title: Derivation and validation of in-hospital mortality prediction models in ischaemic stroke patients using administrative data.
abstract: BACKGROUND
  Stroke and other cerebrovascular diseases are a major cause of death and disability. Predicting in-hospital mortality in ischaemic stroke patients can help to identify high-risk patients and guide treatment approaches. Chart reviews provide important clinical information for mortality prediction, but are laborious and limiting in sample sizes. Administrative data allow for large-scale multi-institutional analyses but lack the necessary clinical information for outcome research. However, administrative claims data in Japan has seen the recent inclusion of patient consciousness and disability information, which may allow more accurate mortality prediction using administrative data alone. The aim of this study was to derive and validate models to predict in-hospital mortality in patients admitted for ischaemic stroke using administrative data.
  
  
  METHODS
  The sample consisted of 21,445 patients from 176 Japanese hospitals, who were randomly divided into derivation and validation subgroups. Multivariable logistic regression models were developed using 7- and 30-day and overall in-hospital mortality as dependent variables. Independent variables included patient age, sex, comorbidities upon admission, Japan Coma Scale (JCS) score, Barthel Index score, modified Rankin Scale (mRS) score, and admissions after hours and on weekends/public holidays. Models were developed in the derivation subgroup, and coefficients from these models were applied to the validation subgroup. Predictive ability was analysed using C-statistics; calibration was evaluated with Hosmer-Lemeshow χ(2) tests.
  
  
  RESULTS
  All three models showed predictive abilities similar or surpassing that of chart review-based models. The C-statistics were highest in the 7-day in-hospital mortality prediction model, at 0.906 and 0.901 in the derivation and validation subgroups, respectively. For the 30-day in-hospital mortality prediction models, the C-statistics for the derivation and validation subgroups were 0.893 and 0.872, respectively; in overall in-hospital mortality prediction these values were 0.883 and 0.876.
  
  
  CONCLUSIONS
  In this study, we have derived and validated in-hospital mortality prediction models for three different time spans using a large population of ischaemic stroke patients in a multi-institutional analysis. The recent inclusion of JCS, Barthel Index, and mRS scores in Japanese administrative data has allowed the prediction of in-hospital mortality with accuracy comparable to that of chart review analyses. The models developed using administrative data had consistently high predictive abilities for all models in both the derivation and validation subgroups. These results have implications in the role of administrative data in future mortality prediction analyses.
authors: Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
authors: Jason
authors: J
authors: Lee
authors: Toshitaka
authors: T
authors: Morishima
authors: Susumu
authors: S
authors: Kunisawa
authors: Noriko
authors: N
authors: Sasaki
authors: Tetsuya
authors: T
authors: Otsubo
authors: Hiroshi
authors: H
authors: Ikai
authors: Yuichi
authors: Y
authors: Imanaka
mesh_terms: D039602:After-Hours Care; D000368:Aged; D002545:Brain Ischemia / Q000175:diagnosis / Q000401:mortality* / Q000503:physiopathology; D016009:Chi-Square Distribution; D015897:Comorbidity; D003243:Consciousness; D004185:Disability Evaluation; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data; D007564:Japan / Q000453:epidemiology; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D016017:Odds Ratio; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D020521:Stroke / Q000175:diagnosis / Q000401:mortality* / Q000503:physiopathology; D013997:Time Factors