Integrating Clinical Information and Electrocardiographic Signal Features to Develop a Prediction Model for Postoperative Delirium in Older Patients: A Prospective Observational Study

PURPOSE: Postoperative delirium (POD) is a frequent and serious complication in older surgical patients. While conventional prediction models rely heavily on subjective clinical indicators, this study aimed to develop a simple, non-invasive preoperative risk prediction model by integrating objective physiological signals (electrocardiographic [ECG] features and heart rate variability [HRV]) with conventional demographic and clinical data. PATIENTS AND METHODS: In this single-center prospective observational study, 767 patients aged ≥65 years who underwent elective non-cardiac surgery were included. Preoperative clinical characteristics, conventional ECG abnormalities, and HRV parameters were extracted. Independent predictors were identified via multivariate logistic regression. A combined clinical-ECG/HRV model was developed, internally validated, and compared against single-domain baseline models. RESULTS: POD occurred in 185 patients (24.1%). Advanced age, lower educational level, prolonged operative duration, non-sinus rhythm, ST-segment abnormalities, atrial/ventricular arrhythmias, and higher HRV fuzzy entropy were identified as independent risk factors for POD. The integrated clinical-ECG/HRV model achieved areas under the receiver-operating curves of 0.842 in the training set and 0.783 in the testing set, demonstrating higher predictive accuracy and improved risk reclassification compared with the baseline clinical model. CONCLUSION: Preoperative ECG anomalies and increased HRV complexity (fuzzy entropy) are independently associated with an increased risk of POD. Integrating these objective, non-invasive physiological signals with routine clinical data yields a simple and practical prediction model. This multimodal approach enhances early preoperative risk stratification, providing valuable evidence to support comprehensive perioperative management for older surgical patients.