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 ). Methods and Results: Data from 333 865 stroke patients ( IS , 82.4%; ICH , 11.2%; SAH , 2.6%; uncertain type, 3.8%) in the G et W ith T he G uidelines— S troke 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.