title: A risk score for in-hospital death in patients admitted with ischemic or hemorrhagic stroke.
abstract: BACKGROUND
  We aimed to derive and validate a single risk score for predicting death from ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH).
  
  
  RESULTS
  Data from 333 865 stroke patients (IS, 82.4%; ICH, 11.2%; SAH, 2.6%; uncertain type, 3.8%) in the Get With The Guidelines-Stroke database were used. In-hospital mortality varied greatly according to stroke type (IS, 5.5%; ICH, 27.2%; SAH, 25.1%; unknown type, 6.0%; P<0.001). The patients were randomly divided into derivation (60%) and validation (40%) samples. Logistic regression was used to determine the independent predictors of mortality and to assign point scores for a prediction model in the overall population and in the subset with the National Institutes of Health Stroke Scale (NIHSS) recorded (37.1%). The c statistic, a measure of how well the models discriminate the risk of death, was 0.78 in the overall validation sample and 0.86 in the model including NIHSS. The model with NIHSS performed nearly as well in each stroke type as in the overall model including all types (c statistics for IS alone, 0.85; for ICH alone, 0.83; for SAH alone, 0.83; uncertain type alone, 0.86). The calibration of the model was excellent, as demonstrated by plots of observed versus predicted mortality.
  
  
  CONCLUSIONS
  A single prediction score for all stroke types can be used to predict risk of in-hospital death following stroke admission. Incorporation of NIHSS information substantially improves this predictive accuracy.
authors.affiliation: Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada. eesmith@ucalgary.ca
authors.forename: Eric E
authors.initials: EE
authors.lastname: Smith
authors.forename: Nandavar
authors.initials: N
authors.lastname: Shobha
authors.forename: David
authors.initials: D
authors.lastname: Dai
authors.forename: DaiWai M
authors.initials: DM
authors.lastname: Olson
authors.forename: Mathew J
authors.initials: MJ
authors.lastname: Reeves
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Saver
authors.forename: Adrian F
authors.initials: AF
authors.lastname: Hernandez
authors.forename: Eric D
authors.initials: ED
authors.lastname: Peterson
authors.forename: Gregg C
authors.initials: GC
authors.lastname: Fonarow
authors.forename: Lee H
authors.initials: LH
authors.lastname: Schwamm
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002545:Brain Ischemia / Q000175:diagnosis / Q000401:mortality*; D002543:Cerebral Hemorrhage / Q000175:diagnosis / Q000401:mortality*; D016009:Chi-Square Distribution; D003661:Decision Support Techniques; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012042:Registries; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D020521:Stroke / Q000175:diagnosis / Q000401:mortality*; D013345:Subarachnoid Hemorrhage / Q000175:diagnosis / Q000401:mortality*; D014481:United States / Q000453:epidemiology