Identifying Mitral Valve Prolapse at Risk for Arrhythmias and Fibrosis From Electrocardiograms Using Deep Learning

Geoffrey H. Tison, Sean Abreau, Joshua Barrios, Lisa J. Lim, Michelle Yang, Valentina Crudo, Dipan J. Shah, Thuy Nguyen, Gene Hu, Shalini Dixit, Gregory Nah, Farzin Arya, Dwight Bibby, Yoojin Lee, Francesca N. Delling

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Mitral valve prolapse (MVP) is a common valvulopathy, with a subset developing sudden cardiac death or cardiac arrest. Complex ventricular ectopy (ComVE) is a marker of arrhythmic risk associated with myocardial fibrosis and increased mortality in MVP. Objectives: The authors sought to evaluate whether electrocardiogram (ECG)-based machine learning can identify MVP at risk for ComVE, death and/or myocardial fibrosis on cardiac magnetic resonance (CMR) imaging. Methods: A deep convolutional neural network (CNN) was trained to detect ComVE using 6,916 12-lead ECGs from 569 MVP patients from the University of California-San Francisco between 2012 and 2020. A separate CNN was trained to detect late gadolinium enhancement (LGE) using 1,369 ECGs from 87 MVP patients with contrast CMR. Results: The prevalence of ComVE was 28% (160/569). The area under the receiver operating characteristic curve (AUC) of the CNN to detect ComVE was 0.80 (95% CI: 0.77-0.83) and remained high after excluding patients with moderate-severe mitral regurgitation [0.80 (95% CI: 0.77-0.83)] or bileaflet MVP [0.81 (95% CI: 0.76-0.85)]. AUC to detect all-cause mortality was 0.82 (95% CI: 0.77-0.87). ECG segments relevant to ComVE prediction were related to ventricular depolarization/repolarization (early-mid ST-segment and QRS from V1, V3, and III). LGE in the papillary muscles or basal inferolateral wall was present in 24% patients with available CMR; AUC for detection of LGE was 0.75 (95% CI: 0.68-0.82). Conclusions: CNN-analyzed 12-lead ECGs can detect MVP at risk for ventricular arrhythmias, death and/or fibrosis and can identify novel ECG correlates of arrhythmic risk. ECG-based CNNs may help select those MVP patients requiring closer follow-up and/or a CMR.

Original languageEnglish (US)
Article number100446
JournalJACC: Advances
Volume2
Issue number6
DOIs
StatePublished - Aug 2023

Keywords

  • artificial intelligence
  • computers
  • echocardiography
  • valvular heart disease

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Dentistry (miscellaneous)

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