, University of North Carolina, Chapel Hill, NC (H.S.).The online-only Data Supplement is available with this article at http://stroke.ahajournals.org/lookup/suppl/doi:10.1161/STROKEAHA.

111.000598/-/DC1.Corresponding to Yongjun Wang, MD, Tiantan Comprehensive Stroke Center, Beijing Tiantan Hospital, Capital Medical University, No. 6 Tiantanxili,

Dongcheng District, Beijing 100050, China. E-mail [email protected]© 2013 American Heart Association, Inc.

Stroke is available at http://stroke.ahajournals.org DOI: 10.1161/STROKEAHA.111.000598

2013

44

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mailto:[email protected]


1304 Stroke May 2013

hospitals in China are classified into 3 grades: I (community hospitals); II (hospitals that serve several communities); and III (central hospitals for a certain district or city). In total, 242 potential sites, including 114 grade III, 71 grade II, and 57 grade I hospitals, from both urban and rural area, were initially identified by soliciting application. The CNSR steering committee evaluated the research capability and commitment to the registry of each hospital with preliminary survey. Finally, a total of 132 hospitals including 100 grade III and 32 grade II were selected, which cover 27 provinces and 4 municipalities across China. Trained research coordinators at each institute reviewed medical records daily to identify, consent, and enroll consecutively eligible patients. To be eligible for this study, subjects had to meet the following criteria: (1) age ≥18 years; (2) hospitalized with a primary diagnosis of AIS according to World Health Organization criteria30; (3) stroke confirmed by head computerized tomography or brain MRI; (4) direct admission to hospital from a physician’s clinic or emergency department. The eligible patients from the CNSR were randomly classified into derivation cohort (60%) and internal validation cohort (40%).

External Validation CohortThe external validation cohort was derived from the cohort of Chinese Intracranial Atherosclerosis Study (CICAS). CICAS was a multicenter and prospective study aiming at investigating prevalence, risk factors, and impact of intracranial atherosclerosis among patients with ischemic stroke.31 The CICAS inclusion criteria were as follows: (1) age between 18 and 80 years; (2) onset of symptom within 7 days; and (3) hospitalized with a primary diagnosis of AIS or transient ischemic attack. Exclusion criteria were as follows: (1) preadmission modified Rankin Scale score >3; and (2) inability to undergo MRI for cerebral vascular imaging. For this study, patients diagnosed of transient ischemic attack were excluded.

Data Collection and DefinitionsThe scientific use of data obtained with informed consent and entered in the CNSR and CICAS registries was approved by the local ethi-cal committees. In these registries, a standardized case report form was used for data collection in the CNSR and CICAS network. The relevant data were extracted from the medical records by trained re-search coordinators. Data from each case report form were manually checked for completeness, correct coding, and proper application of diagnostic algorithm by a research specialist from an independent contract research organization. For the present study, the following candidate variables were analyzed: (1) demographics (age and sex); (2) stroke risk factors: hypertension (history of hypertension or anti-hypertensive medication use), diabetes mellitus (history of diabetes mellitus or antidiabetic medication use), dyslipidemia (history of dys-lipidemia or lipid-lowering medication use), atrial fibrillation (history of atrial fibrillation or documentation of atrial fibrillation at admis-sion), coronary heart disease, history of stroke/transient ischemic at-tack, current smoking, and excess alcohol consumption (≥2 standard alcohol beverages per day); (3) pre-existing comorbidities: congestive heart failure, valvular heart disease, peripheral artery disease, chronic obstructive pulmonary disease, hepatic cirrhosis, peptic ulcer, pre-vious gastrointestinal bleeding, renal failure, arthritis, Alzheimer’s disease/dementia, and cancer; (4) prestroke dependence (modified Rankin Scale ≥3); (5) admission stroke severity based on National Institutes of Health Stroke Scale score (NIHSS) and Glasgow Coma Scale score; (7) symptom of dysphagia; (8) stroke subtype: according to the Oxfordshire Community Stroke Project criteria,32 AIS was clas-sified into partial anterior circulation infarct, total anterior circulation infarct, lacunar infarction, and posterior circulation infarct; (9) admis-sion blood glucose (mmol/L); and (10) length of hospital stay (days).

In this study, SAP was diagnosed by treating physician accord-ing to the Centers for Disease Control and Prevention criteria for hospital-acquired pneumonia,33 on a basis of clinical and laboratory indices of respiratory tract infection (fever, cough, auscultatory re-spiratory crackles, new purulent sputum, or positive sputum culture), and supported by typical chest X-ray findings. Only hospital-acquired pneumonia was documented and pneumonia before stroke was not considered. Data on in-hospital SAP was prospectively collected.

Statistical AnalysisModel building was performed exclusively in the derivation cohort. In univariate analysis, χ2 test was used to compare categorical vari-ables, and Mann–Whitney test was used to compare continuous vari-ables. Univariate and multivariable logistic regression was performed to determine the independent predictors of SAP after AIS in the derivation cohort. Candidate variables were those with biologically plausible link to SAP on the basis of prior publication and those as-sociated with SAP in univariate analysis (P<0.2). On multivariable analysis, stepwise backward estimation was used to remove nonsig-nificant variables from the model. To test for collinearity between the covariates of the final multivariable model, the tolerance and variance inflation factor of each covariate was calculated. The β-coefficients from the final model were used to generate point scoring system of the AIS-APS, as in previous studies.34 The resulting AIS-APS was then validated by assessing model discrimination and calibration,35 in the internal and external validation cohorts. Discrimination was assessed by calculating the area under the receiver operating charac-teristic curve (AUROC). Calibration was assessed by performing the Hosmer–Lemeshow goodness-of-fit test and was graphically depict-ed in the plot of observed versus predicted SAP risk according to 10 deciles of predicted risk. Furthermore, we compared the discrimina-tion of the AIS-APS and 3 prior scores,25–27 with regard to in-hospital SAP after AIS. The AUROC and maximum Youden index were used to assess the discrimination of these scores for in-hospital SAP after AIS. AUROC was compared using Delong method,36 and sensitivity, specificity, positive predict value, and negative predictive value were calculated at each score’s maximum Youden index.

All tests were 2-tailed, and statistical significance was determined at α level of 0.05. Statistical analysis was performed using SAS 9.1 (SAS Institute, Cary, NC), SPSS 17.0 (SPSS Inc, Chicago, IL), and Medcalc software 12.3 (MedCalc, Ostend, Belgium).

ResultsClinical CharacteristicsThe clinical characteristics of the derivation, internal and external validation cohort were shown in Table 1. From September 2007 to August 2008, a total of 14 702 patients with AIS were enrolled in the CNSR network. The median age was 66 (interquartile range [IQR], 56–75), and 61.9% were men. The median length of hospital stay was 14 days (IQR, 10–20), and a total of 1669 (11.4%) patients had SAP dur-ing hospitalization. The eligible patients from the CNSR were randomly classified into derivation cohort (60%, n=8820) and internal validation cohort (40%, n=5882), which were well matched with respect to patient characteristics and overall rate of in-hospital SAP (Table 1).

From October 2007 to June 2009, a total of 3580 patients of AIS were registered in 22 participating hospitals in the CICAS network. Of these, we excluded 329 patients with transient ischemic attack (9.1%; median age, IQR: 62, 54–71; median admission NIHSS, IQR: 0, 0–1) and an additional 214 (6.0%; median age, IQR: 64, 55–73; median admission NIHSS, IQR: 3, 0–6) who had missing data of ≥1 covariates in the AIS-APS. The median length of hospital stay was 14 days (IQR, 10–18), and a total of 222 (7.3%) patients had SAP during hospitalization (Table 1).

Predictors of In-hospital SAPThe univariate analysis for potential predictors of in-hospital SAP after AIS in the derivation cohort was shown in Table I in the online-only Data Supplement, and the multivariable predictors were listed in Table 2. Age, present history of



Ji et al A Risk Score to Predict SAP After AIS 1305

atrial fibrillation, congestive heart failure, chronic obstructive pulmonary disease, and current smoking, prestroke dependence (modified Rankin Scale>3), admission NIHSS score, Glasgow Coma Scale score, symptom of dysphagia, Oxfordshire Community Stroke Project subtype of total anterior circulation infarct and posterior circulation infarct, and blood glucose were significantly predictive of in-hospital SAP after AIS. The tolerance of covariates in the final multivariable model ranged between 0.66 and 0.99; the mean variance inflation factor was 1.16 (range, 1.02–1.68).

Derivation of the AIS-APSThe β-coefficients from the multivariable regression model were used to generate point scoring system of the AIS-APS. To derive an integer value for each predictor, the β-coefficient of current smoking was used as reference and the value was rounded to the closest integer. The point scoring system of the AIS-APS was shown in Figure 1. The median AIS-APS score was 8 (range, 0–32) in the derivation cohort. The risk catego-ries were assigned in 7-point increments, and the magnitude of the score had prognostic implication (Figure 2).

Table 1. Clinical Characteristics

Derivation Cohort (n=8820)

Internal Validation Cohort (n=5882) P

1 Value

External Validation Cohort (n=3037) P

2 Value

Demographics

Age, median (IQR), y 66 (56–74) 66 (57–75) 0.11 64 (54–72) <0.001

Sex (male), n, % 5430 (61.6) 3675 (62.5) 0.26 1997 (65.8) <0.001

Vascular risk factor, n, %

Hypertension 5601 (63.5) 3683 (62.6) 0.27 2016 (66.4) 0.004

Diabetes mellitus 1834 (20.8) 1287 (21.9) 0.11 742 (24.4) <0.001

Dyslipidemia 947 (10.7) 637 (10.8) 0.86 411 (13.5) <0.001

Atrial fibrillation 643 (7.3) 415 (7.1) 0.59 180 (5.9) 0.01

Coronary artery disease 1222 (13.9) 811 (13.8) 0.91 286 (9.4) <0.001

History of stroke/TIA 2795 (31.7) 1822 (31.0) 0.36 777 (25.6) <0.001

Current smoking 3510 (39.8) 2326 (39.5) 0.70 1052 (34.6) <0.001

Excess alcohol consumption 1346 (15.3) 921 (15.7) 0.55 385 (12.7) 0.001

Pre-existing comorbidities, n, %

Congestive heart failure 169 (1.9) 121 (2.1) 0.55 25 (0.8) <0.001

Valvular heart disease 213 (2.4) 139 (2.4) 0.83 38 (1.3) <0.001

Peripheral artery disease 64 (0.7) 29 (0.5) 0.08 26 (0.9) 0.48

COPD 98 (1.1) 64 (1.1) 0.89 13 (0.4) 0.001

Hepatic cirrhosis 29 (0.3) 21 (0.4) 0.78 7 (0.2) 0.40

Peptic ulcer or previous GIB 283 (3.2) 195 (3.3) 0.72 76 (2.5) 0.05

Renal failure 7 (0.1) 4 (0.1) 0.78 4 (0.1) 0.50

Arthritis 266 (3.0) 176 (3.0) <0.001 46 (1.5) <0.001

Dementia 113 (1.3) 82 (1.4) 0.56 22 (0.7) 0.01

Cancer 150 (1.7) 109 (1.9) 0.50 52 (1.7) 1.00

Prestroke dependence (mRS>3), n, % 809 (9.2) 535 (9.1) 0.87 0 (0.0) …

Admission NIHSS score, median (IQR) 5 (2–9) 5 (2–9) 0.18 4 (2–7) <0.001

Admission GCS score, median (IQR) 15 (14–15) 15 (14–15) 0.36 15 (15–15) 0.12

Symptom of dysphagia, n, % 840 (9.5) 529 (9.0) 0.28 236 (7.8) 0.004

OCSP subtype, n, % 0.22 <0.001

Partial anterior circulation infarct 4834 (54.8) 3327 (56.6) 1869 (61.5)

Total anterior circulation infarct 811 (9.2) 519 (8.8) 177 (5.8)

Lacunar infarction 1667 (18.9) 1074 (18.3) 249 (8.2)

Posterior circulation infarct 1508 (17.1) 962 (18.4) 742 (24.4)

Admission blood glucose, median (IQR), mmol/L

6.2 (5.4–7.0) 6.2 (5.5–7.0) 0.28 5.5 (4.9–6.9) <0.001

Length of hospital stay, median (IQR), d 14 (10–20) 14 (10–20) 0.96 15 (12–20) <0.001

In-hospital SAP, n, % 1007 (11.4) 662 (11.3) 0.76 222 (7.3) <0.001

COPD indicates chronic obstructive pulmonary disease; GCS, Glasgow Coma Scale; GIB, gastrointestinal bleeding; IQR, interquartile range; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale; OCSP, Oxfordshire Community Stroke Project; SAP, stroke-associated pneumonia; and TIA, transient ischemic attack.

P1 denotes significant test between the derivation and internal validation cohort; P

2 denotes significant test between the derivation and external validation cohort.




Internal Validation of the AIS-APSThe performance of the AIS-APS (AUROC) in the deriva-tion (n=8820) and internal validation cohort (n=5882) was 0.797 (95% confidence interval, 0.782–0.811) and 0.785 (95% confidence interval, 0.766–0.803), respectively. The Hosmer–Lemeshow test was not significant (P=0.22), and the predicted and observed risk of in-hospital SAP after AIS were in close agreement according to 10 deciles of predicted risk in the internal validation cohort (Figure I in the online-only Data Supplement).

External Validation of the AIS-APSIn the external validation cohort (n=3037), the AIS-APS showed good discrimination with an AUROC of 0.792 (95% confidence interval, 0.761–0.823). The Hosmer–Lemeshow test was not significant (P=0.30). The plot of observed versus predicted risk of in-hospital SAP after AIS showed high correlation between observed and predicted risk in the external validation cohort (Figure I in the online-only Data Supplement).

Sensitivity AnalysisWe completed prespecified subgroup analyses by age, sex, time delay from onset to hospital arrival, and length of hospital stay. Similar good discrimination was seen in these subgroups (AUROC range, 0.740–0.837) (Table III in the online-only Data Supplement).

Comparison of SAP ScoresTable 3 showed the discrimination of the AIS-APS and 3 com-pared prior scores for in-hospital SAP after AIS. Although Chumbler and Hoffmann scores consistently showed good discrimination for in-hospital SAP in the derivation, internal

and external validation cohort, the AIS-APS demonstrated the highest AUROC. The difference in AUROC between AIS-APS and 3 compared scores was statistically significant (all P<0.0001). The AIS-APS had the highest maximum Youden index and associated sensitivity, specificity, positive predict value and negative predictive value (Table 3).

DiscussionIn the present study, we derived and validated a risk score for predicting in-hospital SAP after AIS. A 34-point AIS-APS was developed from the set of independent predictors. The AIS-APS showed good discrimination and calibration in the derivation, internal and external validation cohort. When com-pared with 3 prior scores, the AIS-APS showed significantly better discrimination with regard to in-hospital SAP after AIS.

To preserve the clinical use of the model for decision-mak-ing during acute hospitalization, we used only patient char-acteristics available at presentation. We chose not to include variables related to in-hospital management and those not routinely collected, such as quality of care,37 mechanical ven-tilation, and swallowing test,11,38 despite the fact that these fac-tors might influence the development of SAP after AIS. This model therefore predicts the expected risk of in-hospital SAP at presentation, and as such, the predictions could be used to guide subsequent in-hospital management.

Several risk factors for SAP have been identified. Consistent with these studies, we confirmed that in-hospital SAP was sig-nificantly associated with older age, present history of atrial fibrillation, congestive heart failure, chronic obstructive pul-monary disease and smoking, prestroke dependence, admis-sion NIHSS score, Glasgow Coma Scale score, dysphagia, and stroke subtypes. In addition, our study showed that admission blood glucose was significantly associated with in-hospital

Table 2. Multivariable Predictors of In-hospital SAP After AIS in the Derivation Cohort (n=8820)

Variables β-Coefficients SE Adjusted OR* 95% CI P Value

Model intercept −6.873

Age (1 yr increase) 0.049 0.003 1.05 1.04–1.06 <0.001

Atrial fibrillation (yes) 0.199 0.098 1.22 1.03–1.50 0.03

Congestive heart failure (yes) 0.764 0.162 2.15 1.56–2.95 <0.001

COPD (yes) 0.510 0.217 1.67 1.09–2.55 0.02

Current smoking (yes) 0.238 0.063 1.27 1.12–1.44 <0.001

Prestroke dependence (mRS>3) (yes) 0.313 0.086 1.37 1.16–1.62 <0.001

Admission NIHSS score (1 increase) 0.077 0.005 1.08 1.07–1.09 <0.001

Admission GCS score (1 decrease) 0.043 0.015 1.04 1.01–1.08 0.005

Dysphagia (yes) 0.642 0.083 1.90 1.62–2.24 <0.001

OCSP subtype

Lacunar infarction) 1.00

Partial anterior circulation infarct 0.001 0.088 1.00 0.83–1.19 0.99

Total anterior circulation infarct 0.319 0.115 1.38 1.10–1.72 0.006

Posterior circulation infarct 0.265 0.106 1.30 1.06–1.60 0.01

Admission blood glucose (1 mmol increase) 0.057 0.011 1.06 1.04–1.08 <0.001

AIS indicates acute ischemic stroke; CI, confidence interval; COPD, chronic obstructive pulmonary disease; GCS, Glasgow Coma Scale; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale; OCSP, Oxfordshire Community Stroke Project; OR, odds ratio; and SAP, stroke-associated pneumonia.

*Multivariable logistic regression adjusted for age, sex, stroke risk factors, comorbidities, prestroke dependence, admission NIHSS score, GCS score, symptom of dysphagia, OCSP subtypes, blood glucose, and length of hospital stay.




SAP after AIS. Prior studies have indicated the relationship between blood glucose and in-hospital acquired infections.39,40 Biological evidence demonstrates that diabetes mellitus can increase susceptibility of infection by compromising the

immune system. For example, neutrophils from people with diabetes mellitus showed reduced chemotaxis and oxidative killing potential compared with those from nondiabetes con-trols.41 Leukocyte bactericidal activity is diminished in those with poor glucose control.42

The present risk stratification and prognostic model for SAP is unique in that it was derived from a large, multicenter, and prospective cohort, which included consecutive patients of AIS, was outside of clinical trial, and was more reflective of real-world clinical practice; the model included comprehen-sive information on demographics, medical history, prestroke functional status, admission stroke-related characteristics, and laboratory test; in addition, it could be used to predict in-hos-pital SAP on hospital arrival and could be readily incorporated into clinical practice by performing a simple score. Several SAP prediction rules have been developed using similar tech-niques; however, these models have not been widely used in clinical practice. It is not our intention to show superiority of the AIS-APS compared to the earlier scores; however, we want to point out the major difference. Kwon et al27 developed a pneumonia score, which included age, sex, NIHSS score, mechanical ventilation, and dysphagia. However, the study was limited by small sample size and was not validated exter-nally. Sellars et al11 presented key predictors for poststroke pneumonia, including older age, dysarthria or no speech due to aphasia, modified Rankin Scale score >4, low abbreviated mental test score, and failed water swallowing test. Although the model was informative, some predictors were not rou-tinely collected. Chumbler et al25 presented a 3-level scor-ing system for predicting pneumonia in acute stroke, which included medical history of pneumonia, symptom of dyspha-gia, increasing NIHSS score, being found down at symptom onset, and age >70 years. Although the model showed accept-able C-statistics, the study was limited by its retrospective nature and lacking of validation. Hoffmann et al26 derived

Figure 1. Acute ischemic stroke-associated pneumonia score (AIS-APS). COPD indicates chronic obstructive pulmonary dis-ease; GCS, Glasgow Coma Scale; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale; and OCSP, Oxfordshire Community Stroke Project.

Figure 2. The proportion of in-hospital stroke-associated pneumonia (SAP) after acute ischemic stroke (AIS) according to the AIS–pneu-monia score (PS) in the derivation, internal and external validation cohort. The potential risk of in-hospital SAP after AIS increased steadily with higher AIS-PS score. Error bars indicated 95% confidence interval for the proportion of in-hospital SAP after AIS in each category.




a score (A2DS2) to predict pneumonia after AIS in a large derivation and validation sample. The model showed good discrimination and calibration properties; however, it was not externally validated in geographically, culturally, and socio-economically distinct population.

With several SAP-related risk stratification and prognos-tic models available, identification of the most accurate and reliable grading scale(s) would be of great value to patients, clinicians, and researchers. In this study, we compared the dis-crimination of the AIS-APS and 3 prior scores with regard to in-hospital SAP after AIS. When using 2 measures to assess model discrimination (AUROC and maximum Youden index), the AIS-APS consistently showed to be superior at predicting in-hospital SAP after AIS than 3 compared scores in our popu-lation. In addition, similar results were verified in the deriva-tion, internal and external validation cohort. It was noteworthy that all scales had higher negative predictive value than posi-tive predict value for in-hospital SAP after AIS, which sug-gested that lower values more consistently predict patients without in-hospital SAP than higher values predicting those developing in-hospital SAP after AIS. Development of future prognostic models might benefit from attempts to make them more balanced in this regard, with discriminative use distrib-uted more evenly among higher and lower values.

Prior studies have shown that SAP was an important risk factor of mortality and morbidity after stroke; however, a sys-tematic review on efficacy of early antibiotics prophylaxis after stroke failed to show benefit in patients’ outcome.43 This might be attributable to inclusion of patients with low risk of developing SAP in these studies. The AIS-APS and other SAP predictive tools could be used to identify patients who are at high risk of developing SAP after stroke. It is expected that randomized controlled trials on efficacy of antibiotics pro-phylaxis on stroke functional outcome with stratification of patients’ potential risk of developing SAP.

Our study had some limitations that deserve comment. First, as all observational studies, we cannot rule out the possibility that additional baseline variable (unmeasured confounders) might have some impact on the development of in-hospital SAP after AIS, such as use of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers.44 Second, the time course of SAP after AIS is unclear. Because we only have information on new-onset SAP during hospitalization without documentation of the exact date, our data allow no conclu-sion as to whether patients with a longer length of stay per se are more likely to develop pneumonia or whether diagnosis of pneumonia leads to a longer hospitalization. However, by sen-sitive analysis, the AIS-APS was proven to be valid and signif-icant, regardless of length of hospital stay. Third, the limited information on time course of pneumonia also does not allow for a safe interpretation of causality between mechanical ven-tilation and pneumonia. We therefore abstained from includ-ing mechanical ventilation in our predictive score. In addition, the primary aim of our study was to develop a predictive score for SAP after AIS at presentation. Fourth, the study included only hospitalized patients with stroke, and those patients died in the emergency department, shortly after admission, or treated in outpatient clinics were not included. Meanwhile, the results of current analysis apply only to patients of AIS and cannot be extrapolated to patients of hemorrhagic stroke. Finally, the AIS-APS need to be further validated in additional populations.

In summary, the AIS-APS is a valid clinical grading scale for predicting in-hospital SAP after AIS at presentation. Further studies on effect of AIS-APS on stroke outcomes are needed.

Sources of FundingThe China National Stroke Registry is funded by the Ministry of Science and Technology (2006BA101A11) and the Ministry of Health of the People’s Republic of China (2009CB521905).

Table 3. Discrimination of AIS-APS and 3 Compared Prior Scores With Regard to In-hospital SAP After AIS

AUROC 95% CI ΔAUROC* P Value† Youden Index Cutoff Sensitivity Specificity PPV NPV

In the derivation cohort (n=8850)

Pneumonia score (Kwon et al. 2006)27 0.713 0.704–0.723 0.084 <0.0001 0.342 2 0.762 0.580 0.189 0.950

Chumbler’s score (Chumbler et al. 2010)25 0.752 0.743–0.761 0.045 <0.0001 0.401 13 0.605 0.796 0.276 0.940

A2DS2 score (Hoffmann, et al. 2012)26 0.745 0.736–0.754 0.052 <0.0001 0.373 5 0.545 0.828 0.291 0.934

AIS-APS score (2012) 0.797 0.782–0.811 Ref 0.435 8 0.793 0.769 0.306 0.967

In the internal validation cohort (n=5882)



A2DS2 score (Hoffmann et al. 2012)26 0.728 0.716–0.739 0.057 <0.0001 0.364 5 0.527 0.837 0.290 0.933

AIS-APS (2012) 0.785 0.766–0.803 Ref 0.435 8 0.781 0.764 0.300 0.965

In the external validation cohort-A (n=3037)



A2DS2 score (Hoffmann et al. 2012)26 0.759 0.744–0.774 0.033 <0.0001 0.392 5 0.725 0.667 0.146 0.969

AIS-APS score (2012) 0.792 0.761–0.823 Ref 0.530 8 0.728 0.802 0.225 0.973

AIS-APS indicates acute ischemic stroke-associated pneumonia score; AUROC, area under the receiver operating characteristic curve; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value; and SAP, stroke-associated pneumonia.

*ΔAUROC denotes the difference in AUROC between the AIS-APS and compared scores with regard to in-hospital SAP after AIS.†P value of comparing paired AUROC with Delong method.




DisclosuresNone.

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SUPPLEMENTARY MATERIAL

Table e-1. Univariable predictor of SAP after AIS in the derivation cohort (n=8820)

Increment/categories OR 95% CI P value

Demographics Age, year 1 year increase 1.05 1.05-1.07 <0.001 Gender female vs. male 1.18 1.06-1.31 0.002

Vascular risk factor Hypertension yes vs. no 1.12 1.00-1.25 0.04 Diabetes mellitus yes vs. no 1.09 0.96-1.23 0.19 Dyslipidemia yes vs. no 0.76 0.64-0.91 0.003 Atrial fibrillation yes vs. no 2.91 2.51-3.38 <0.001 Coronary artery disease yes vs. no 1.73 1.53-1.98 <0.001 History of stroke/TIA yes vs. no 1.40 1.26-1.56 <0.001 Current smoking yes vs. no 0.88 0.79-0.98 0.02 Excess alcohol yes vs. no 0.84 0.73-0.98 0.02

Preexisting comorbidities Congestive heart failure yes vs. no 3.84 2.98-4.95 <0.001 Valvular heart disease yes vs. no 2.29 1.77-2.96 <0.001

Peripheral artery disease yes vs. no 1.27 0.71-2.29 0.42 COPD yes vs. no 3.46 2.46-4.86 <0.001 Hepatic cirrhosis yes vs. no 1.72 0.83-3.54 0.14 Peptic ulcer or previous GIB yes vs. no 1.04 0.78-1.38 0.80 Renal failure yes vs. no 4.47 1.31-15.3 0.02 Arthritis yes vs. no 1.38 1.06-1.81 0.02 Alzheimer’s disease/dementia yes vs. no 3.14 2.29-4.31 <0.001 Cancer yes vs. no 1.75 1.27-2.41 0.001

Pre-stroke dependence (mRS>3) yes vs. no 2.56 2.22-2.93 <0.001 Admission NIHSS score 1 point increase 1.11 1.10-1.12 <0.001 Admission GCS score 1 point decrease 1.28 1.26-1.31 <0.001 Symptom of dysphagia yes vs. no 2.85 2.46-3.26 <0.001 OCSP subtype, n (%) Lacunar infarction (LACI) 1.00 Partial anterior circulation infarct (PACI) PACI vs. LACI 1.16 1.00-1.35 0.05 Total anterior circulation infarct (TACI) TACI vs. LACI 3.14 2.61-3.78 <0.001 Posterior circulation infarct (POCI) POCI vs. LACI 1.50 1.26-1.80 <0.001 Admission blood glucose (mmol/L) 1×mmol/L increase 1.08 1.06-1.10 <0.001 Length of hospital stay (days) 1 day increase 1.02 1.01-1.03 <0.001

Abbreviation: SAP, Stroke Associated Pneumonia; AIS, Acute Ischemic Stroke; OR, Odds Ratio; CI, Confidence

Interval; TIA, Transient Ischemic Attack; COPD, Chronic Obstructive Pulmonary Disease; GIB, Gastrointestinal

Bleeding; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale score; GCS, Glasgow

Coma Scale; OCSP, Oxfordshire Community Stroke Project



Table e-2. Subgroup analysis of discrimination of the AIS-PS with regard to in-hospital SAP after AIS

Derivation cohort

(n=8820) Internal validation cohort

(n=5882) External validation cohort

(n=3037) AUROC 95% CI AUROC 95% CI AUROC 95% CI

Overall cohort 0.797 0.782-0.811 0.785 0.766-0.803 0.792 0.761-0.823 Subgroups Age

<59 0.802 0.764-0.840 0.774 0.718-0.830 0.769 0.701-0.840 >60 0.766 0.748-0.783 0.755 0.733-0.777 0.783 0.749-0.818 Gender Male 0.806 0.789-0.823 0.803 0.779-0.826 0.798 0.759-0.838 Female 0.784 0.760-0.807 0.756 0.725-0.787 0.778 0.78-0.828 Time from onset to arrival (hours) <6 0.802 0.776-0.829 0.744 0.705-0.784 0.792 0.761-0.823 6-12 0.812 0.771-0.854 0.803 0.751-0.854 0.837 0.749-0.927 12-24 0.803 0.772-0.835 0.780 0.736-0.823 0.740 0.700-0.820 >24 0.781 0.759-0.803 0.792 0.764-0.820 0.744 0.701-0.812

Length of hospital stay (days) < 7 0.830 0.800-0.860 0.809 0.765-0.853 0.782 0.742-0.816 8-13 0.786 0.758-0.815 0.774 0.734-0.813 0.814 0.736-0.893 >14 0.793 0.775-0.812 0.779 0.756-0.802 0.774 0.740-0.809

Abbreviation: SAP, Stroke Associated Pneumonia; AIS, Acute Ischemic Stroke; AUROC, Area Under the Receiver Operating Characteristic Curve; CI, Confidence Interval.

by guest on April 23, 2013

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ownloaded from


Figure e-1. Plot of observed versus predicted risk of SAP after AIS in the derivation and validation cohorts

Figure e-1 legend Plot of observed versus predicted risk of in-hospital SAP after AIS with 95% confidence interval (C.I.) in the derivation and validation cohorts according to 10 deciles of predicted risk. Overall, there was a very high correlation between observed and predicted risk in the derivation cohort (A) (n=8820; r=0.99, P<0.001), internal validation cohort (B) (n=5882; r=0.99, P<0.001), and external validation cohort (C) (n=3037; r=0.98, P<0.001), which indicated excellent calibration.



Appendix: The CNSR investigators

Yongjun Wang, Beijing Tiantan Hospital; Qi Bi, Beijing Anzhen Hospital; Weiwei Zhang, Beijing Military District

Gengral hospital of Chinese People’s Liberation Army; Liying Cui, Peking Union Medical College Hospital of

Peking University; Yuheng Sun, Beijing Jishuitan Hospital; Maolin He, Beijing Shijitan Hospital; Dongsheng Fan,

Peking University Third Hospital; Xunming Ji, Beijing Xuanwu Hospital; Jimei Li, Beijing Friendship Hospital

Affiliated to Capital Medical University; Fang Zhang, Beijing Guangwai Hospital; Kai Feng, Beijing Shunyi

District Hospital; Xiaojun Zhang, Beijing Tongren Hospital; Yansheng Li, Shanghai Renji Hospital; Shaoshi Wang,

Shanghai First Municipal People’s Branch hospital; Wei Fan, Zhongshan Hospital of Fudan University; Zhenguo

Liu, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University; Xiaojiang Sun, The sixth People’sHospital

Affiliated to Shanghai JiaoTong University; Wei Li, Shanghai Ninth People’s Hospital Affiliated to Shanghai

JiaoTong University; Jianrong Liu, ShanghaiRuijin Hospital; Xu Chen, Shanghai 8th People’s Hospital; Qingke

Bai, Pudong New Area People’s Hospital; Dexiang Gu, Shanghai Yangpu Area Shidong Hospital; Xin Li, Shanghai

Yangpu Area Center Hospital; Qiang Dong, Huashan Hospital of Fudan University; Yan Cheng, Tianjin Medical

University Gengeal Hospital; Lan Yu, Tianjin Huanhu Hospital; Bin Li, Dagang Oilfield Gengeal Hospital; Tongyu

Wang, Bohai Oilfield Hospital; Kun Zhao, Baodi District People’s Hospital of Tianjin; Chaodong Zhang, The First

Affiliated Hospital of China Medical University; Dingbo Tao, The First Afflicated Hospital of Dlian Medical

University; Lin Yin, The Second Affiliated Hospital of Dlian Medical University; Fang Qu, Dlian Second People’s

hospital; Jingbo Zhang, Dlian Third People’s hospital; Jianfeng Wang, Dalian Central hospital; Ying Lian, Dalian

Economic and Technological Development District Hospital; Fang Qu, Shenying Military District General hospital

of Chinese People’s Liberation Army; Jun Fan, Shenyang Military District 202 Hospital; Ying Gao, National

Traditional Chinese Medicine (TCM)Thrombus Treatment Center of Liaoning Province; Mingdong Cheng,

En’liang hopital of Tai’an County; Jiang Wu, The First Clinical College of Jilin University; Huashan Sun, Jilin

Chemical Industrial Group General hopital; Jinying Li, Jilin Oilfield General Hospital; Guozhong Li, The First

Clinical College of Harbin Medical University; Yulan Zhu, The Second Clinical College of Harbin Medical

University; Zichao Yang, The Fourth Clinical College of Harbin Medical University; Fengmin Yang, Daqing

Oilfield General Hospital; Jun Zhou, Mudan Jiang Second hospital of Hailongjiang Province; Minxia Guo,

Shaanxi Provincial People’s Hospital; Zhengyi Li, The First Afflicated Hospital of Medical College of Xian

Jiaotong University; Qilin Ma, The First Hospital of Xiamen; Renbin Huang, Chenzhou First People’s Hospital;

Bo Xiao, Xiangya Hospital of Centre-south University; Kangning Chen, Southwest Hospital; Xinyue Qin, The

First Affiliated Hospital of Chongqing Medical University; Changlin Hu, The Second Affiliated Hospital of

Chongqing Medical University; Li Gao, Chengdu Third Municipal People’s Hospital; Jinsheng Zeng, The First

Affiliated Hospital of Sun Yat-Sen University; Anding Xu, The First Affiliated Hospital of Jinan University; Xiong

Zhang, Guangdong People’s Hospital; Ming Shao, The First Affiliated Hospital of Guangzhou Medical University;

Feng Qi, LiWan Hospital of GuangZhou Medical College; Weimin Xiao, Dungun Municipal People’s Hospital;

Suping Zhang, Guangzhou Red Cross Hospital; Xiaoping Pan, Guangzhou First TMUNICIPAL People’s Hospital;

Suyue Pan, Nan Fang Hospital; Yefeng Cai, Guangdong Provincial Hospital of Traditional Chinese Medicine; Qi

Wan, Jiang Su People’s Hospital; Yun Xu, Drum Tower Hospital Affiliated to Nanjing Medical University Upper

First-class Hospital; KaiFu Ke, he Affiliated Hospital of Nantong University Upper First class Hospital; Yuenan

Kong,Wuxi Second People’s Hospital Upper First-class Hospital; Qing Di, Neurology Hospital Affiliated to

Nanjing Medical University Upper First-class Hospital; Fengyang Shao, Jiangsu Province Lianyungang Hospital

of TCM Upper First-class Hospital; Yajun Jiang, Jiangsu Province Hospital of TCM Upper First-class Hospital;

Daming Wang, The First People’s Hospital of Changzhou Upper First-class Hospital; Li Guo, The Second Hospital

of Hebei Medical University; Wencui Xue, Qinhuangdao C.



on behalf of china national stroke registry investigators yongjun

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