Prediction model for postoperative delirium risk in elderly hypertensive patients: machine learning-based development and validation.

Background: Postoperative delirium (POD) is a severe complication in elderly hypertensive patients, associated with poor long-term outcomes. Existing models often rely on intraoperative data, limiting preoperative risk stratification. This study aimed to develop a non-invasive machine learning model to predict POD and investigate its preoperative markers' impact on three-year mortality. Methods: Preoperative variables were selected using LASSO regression from a cohort of 1,782 patients. Ten machine learning models were trained and validated (7:3 ratio). Model performance was evaluated via AUC-ROC and decision curve analysis (DCA). The optimal model was interpreted using SHAP values. Long-term prognosis within the POD cohort was assessed using Kaplan-Meier curves and multivariable Cox proportional hazards regression. Results: The POD incidence was 10.9%. The Gradient Boosting Machine (GBM) demonstrated optimal performance (AUC = 0.868, 95% CI: 0.819-0.917). SHAP analysis identified MMSE score as the most influential predictor, followed by HADS score, age, CFS, frailty, and PSQI score. Multivariable Cox analysis revealed that lower MMSE, alongside elevated HADS, CFS, frailty, and PSQI scores-but not chronological age-were independent predictors of increased three-year mortality in POD patients (all Conclusion: We developed a robust machine learning tool for individualized POD prediction. Cognitive impairment, psychological distress, frailty, and poor sleep quality serve as critical dual-prognostic markers for both acute POD occurrence and long-term survival. These findings underscore the necessity of routine multidimensional preoperative assessment to facilitate personalized interventions for vulnerable hypertensive populations.