| Title: | Deep Learning to Predict Cardiac Magnetic Resonance-Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs |
| Journal: | Circulation Cardiovascular Imaging |
| Published: | 15 Jun 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34126762/ |
| DOI: | https://doi.org/10.1161/circimaging.120.012281 |
| URL: | https://www.ahajournals.org/doi/pdf/10.1161/CIRCIMAGING.120.012281 |
Publication 3726
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Abstract
BACKGROUND: Classical methods for detecting left ventricular (LV) hypertrophy (LVH) using 12-lead ECGs are insensitive. Deep learning models using ECG to infer cardiac magnetic resonance (CMR)-derived LV mass may improve LVH detection. METHODS: Within 32 239 individuals of the UK Biobank prospective cohort who underwent CMR and 12-lead ECG, we trained a convolutional neural network to predict CMR-derived LV mass using 12-lead ECGs (left ventricular mass-artificial intelligence [LVM-AI]). In independent test sets (UK Biobank [n=4903] and Mass General Brigham [MGB, n=1371]), we assessed correlation between LVM-AI predicted and CMR-derived LV mass and compared LVH discrimination using LVM-AI versus traditional ECG-based rules (ie, Sokolow-Lyon, Cornell, lead aVL rule, or any ECG rule). In the UK Biobank and an ambulatory MGB cohort (MGB outcomes, n=28 612), we assessed associations between LVM-AI predicted LVH and incident cardiovascular outcomes using age- and sex-adjusted Cox regression. RESULTS: LVM-AI predicted LV mass correlated with CMR-derived LV mass in both test sets, although correlation was greater in the UK Biobank (r=0.79) versus MGB (r=0.60, P<0.001 for both). When compared with any ECG rule, LVM-AI demonstrated similar LVH discrimination in the UK Biobank (LVM-AI c-statistic 0.653 [95% CI, 0.608 -0.698] versus any ECG rule c-statistic 0.618 [95% CI, 0.574 -0.663], P=0.11) and superior discrimination in MGB (0.621; 95% CI, 0.592 -0.649 versus 0.588; 95% CI, 0.564 -0.611, P=0.02). LVM-AI-predicted LVH was associated with incident atrial fibrillation, myocardial infarction, heart failure, and ventricular arrhythmias. CONCLUSIONS: Deep learning-inferred LV mass estimates from 12-lead ECGs correlate with CMR-derived LV mass, associate with incident cardiovascular disease, and may improve LVH discrimination compared to traditional ECG rules.11 Authors
- Shaan Khurshid
- Samuel Friedman
- James P. Pirruccello
- Paolo Di Achille
- Nathaniel Diamant
- Christopher D. Anderson
- Patrick T. Ellinor
- Puneet Batra
- Jennifer E. Ho
- Anthony A. Philippakis
- Steven A. Lubitz
1 Application
| Application ID | Title |
|---|---|
| 7089 | Exome Sequencing of All Premature Coronary Artery Disease Participants in UK Biobank |
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