Development and validation of an online machine-learning tool for predicting delirium risk in older adults with type 2 diabetes: A retrospective cohort study based on the MIMIC-IV database.

ObjectiveIn older adults with Type 2 diabetes mellitus (T2DM), the risk of delirium is significantly increased, driven by neuropathological alterations stemming from chronic insulin resistance. We utilized artificial intelligence and geriatric electronic health records to create an interpretable online machine-learning algorithm for predicting delirium risk. This tool facilitates prompt identification of high-risk elderly T2DM patients, enabling optimized interventions and improved clinical outcomes.MethodsThis retrospective cohort study identified older adults with T2DM using International Classification of Diseases (ICD) codes, with delirium defined by the Confusion Assessment Method for the intensive care unit (CAM-ICU). We extracted baseline demographics, vital signs, laboratory measurements, comorbidities and clinical severity scores. Candidate predictors for eight machine-learning algorithms were selected using least absolute shrinkage and selection operator regression and the Boruta method. Discrimination was assessed using accuracy, sensitivity, specificity and the F1 score. The final model was interpreted using SHapley Additive exPlanations (SHAP) and deployed as an online risk calculator.ResultsIntegrating dual feature selection methods identified 14 key predictors and the gradient boosting machine (GBM) model accurately predicted delirium risk in elderly patients with T2DM, demonstrating strong discriminatory performance with robust calibration in both internal and external validation. SHAP analysis highlighted the Glasgow Coma Scale, ICU length of stay and Sequential Organ Failure Assessment score as the predominant contributors to model predictions. The model was successfully deployed as an accessible online tool and the accompanying web-based calculator enables rapid, personalized risk assessment to support early intervention in ICU settings.ConclusionsThe GBM model showed strong performance in predicting delirium risk among elderly patients with T2DM, supporting clinically meaningful risk stratification. The accompanying web-based calculator enables rapid, individualized bedside assessment and may facilitate early identification of high-risk patients and timely intervention in ICU settings.