ArticleFrontiers in cardiovascular medicine2026
Machine learning-based early identification of heart failure/congestion complicating acute coronary syndrome using body composition analysis: a retrospective cohort study.
Article in Frontiers in cardiovascular medicine, 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
Background: Heart failure (HF) is a major complication of acute coronary syndrome (ACS) and is associated with poor outcomes. Aims: To develop and internally evaluate a machine learning (ML) model for early identification/risk classification of HF or congestion among patients with ACS using bioelectrical impedance analysis (BIA)-derived body composition parameters. Methods: This single-center retrospective cohort study included 527 patients with ACS. Body composition was assessed within 48 h of admission using multi-frequency direct segmental BIA. The dataset was first randomly partitioned into training and locked test cohorts. Imputation, standardization, feature selection, and model selection were performed using training data only. A total of 120 model pipelines were compared by training-stage cross-validation, and the locked test set was reserved for one final evaluation. Results: HF was identified in 198 patients (37.6%). The final plsRglm + glmBoost model used six BIA/body-composition variables: Protein, Muscle-Right arm, Muscle-Trunk, ECW/TBW ratio-Trunk, ECW/TBW ratio-Left leg, and arm muscle circumference. In the locked test set, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.793 (95% CI 0.719-0.859) and a Brier score of 0.186 (95% CI 0.154-0.221). Calibration assessment showed an intercept of 0.266 (95% CI -0.145 to 0.709) and slope of 0.918 (95% CI 0.645-1.287). SHAP analysis indicated that ECW/TBW ratio-Trunk and ECW/TBW ratio-Left leg were the most influential predictors. Conclusions: A leakage-free ML workflow using bedside BIA-derived body composition parameters showed moderate discrimination for early identification/risk classification of HF/congestion among patients with ACS. Because of the retrospective single-center design, limited timing information, and absence of external validation, the model should not be interpreted as proving prospective prediction of future HF events.
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