A predictive risk model for outcomes of ischemic stroke

Background and Purpose-The great variability of outcome seen in stroke pati ents has led to an interest in identifying predictors of outcome. The combi nation of clinical and imaging variables as predictors of stroke outcome in a multivariable risk adjustment model may be more powerful than either alo ne. The purpose of this study was to determine the multivariable relationsh ip between infarct volume, 6 clinical variables, and 3-month outcomes in is chemic stroke patients. Methods-Included in the study were 256 eligible patients from the Randomize d Trial of Tirilazad Mesylate in Acute Stroke (RANTTAS). Six clinical varia bles and I-week infarct volume were the prespecified predictor variables, T he National Institutes of Health Stroke Scale, Barthel Index, and Glasgow O utcome Scale were the outcomes. Multivariable logistic regression technique s were used to develop the model equations, and bootstrap techniques were u sed for internal validation. Predictive performance of the models was asses sed for discrimination with receiver operator characteristic (ROC) curves a nd for calibration with calibration curves. Results-The predictive models had areas under the ROC curve of 0.79 to 0.88 and demonstrated nearly ideal calibration curves. The areas under the ROC curves were statistically greater (P<0.001) with both clinical and imaging information combined than with either alone for predicting excellent recove ry and death or seven disability. Conclusions-Combined clinical and imaging variables are predictive of 3-mon th outcome in ischemic stroke patients. Demonstration of this relationship with acute clinical variables and 1-week infarct information supports futur e attempts to predict 3-month outcome with all acute variables.