Development and validation of a nomogram for predicting frailty in older adults with multimorbidity: a cross-sectional study

Abstract Objectives To identify the risk factors for frailty in older adults with multimorbidity and to develop and validate a nomogram-based risk prediction model. Methods This cross-sectional study recruited participants using convenience sampling from March 2022 to October 2023. Participants were randomly assigned to a training set (n=266) and a validation set (n=114) in a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression was used for predictor selection, followed by binary logistic regression to identify factors associated with frailty. A nomogram-based prediction model was developed, and its performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). Results Among the 380 participants, 209 (55.0 %) were identified as frailty. The nomogram model included six predictors: age, marital status, residence type, number of children, exercise frequency, and ADL (Activities of Daily Living). The model demonstrated good consistency and accuracy. The nomogram exhibited good discrimination, with AUC values of 0.777 (95 % CI: 0.722–0.831) in the training set and 0.756 (95 % CI: 0.667–0.845) in the validation set. Calibration curves demonstrated good agreement between predicted and observed outcomes, and DCA showed high clinical utility. Conclusions The frailty nomogram model developed in this study for older adults with multimorbidity demonstrates strong predictive performance and serves as a valuable tool for early clinical screening of high-risk groups, facilitating timely interventions in both clinical and community settings.