Predicting functional outcome after stroke by modelling baseline clinical and CT variables

Background: We aimed to assess whether the performance of stroke outcome models comprising simple clinical variables could be improved by the addition of more complex clinical variables and information from the first computed tomography (CT) scan. Methods: 538 consecutive acute ischaemic and haemorrhagic stroke patients were enrolled in a Stroke Outcome Study between 2001 and 2002. Independent survival (modfied Rankin scale ≤2) was assessed at 6 months. Models based on clinical and radiological variables from the first assessment were developed using multivariate logistic regression analysis. Results: three models were developed (I-III). Model I included age, pre-stroke independence, arm power and a stroke severity score (area under a receiver operating characteristic curve, AUC = 0.882) but performed no better than Model II, which comprised age, pre-stroke independence, normal verbal component of the Glasgow coma score, arm power and being able to walk without assistance (AUC 0.876). Model III, including two radiological variables and clinical variables, was not statistically superior to model II (AUC 0.901, P = 0.12). Model II was externally validated in two independent datasets (AUCs of 0.773 and 0.787).Conclusion: this study demonstrates an externally validated stroke outcome prediction model using simple clinical variables. Outcome prediction was not signficantly improved with CT-derived radiological variables or more complex clinical variables.