Construction and validation of an identification model for frailty in maintenance hemodialysis patients using multiple machine learning algorithms: a cross-sectional study of retrospectively collected data
TL;DR
The interpretable XGBoost model effectively identified prevalent frailty in MHD patients, demonstrating strong discrimination, calibration, and clinical applicability, and may support systematic frailty screening and individualized assessment.
Abstract
Abstract Background Frailty adversely affects survival and quality of life in patients undergoing maintenance hemodialysis (MHD). This study aimed to develop a clinical multimodal model to identify prevalent frailty in MHD patients. Methods Clinical data from 626 MHD patients across four tertiary hospitals were retrospectively collected, including demographics, clinical characteristics, laboratory indicators, and physical function assessments. Frailty was assessed using the FRAIL scale. Patients from Wuxi (n = 502) were divided into training (n = 352) and internal validation (n = 150) sets, while patients from Yancheng (n = 124) formed the external validation set. Least absolute shrinkage and selection operator (LASSO) and Boruta were used for feature selection. Ten machine learning models were developed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration, Brier score, and decision curve analysis. SHAP was applied for model interpretation. Results Frailty prevalence was 48.6%. Six predictors were identified: age, grip strength, comorbidities, Charlson comorbidity index (CCI), albumin, and pro-B-type natriuretic peptide (pro-BNP). The extreme gradient boosting (XGBoost) achieved the best external validation performance (AUC=0.917, 95% CI: 0.867–0.967; accuracy=0.847; sensitivity=0.850; specificity=0.844), with good calibration and clinical net benefit. The shapley additive explanations (SHAP) identified pro-BNP as the most influential predictor and revealed nonlinear associations and clinically relevant thresholds. Conclusion The interpretable XGBoost model effectively identified prevalent frailty in MHD patients, demonstrating strong discrimination, calibration, and clinical applicability, and may support systematic frailty screening and individualized assessment.