• A W Oldenbeuving, P L M de Kort, J F van Eck van der Sluijs, L J Kappelle, and G Roks.
• Department of Intensive Care Medicine, St Elisabeth Hospital, , Tilburg, The Netherlands.
• J. Neurol. Neurosurg. Psychiatr.. 2014 Apr 1;85(4):431-4.

BackgroundWe developed and validated a risk score to predict delirium after stroke which was derived from our prospective cohort study where several risk factors were identified.MethodsUsing the β coefficients from the logistic regression model, we allocated a score to values of the risk factors. In the first model, stroke severity, stroke subtype, infection, stroke localisation, pre-existent cognitive decline and age were included. The second model included age, stroke severity, stroke subtype and infection. A third model only included age and stroke severity. The risk score was validated in an independent dataset.ResultsThe area under the curve (AUC) of the first model was 0.85 (sensitivity 86%, specificity 74%). In the second model, the AUC was 0.84 (sensitivity 80%, specificity 75%). The third model had an AUC of 0.80 (sensitivity 79%, specificity 73%). In the validation set, model 1 had an AUC of 0.83 (sensitivity 78%, specificity 77%). The second had an AUC of 0.83 (sensitivity 76%, specificity 81%). The third model gave an AUC of 0.82 (sensitivity of 73%, specificity 75%). We conclude that model 2 is easy to use in clinical practice and slightly better than model 3 and, therefore, was used to create risk tables to use as a tool in clinical practice.ConclusionsA model including age, stroke severity, stroke subtype and infection can be used to identify patients who have a high risk to develop delirium in the early phase of stroke.

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