Frailty prediction in heart failure patients with acute infections: the potential role of thiazide diuretics?

Background Frailty remains a significant risk factor for adverse health outcomes in hospitalized patients. Research on frailty-related risk factors in patients with comorbidities (concurrent acute and chronic conditions) remains limited. Few have evaluated frailty risk and its influencing factors in heart failure (HF) patients during acute infection episodes. Previous research in machine learning has predominantly overlooked the incorporation of visualization techniques while consistently demonstrating suboptimal model accuracy. This study aims to investigate the risk factors for frailty in HF patients with acute infections and to develop a machine learning-based prediction model for frailty. Methods This study enrolled 1498 patients hospitalized for HF with acute infections at Nanjing First Hospital between January 1 and December 31, 2023. The study population was randomly divided into training and testing sets at a 7:3 ratio. Potential predictors were screened through univariate analysis and the least absolute shrinkage and selection operator (LASSO) regression. Eight machine learning algorithms were evaluated to determine the optimal predictive model. Model interpretability was enhanced using the SHapley Additive exPlanations (SHAP) method. Results Frailty was prevalent in 80.3% of the cohort. Key predictors included thiazide diuretics use, serum albumin, estimated glomerular filtration rate (eGFR), lymphocyte percentage, mean corpuscular hemoglobin concentration (MCHC), capacity for action, age, left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) functional class, history of cerebral infarction, and smoking. Comparative analysis of the eight models revealed that eXtreme Gradient Boosting (XGBoost) achieved superior performance, with the highest area under the receiver operating characteristic curve (AUROC: 0.872) and precision-recall curve (AUPRC: 0.969). The model’s robustness was further validated by calibration curves and decision curve analysis. SHAP analysis revealed that thiazide diuretics use is inversely associated with frailty risk. Conclusions The prediction model developed in this study incorporates 11 readily accessible predictors. A key innovation of this study lies in its pioneering inclusion of thiazide diuretics within the frailty prediction system. We successfully developed and deployed a clinically accessible online calculator based on the optimal XGBoost model. The web-based calculator offers a user-friendly clinical application, allowing real-time risk evaluation to guide timely therapeutic decisions. Clinical trial number not applicable.