Construction and validation of a risk prediction model for postoperative frailty in older adults:a multicenter study

BACKGROUND: day in elderly patients. METHODS: Data from seven Guangzhou hospitals were collected, encompassing 2,290 patients for analysis. This study constructed the model using LASSO regression and stepwise regression, and the optimal predictive model was selected based on comparison. Model performance was assessed through calibration curves, the area under the ROC curve (AUC), and decision curve analysis (DCA). RESULTS: The final model included the following variables: American Society of Anesthesiologists (ASA) grade, intraoperative blood loss, economic income, caregiver status, sedentary behavior, cognitive function, Activities of Daily Living (ADL), postoperative hemoglobin (Hb) level, and postoperative ICU admission. The model demonstrated good discrimination, with an area under the curve (AUC) of 0.7431 (95% CI = 0.7073-0.7788) in the training set and 0.7285 (95% CI = 0.6671-0.7624) in the validation set. CONCLUSIONS: According to general demographic information, lifestyle habits, and surgery-related factors, a predictive model for postoperative frailty in the elderly was constructed, which has good predictive power. This model can identify high-risk populations for postoperative frailty and provides a reference for the early detection and intervention of frailty in the elderly in clinical practice. TRIAL REGISTRATION: This study was registered on May 17, 2023, at the Chinese Clinical Trial Registry (registration number: ChiCTR2300071535).