Establishment and validation of a risk prediction model for the occurrence of frailty in elderly patients with heart failure with preserved ejection fraction

Objective: This study aimed to investigate the determinants of frailty in elderly patients with heart failure with preserved ejection fraction (HFpEF) and to develop and validate a predictive nomogram for frailty incidence. Methods: The study enrolled 206 elderly patients with chronic HFpEF, admitted to the Department of Geriatric Cardiology at the First Affiliated Hospital of Nanjing Medical University, from September 2021 to August 2023. The Fried frailty phenotype scale was used to evaluate all patients, who were then categorized into frailty and non-frailty groups. The participants were randomly allocated to either the training or validation group in a 7:3 ratio. Clinical data between the two groups were compared, and a univariate analysis was conducted using 52 clinical variables as independent variables. Predictive factors were selected from those with statistically significant differences in the univariate analysis through LASSO regression, followed by multivariate logistic regression analysis. The HFpEF frailty predictive nomogram was developed using R 4.2 software. The nomogram's performance was assessed using ROC curve analysis, Hosmer-Lemeshow goodness-of-fit test, calibration curve, and clinical decision curve. Results: Following LASSO regression selection, multivariate logistic regression analysis revealed that age, grip strength, MNA score, albumin, and tricuspid regurgitation velocity were independent risk factors for frailty incidence in HFpEF patients. The nomogram was developed based on these logistic regression results. The AUC of the ROC curve for the nomogram in the training set was 0.950 (95% CI: 0.911-0.869), and in the validation set, it was 0.932 (95% CI: 0.882-0.867), demonstrating strong discriminant performance of the model. The Hosmer-Lemeshow goodness-of-fit test indicated a good fit of the nomogram (χ2=4.761, P=0.783). The decision curve analysis (DCA) curve showed a significant net clinical benefit of the model. Conclusion: The predictive model developed in this study exhibits strong predictive value for frailty incidence in HFpEF patients, offering a foundation for precise treatment of elderly HFpEF patients.