Abstract
BACKGROUND: This study aimed to identify factors influencing frailty in older adults with panvascular disease and to develop and validate a nomogram-based risk prediction model to support individualized frailty management. METHODS: A total of 1,344 patients aged ≥60 years with panvascular disease were recruited using convenience sampling from March to December 2024. Participants were randomly divided into training and validation sets (7:3). Data included general characteristics, laboratory indices, and scores from the PSQI, ADL, GDS-15, and frailty assessments. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors, followed by multivariate logistic regression to construct the model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curves, and decision curve analysis (DCA). RESULTS: Among 1,344 participants, 366 (27.23%) were frail. LASSO regression identified increasing age, multiple atherosclerotic sites, elevated LDL-C, hypertension history, high HbA1c, and low ADL as significant predictors of frailty. The AUC values for the training and validation sets were 0.78 and 0.77, respectively, indicating good discrimination. The Hosmer-Lemeshow test (χ(2) = 4.09, p > 0.05) and calibration curve demonstrated strong agreement between predicted and observed outcomes, confirming good model calibration and clinical utility. CONCLUSION: The developed nomogram-based model demonstrates strong predictive performance and can objectively estimate frailty risk in older adults with panvascular disease, providing a basis for early screening and targeted prevention strategies.