title: Risk score for in-hospital ischemic stroke mortality derived and validated within the Get With the Guidelines-Stroke Program.
abstract: BACKGROUND
  There are few validated models for prediction of in-hospital mortality after ischemic stroke. We used Get With the Guidelines-Stroke Program data to derive and validate prediction models for a patient's risk of in-hospital ischemic stroke mortality.
  
  
  RESULTS
  Between October 2001 and December 2007, there were 1036 hospitals that contributed 274,988 ischemic stroke patients to this study. The sample was randomly divided into a derivation (60%) and validation (40%) sample. Logistic regression was used to determine the independent predictors of mortality and to assign point scores for a prediction model. We also separately derived and validated a model in the 109,187 patients (39.7%) with a National Institutes of Health Stroke Scale (NIHSS) score recorded. Model discrimination was quantified by calculating the C statistic from the validation sample. In-hospital mortality was 5.5% overall and 5.2% in the subset in which NIHSS score was recorded. Characteristics associated with in-hospital mortality were age, arrival mode (eg, via ambulance versus other mode), history of atrial fibrillation, previous stroke, previous myocardial infarction, carotid stenosis, diabetes mellitus, peripheral vascular disease, hypertension, history of dyslipidemia, current smoking, and weekend or night admission. The C statistic was 0.72 in the overall validation sample and 0.85 in the model that included NIHSS score. A model with NIHSS score alone provided nearly as good discrimination (C statistic 0.83). Plots of observed versus predicted mortality showed excellent model calibration in the validation sample.
  
  
  CONCLUSIONS
  The Get With the Guidelines-Stroke risk model provides clinicians with a well-validated, practical bedside tool for mortality risk stratification. The NIHSS score provides substantial incremental information on a patient's short-term mortality risk and is the strongest predictor of mortality.
authors.affiliation: Calgary Stroke Program, 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: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001281:Atrial Fibrillation; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D020407:Internet; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012042:Registries; D012189:Retrospective Studies; D012307:Risk Factors; D012720:Severity of Illness Index; D020521:Stroke / Q000453:epidemiology* / Q000401:mortality*