{
  "abstract": "BackgroundEstablished heart-failure (HF) risk scores were developed largely in Western cohorts, assume linear predictor effects, and stop at prognosis without indicating action. We developed and externally validated machine-learning (ML) models for 30-day and 1-year mortality in a multi-ethnic Asian HF population, benchmarked against three established scores, and tested whether they localised correctable guideline-directed medical therapy (GDMT) gaps.\n\nMethodsWe analysed 9,050 adults hospitalised with HF across eight Singapore hospitals (November 2016-March 2023). Gradient-boosted trees (XGBoost, primary) and elastic-net logistic regression were developed on three hospitals (n=4,034) and validated on five independent hospitals (n=5,016), without patient or site overlap. Comparators (MAGGIC, Singapore HF Score, OPTIMIZE-HF-Asia) were logistically recalibrated on development data.\n\nResultsOn external validation XGBoost achieved AUROCs of 0.862 (95% CI 0.833-0.890) for 30-day and 0.749 (0.734-0.764) for 1-year mortality, versus 0.711-0.754 and 0.682-0.699 for the three recalibrated scores; logistic regression performed near-identically (0.864, 0.757) and was better calibrated at 1 year. Superiority held under temporal validation, multiple imputation and optimism correction, although the 30-day advantage was no longer distinguishable from MAGGIC once medications were removed. Among predicted-high-risk patients with reduced ejection fraction, mean GDMT exposure was 0.92 of four pillars versus 1.87 across all HFrEF patients, and 36.3% received none of the four versus 2.0% of low-risk patients. Ten variables reproduced full-model 30-day discrimination (AUROC 0.865).\n\nConclusionsIn external validation, interpretable models substantially outperformed established HF risk scores for short- and long-term mortality and simultaneously localised correctable therapeutic gaps, supporting a prediction-to-action tool deployable with ten bedside variables.\n\nClinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LIIn 9,050 patients hospitalised for heart failure across eight Singapore hospitals, prediction models estimated 30-day and 1-year all-cause mortality substantially better than three established risk scores (30-day AUROC 0.86 versus 0.71-0.75), validated in five hospitals independent of model development. C_LIO_LITen routinely recorded bedside variables reproduced the full 85-variable models 30-day discrimination. This enables bedside risk estimation without electronic health record integration. C_LI\n\nWhat Are the Clinical Implications?O_LIThe same model identifies both who is at risk and which guideline-directed therapies they lack: flagged patients were receiving a mean of 0.9 of the four pillars, against 1.9 across all patients with reduced ejection fraction, although most non-prescription had a documented renal or haemodynamic contraindication, so this correctable fraction is an upper bound requiring prospective validation. C_LI",
  "abstract_shown": true,
  "affiliations": [
    "National University of Singapore"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 17,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. M. Jauhari"
    },
    {
      "name": "W. L. Yeung"
    },
    {
      "name": "T. Li"
    },
    {
      "name": "J. Chua"
    },
    {
      "name": "A. A. Zhang"
    },
    {
      "name": "E.-H. Lim"
    },
    {
      "name": "R. Cherian"
    },
    {
      "name": "D. Sim"
    },
    {
      "name": "S. Lee Shao Guang"
    },
    {
      "name": "D. K. N. Soon"
    },
    {
      "name": "P. F. Chan"
    },
    {
      "name": "S. Y. Loh"
    },
    {
      "name": "F. R. Jaufeerally"
    },
    {
      "name": "S.-P. Chan"
    },
    {
      "name": "M. Y.-Y. Chan"
    },
    {
      "name": "W. Lin"
    },
    {
      "name": "R. C. C. Wong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3ggzaw62pmghb.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.10.26362800",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.10.26362800v1.full",
  "id": "3ggzaw62pmghb",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.10.26362800v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "cardiovascular medicine"
    ],
    [
      "openalex",
      "2705|Heart Failure Treatment and Management"
    ]
  ],
  "openalex": "W7212424977",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.10.26362800v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3ggzaw62pmghb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:28Z",
      "native_id": "10.64898/2026.09.10.26362800",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.10.26362800v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:cardiovascular medicine",
      "subfield": "cardiovascular"
    }
  ],
  "title": "\"Mortality Prediction in Heart Failure using Explainable AI: Development and Validation of the EXACT-HF Model in a Nationwide Multi-Ethnic Asian Cohort\"",
  "title_text": "\"Mortality Prediction in Heart Failure using Explainable AI: Development and Validation of the EXACT-HF Model in a Nationwide Multi-Ethnic Asian Cohort\"",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:28Z",
  "url": "https://research.draconitic.com/paper/3ggzaw62pmghb",
  "venue": "medRxiv",
  "versions": []
}