Explainable machine learning for predicting in-hospital mortality in acute heart failure: a prospective single-center cohort study from Vietnam
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: Accurate risk stratification is essential in acute heart failure (AHF), which carries substantial in-hospital mortality. The Get With The Guidelines-Heart Failure (GWTG-HF) score is widely used, but its performance may vary across populations, and prospective data from Southeast Asia are scarce. We developed and internally validated explainable machine-learning (ML) models for in-hospital mortality in a Vietnamese AHF cohort and compared them with the GWTG-HF score. Methods: We prospectively enrolled 572 consecutive patients admitted with AHF (October 2024-June 2025). Missing data were imputed with a random forest-based algorithm, and class imbalance handled with SMOTE within training folds. Six algorithms (logistic regression, elastic net, random forest, XGBoost, LightGBM, CatBoost) were trained with stratified cross-validation, with optimism correction, and evaluated on an independent 25% test set. We assessed discrimination (ROC-AUC, PR-AUC), calibration (calibration slope/intercept, Brier and scaled Brier scores), classification, and clinical utility (decision-curve analysis); thresholds were derived from training data only. Pairwise AUC comparisons used the DeLong test, and interpretation used SHAP. Reporting followed TRIPOD + AI. Results: > 0.05), a comparison limited by only 13 test-set events. Although CatBoost had the numerically lowest Brier score (0.085), scaled Brier scores were low for all models and calibration intercepts were uniformly negative, indicating over-estimation of risk from aggressive imbalance correction; predicted probabilities therefore require recalibration. SHAP identified respiratory failure, NT-proBNP, high-sensitivity troponin I (risk-increasing) and an ADHF phenotype and left ventricular dilation (associated with lower predicted risk) as the most influential features. Conclusions: Explainable ML, led by random forest, achieved good discrimination but did not significantly outperform the GWTG-HF score, most plausibly reflecting limited statistical power rather than true equivalence. With miscalibrated probabilities requiring recalibration, explainable ML is best viewed as a complementary risk-ranking tool; external, multicenter, and temporal validation are required before clinical use.
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7223151487