Predicting Delirium Risk Using an Automated Mayo Delirium Prediction Tool: Development and Validation of a Risk-Stratification Model

Abstract

ObjectiveTo develop a delirium risk-prediction tool that is applicable across different clinical patient populations and can predict the risk of delirium at admission to hospital. MethodsThis retrospective study included 120,764 patients admitted to Mayo Clinic between January 1, 2012, and December 31, 2017, with age 50 and greater. The study group was randomized into a derivation cohort (n=80,000) and a validation cohort (n=40,764). Different risk factors were extracted and analyzed using least absolute shrinkage and selection operator (LASSO) penalized logistic regression. ResultsThe area under the receiver operating characteristic curve (AUROC) for Mayo Delirium Prediction (MDP) tool using derivation cohort was 0.85 (95% confidence interval [CI], .846 to .855). Using the regression coefficients obtained from the derivation cohort, predicted probability of delirium was calculated for each patient in the validation cohort. For the validation cohort, AUROC was 0.84 (95% CI, .834 to .847). Patients were classified into 1 of the 3 risk groups, based on their predicted probability of delirium: low (≤5%), moderate (6% to 29%), and high (≥30%). In the derivation cohort, observed incidence of delirium was 1.7%, 12.8%, and 44.8% (low, moderate, and high risk, respectively), which is similar to the incidence rates in the validation cohort of 1.9%, 12.7%, and 46.3%. ConclusionThe Mayo Delirium Prediction tool was developed from a large heterogeneous patient population with good validation results and appears to be a reliable automated tool for delirium risk prediction with hospitalization. Further prospective validation studies are required.