ArticlePloS one2026
Frailty prediction in heart failure patients with acute infections: the potential role of thiazide diuretics?
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundFrailty remains a significant risk factor for adverse health outcomes in hospitalized patients. Few have evaluated frailty risk and its influencing factors in heart failure (HF) patients with acute infections, and previous machine learning models have predominantly overlooked the incorporation of visualization techniques. This study aims to investigate frailty risk factors in this population and develop an interpretable prediction model for frailty.
methodsThis study enrolled 1498 patients hospitalized for HF with acute infections at Nanjing First Hospital in 2023. Participants were 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.
resultsFrailty was prevalent in 80.3% of the cohort. Key predictors included the use of thiazide diuretics, 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).
conclusionsThis study identified the use of thiazide diuretics as an independent predictor associated with lower frailty probability. We developed an online calculator as a proof-of-concept tool to demonstrate the potential application of the predictive model and facilitate real-time risk estimation.
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