Prediction Model for Frailty in Middle-Aged and Older Adults with Cardiovascular Disease

Frailty is common among patients with cardiovascular disease (CVD) and is associated with adverse clinical outcomes. However, practical tools for predicting frailty risk in middle-aged and older patients with CVD remain limited. This study aimed to develop and validate a prediction model for frailty risk in patients with CVD.

A cross-sectional study was conducted using data from the 2015 China Health and Retirement Longitudinal Study (CHARLS). A total of 1184 participants aged ≥45 years with CVD were included and randomly divided into training and validation cohorts at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by multivariable logistic regression to construct a nomogram model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, the Hosmer-Lemeshow test, and decision curve analysis (DCA).

Frailty was identified in 148 participants (12.5%). Sleep duration, activities of daily living (ADL), waist circumference, cognitive function, handgrip strength, age, and depression were independent predictors of frailty. The nomogram demonstrated good discrimination, with area under the curve (AUC) values of 0.851 (95% CI: 0.814-0.888) in the training cohort and 0.861 (95% CI: 0.804-0.917) in the validation cohort. Calibration showed good agreement between predicted and observed outcomes (Hosmer-Lemeshow test, P>0.05). DCA indicated favorable clinical utility.

This nomogram provides a simple and effective tool for predicting frailty risk in patients with CVD and may facilitate early screening and risk stratification in clinical practice.