Identifying Delirium Early after Stroke: A New Prediction Tool for the Intensive Care Unit

Abstract

BackgroundDelirium is common after stroke and associated with poor functional outcomes and mortality. It is unknown whether delirium is a modifiable risk factor, or simply an indicator of prognosis, but in order to intervene successfully, those at greatest risk must be identified early. We created a tool to predict the development of delirium in patients admitted to the intensive care unit for stroke, focusing on factors present on hospital admission. MethodsCharts of 102 patients admitted to the ICU or IMC after ischemic stroke or intracranial hemorrhage with symptom onset within 72 hours were reviewed. Delirium was identified using the Confusion Assessment Method for the ICU (CAM-ICU). Factors significantly associated with delirium were included in a multivariable logistic regression analysis to create a predictive model. The model was validated in a unique inpatient cohort. ResultsIn regression analyses, the variables present on admission most strongly associated with the development of delirium after stroke included: age greater than 64 years; intraventricular hemorrhage; intubation; presence of either cognitive dysfunction, aphasia, or neglect; and acute kidney injury. Using these variables in our predictive model, an ROC analysis resulted in an area under the curve of 0.90, and 0.82 in our validation cohort. ConclusionsFactors available on admission can be used to accurately predict risk of delirium following stroke. Our model can be used to implement more rigorous screening paradigms, allowing for earlier detection and timely treatment. Futures studies will focus on determining if prevention can mitigate the poor outcomes with which delirium is associated.