Development and validation of a risk prediction model for frailty in patients with chronic diseases

Abstract

Background The occurrence rate of frailty is high among patients with chronic diseases. However, the assessment of frailty among these patients is still far from being a routine part of clinical practice. The aim of this study is to develop a validated predictive model for assessing frailty risk in patients with chronic illnesses. Methods This study utilized survey data from elderly chronic disease patients (aged ≥ 60 years) at a tertiary hospital in China between 2022 and 2023. A total of 57 indicators were analyzed, encompassing sociodemographic variables, health status, physical measurements, nutritional assessment, physical activity levels, and blood biomarkers. The research cohort was randomly divided into training and validation sets at a ratio of 70–30%. Employing LASSO regression analysis, the study selected the optimal predictive factors based on univariate analysis. Logistic regression models were applied to investigate factors associated with frailty in chronic disease patients. A nomogram was constructed to establish the predictive model. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve, calibration curves, and decision curve analysis. Results This study recruited a total of 543 patients with chronic diseases, among which 237 were included in the development and validation of the predictive model. There were 100 cases (42.2%) presenting frailty symptoms. Multivariate logistic regression analysis revealed that gender, age, chronic diseases, Mini Nutritional Assessment (MNA) score, and Clinical Frailty Scale (CFS) score were predictive factors for frailty in chronic disease patients. Utilizing these factors, a nomogram model demonstrated good consistency and accuracy. The AUC values for the predictive model and validation set were 0.946 and 0.945, respectively. Calibration curves, ROC, and DCA indicated the nomogram had favorable predictive performance. Conclusions The comprehensive nomogram developed in this study is a promising and convenient tool for assessing frailty risk in patients with chronic diseases, aiding clinical practitioners in screening high-risk populations. Registration: ChICTR2300068076 (first recruitment date was 2022/07/06)