Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy.

Journal: Nature cardiovascular research
Published Date: Jul 2, 2025

Abstract

Sudden cardiac death from ventricular arrhythmias is a leading cause of mortality worldwide. Arrhythmic death prognostication is challenging in patients with hypertrophic cardiomyopathy (HCM), a setting where current clinical guidelines show low performance and inconsistent accuracy. Here, we present a deep learning approach, MAARS (Multimodal Artificial intelligence for ventricular Arrhythmia Risk Stratification), to forecast lethal arrhythmia events in patients with HCM by analyzing multimodal medical data. MAARS' transformer-based neural networks learn from electronic health records, echocardiogram and radiology reports, and contrast-enhanced cardiac magnetic resonance images, the latter being a unique feature of this model. MAARS achieves an area under the curve of 0.89 (95% confidence interval (CI) 0.79-0.94) and 0.81 (95% CI 0.69-0.93) in internal and external cohorts and outperforms current clinical guidelines by 0.27-0.35 (internal) and 0.22-0.30 (external). In contrast to clinical guidelines, it demonstrates fairness across demographic subgroups. We interpret MAARS' predictions on multiple levels to promote artificial intelligence transparency and derive risk factors warranting further investigation.

Authors

  • Changxin Lai
    Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Minglang Yin
    Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.
  • Eugene G Kholmovski
    Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.
  • Dan M Popescu
    Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, Maryland.
  • Dai-Yin Lu
    Hypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland; Division of Cardiology, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Public Health, National Yang-Ming University, Taipei, Taiwan.
  • Erica Scherer
    Sanger Heart & Vascular Institute, Atrium Health, Charlotte, NC, USA.
  • Edem Binka
    Division of Pediatric Cardiology, Department of Pediatrics, University of Utah, Salt Lake City, UT, USA.
  • Stefan L Zimmerman
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD 21287 (A.B., G.Z., I.R.K., S.L.Z., B.A.V.).
  • Jonathan Chrispin
    Division of Cardiology, Department of Medicine (D.R.O., J.C., S.J., K.C.W.).
  • Allison G Hays
    School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
  • Dermot M Phelan
    Sanger Heart & Vascular Institute, Atrium Health, Charlotte, NC, USA.
  • M Roselle Abraham
    Hypertrophic Cardiomyopathy Center of Excellence, Division of Cardiology, University of California, San Francisco, San Francisco, CA, USA.
  • Natalia A Trayanova
    Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, Maryland.

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