Abstract
The study aims to develop and validate an effective model for predicting frailty risk in individuals with mild cognitive impairment (MCI). The cross-sectional analysis employed nationally representative data from CHARLS 2013-2015. The sample was randomly divided into training (70%) and validation sets (30%). The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression model were used to identify independent predictors and establish a nomogram to predict the occurrence of frailty. The receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) were used to evaluate the performance of the nomogram. A total of 3,196 MCI patients were analyzed, and 803 (25.1%) exhibited symptoms of frailty. Multivariate logistic regression analysis revealed that age, activities of daily living (ADL) score, depression score, grip strength, cardiovascular disease (CVD), liver disease, pain, hearing, and vision were associated factors for frailty in MCI patients. The nomogram based on these factors achieved AUC values of 0.810 (95% CI 0.780, 0.840) in the training set and 0.791 (95% CI 0.760, 0.820) in the validation set. Calibration curves showed good agreement between the nomogram and the observed values. The Hosmer-Lemeshow test results for the training and validation sets were P = 0.396 and P = 0.518, respectively. The ROC curve and decision curve analysis further validated the robust predictive ability of the nomogram. The application of this model may facilitate early clinical interventions, thereby potentially reducing the incidence of frailty among patients with MCI and significantly enhancing their long-term health outcomes.