Identification of Risk Factors and Development of a Prediction Model for Recovery Room Delirium Following Transurethral Resection of the Prostate

Abstract

Purpose To identify independent risk factors for recovery room delirium (RRD) in patients undergoing transurethral resection of the prostate (TURP) and to develop a predictive model for early identification of high-risk patients. Patients and Methods We retrospectively reviewed 1166 adult patients who underwent TURP under general anesthesia between January 2020 and December 2024. RRD was assessed using the 3-minute Diagnostic Interview for CAM-defined Delirium (3D-CAM). Patients were randomly assigned to the training set (n = 816) and the validation set (n = 350). Independent risk factors were identified by multivariate logistic regression, and a nomogram was constructed. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results The incidence of RRD was 19.5% in the training set and 20% in the validation set. Multivariable logistic regression identified older age, higher ASA physical status classification, preoperative hypoalbuminemia, intraoperative hypothermia, and intraoperative hypotension as independent predictors associated with an increased risk of RRD. Based on these predictors, a nomogram was established. The model demonstrated good discrimination, with an AUC of 0.841 (95% CI, 0.806–0.876) in the training cohort and 0.858 (95% CI, 0.810–0.906) in the validation cohort. Calibration analysis showed good agreement between the predicted and observed probabilities, and decision curve analysis demonstrated favorable clinical utility across a wide range of threshold probabilities. Conclusion The RRD prediction model incorporating age, ASA classification, preoperative hypoalbuminemia, intraoperative hypothermia, and hypotension demonstrated promising discriminatory ability and calibration in this single-center cohort. However, prospective external validation is required before widespread clinical implementation.