Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy.
Authors
Affiliations (8)
Affiliations (8)
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Hypertrophic Cardiomyopathy Center of Excellence, Division of Cardiology, University of California, San Francisco, San Francisco, CA, USA.
- Sanger Heart & Vascular Institute, Atrium Health, Charlotte, NC, USA.
- Division of Pediatric Cardiology, Department of Pediatrics, University of Utah, Salt Lake City, UT, USA.
- School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA. [email protected].
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. [email protected].
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.