{
  "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.\n\nMethods: 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.\n\nResults: Fifty-three patients (9.3%) died in hospital. Random forest had the highest discrimination (AUC 0.846, 95% CI 0.757–0.922), followed by the GWTG-HF score (0.817), logistic regression (0.795), elastic net (0.794), CatBoost (0.786), XGBoost (0.763), and LightGBM (0.694). No ML model significantly outperformed the GWTG-HF score (all DeLong p > 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.\n\nConclusions: 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Internal Medicine, School of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City",
    "Department of Cardiology, Cho Ray Hospital",
    "Department of Cardiology, Nhan Dan Gia Dinh Hospital"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Sy Van Hoang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Kha Minh Nguyen"
    },
    {
      "affs": [
        0
      ],
      "name": "Tai Nhat Nguyen"
    },
    {
      "affs": [
        2
      ],
      "name": "Huy Nhat Do"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2nsw8ggpemc7y.bib",
  "cited_by": 0,
  "doi": "10.3389/fcvm.2026.1927124",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13731333/",
  "id": "2nsw8ggpemc7y",
  "issns": [
    "2297-055X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fcvm.2026.1927124/full",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2705|Heart Failure Treatment and Management"
    ]
  ],
  "openalex": "W7223151487",
  "pdf_url": "https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2026.1927124/pdf",
  "pmcid": "PMC13731333",
  "pmid": "42984620",
  "published": "2026-10-21",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/2nsw8ggpemc7y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:10Z",
      "native_id": "10.3389/fcvm.2026.1927124",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fcvm.2026.1927124/full"
    },
    {
      "first_seen": "2026-11-08T12:21:33Z",
      "native_id": "PMC13731333",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fcvm.2026.1927124"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2705|Heart Failure Treatment and Management",
      "subfield": "cardiovascular"
    }
  ],
  "title": "Explainable machine learning for predicting in-hospital mortality in acute heart failure: a prospective single-center cohort study from Vietnam",
  "title_text": "Explainable machine learning for predicting in-hospital mortality in acute heart failure: a prospective single-center cohort study from Vietnam",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:21:33Z",
  "url": "https://research.draconitic.com/paper/2nsw8ggpemc7y",
  "venue": "Frontiers in Cardiovascular Medicine",
  "versions": []
}