Risk score for in-hospital ischemic stroke mortality derived and validated within the Get With the Guidelines-Stroke Program.

Published

Journal Article

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. METHODS AND 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.

Full Text

Duke Authors

Cited Authors

  • Smith, EE; Shobha, N; Dai, D; Olson, DM; Reeves, MJ; Saver, JL; Hernandez, AF; Peterson, ED; Fonarow, GC; Schwamm, LH

Published Date

  • October 12, 2010

Published In

Volume / Issue

  • 122 / 15

Start / End Page

  • 1496 - 1504

PubMed ID

  • 20876438

Pubmed Central ID

  • 20876438

Electronic International Standard Serial Number (EISSN)

  • 1524-4539

Digital Object Identifier (DOI)

  • 10.1161/CIRCULATIONAHA.109.932822

Language

  • eng

Conference Location

  • United States