Deep learning prediction of left atrial structure and function from 12-lead electrocardiograms

Abstract

Abstract Abnormal cardiac atrial structure and function, termed atrial cardiopathy, typically precedes atrial fibrillation and downstream cardiovascular complications, yet detection is limited by the cost and accessibility of high-quality cardiac imaging. Here we show that a deep learning model trained on 12-lead electrocardiograms paired with 21,749 cardiac magnetic resonance scans from the UK Biobank predicts left atrial structure and function. Model-derived measures of atrial cardiopathy are strongly associated with new-onset atrial fibrillation, heart failure, and ischemic stroke after adjustment for clinical risk factors and biomarkers in two external cohorts, with magnitudes comparable to or greater than those for direct imaging measures and clinical risk factors. The risk of cardioembolic stroke, the hallmark complication of atrial fibrillation, increases 66% per standard deviation of left atrial volume. In exploratory analyses, model measures predict cardiac monitor-detected atrial fibrillation more accurately than a clinical risk prediction tool and NT-proBNP levels. This model is an inexpensive, accessible tool that identifies individuals at high-risk for atrial fibrillation and related complications.