Background We 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.
Methods Using 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.
Results The 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.
Conclusions A 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.