Predicting functional outcome in acute stroke: comparison of a simple six variable model with other predictive systems and informal clinical prediction

Background: Statistical models that predict functional outcome after stroke using six simple variables (SSV) have recently been developed and validated.
Objective: To compare the accuracy of these models with other simple ways of predicting outcome soon after stroke.
Methods: The SSV model for being alive and independent ( modified Rankin score less than or equal to2) six months or one year after stroke was compared with predictions based on a model that included only age and Oxford community stroke project classification, with predictions based on conscious level and urinary continence, and with informal clinical predictions made by clinicians interested in stroke. Predictions were compared in an independent hospital based cohort of stroke patients using receiver operator characteristic (ROC) curves.
Results: The SSV model at six months had a significantly greater area under the curve (0.84) than the model with only age and stroke classification (0.75). Predictions based on conscious level and urinary continence were no better than those of the SSV model and were unable to predict subjects with a high probability of good outcome. The sensitivity and specificity for informal clinical predictions at one year lay on or below the SSV model curve, implying that the SSV model was at least as good as clinical predictions.
Conclusions: The SSV models performed as well as or better than other simple predictive systems. These models will be useful in epidemiological studies but should not be used to guide clinical management until their impact on patient care and outcome has been evaluated.