Deep Learning to Predict Cardiac Magnetic Resonance–Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs. (16th June 2021)

• Record Type: Journal Article
• Authors: Khurshid, Shaan; Friedman, Samuel; Pirruccello, James P.; Di Achille, Paolo; Diamant, Nathaniel; Anderson, Christopher D.; Ellinor, Patrick T.; Batra, Puneet; Ho, Jennifer E.; Philippakis, Anthony A.; Lubitz, Steven A.
• Is Part Of: Circulation. Volume 14:Number 6(2021)
• Page Start: e012281
• Publication Date: 2021-06-16
• Subjects: atrial fibrillation -- heart failure -- left ventricular hypertrophy -- machine learning -- myocardial infarction; Cardiovascular system -- Imaging -- Periodicals; Heart -- Imaging -- Periodicals; 616.1075405
• DOI: 10.1161/CIRCIMAGING.120.012281
• Languages: English
• ISSNs: 1941-9651

Abstract

Abstract : Supplemental Digital Content is available in the text. 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.