Development of risk prediction model for delirium using routinely collected electronic health records

Background

Delirium, an acute confusional state in hospitalised older adults, is common and associated with poor outcomes. We developed and internally validated a machine learning model for early identification of patients at risk of delirium using routinely collected electronic health record data. Setting and Methods: Risk prediction model was developed using electronic health data from all adults over 50 years in a large metropolitan publicly funded hospital in Victoria, Australia. Candidate predictors, informed by the literature, were sourced from the first 48 hours of admission. Delirium diagnosed during admission was the outcome. Logistic regression (LR) and Random Forest (RF) models were developed using 26 variables, with 10-fold stratified nested cross-validation and multiple imputation, and internal validity estimated by Harrell bootstrap optimism correction over 200 repetitions. Model performance was assessed using area under curve (AUC) and calibration plots and metrics including decision curve analysis.