An early prediction of delirium in the acute phase after stroke

J Neurol Neurosurg Psychiatry. 2014 Apr;85(4):431-4. doi: 10.1136/jnnp-2013-304920. Epub 2013 Jun 6.


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.


Publication types

  • Validation Study

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Aged, 80 and over
  • Cognition Disorders / complications
  • Cognition Disorders / diagnosis
  • Cognition Disorders / psychology
  • Delirium / complications*
  • Delirium / diagnosis*
  • Delirium / psychology
  • Female
  • Humans
  • Infections / complications
  • Infections / diagnosis
  • Infections / psychology
  • Logistic Models
  • Male
  • Middle Aged
  • Prognosis
  • Risk Factors
  • Sensitivity and Specificity
  • Severity of Illness Index
  • Stroke / complications*
  • Stroke / diagnosis
  • Stroke / psychology