• Carl Counsell, Martin Dennis, Michael McDowall, and Charles Warlow.
• Department of Clinical Neurosciences, Western General Hospital, Edinburgh, Scotland. carl.counsell@abdn.ac.uk
• Stroke. 2002 Apr 1;33(4):1041-7.

Background And PurposeStatistical models to predict the outcome of patients with acute and subacute stroke could have several uses, but no adequate models exist. We therefore developed and validated new models.MethodsRegression models to predict survival to 30 days after stroke and survival in a nondisabled state at 6 months were produced with the use of established guidelines on 530 patients from a stroke incidence study. Three models were produced for each outcome with progressively more detailed sets of predictor variables collected within 30 days of stroke onset. The models were externally validated and compared on 2 independent cohorts of stroke patients (538 and 1330 patients) by calculating the area under receiver operating characteristic curves (AUC) and by plotting calibration graphs.ResultsModels that included only 6 simple variables (age, living alone, independence in activities of daily living before the stroke, the verbal component of the Glasgow Coma Scale, arm power, ability to walk) generally performed as well as more complex models in both validation cohorts (AUC 0.84 to 0.88). They had good calibration but were overoptimistic in patients with the highest predicted probabilities of being independent. There were no differences in AUCs between patients seen within 48 hours of stroke onset and those seen later; between ischemic and hemorrhagic strokes; and between those with and without a previous stroke.ConclusionsThe simple models performed well enough to be used for epidemiological purposes such as stratification in trials or correction for case mix. However, clinicians should be cautious about using these models, especially in hyperacute stroke, to influence individual patient management until they have been further evaluated. Further research is required to test whether additional information from brain imaging improves predictive accuracy.

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