Abstract 12922: Electrocardiogram-Based Deep Learning and Clinical Risk Factors to Predict Incident Atrial Fibrillation

Abstract

Introduction: Deep learning-derived representations of 12-lead electrocardiograms (ECGs) may allow for atrial fibrillation (AF) risk prediction. However, it remains unclear whether ECG-based artificial intelligence improves prediction beyond established clinical risk factors for AF and whether predictions are generalizable. Methods: Within a dataset comprising over 500,000 individuals receiving regular primary care at a multi-institutional network, we trained a convolutional neural network to predict incident AF using 12-lead ECGs (“ECG-AI”). ECG-AI was trained in individuals with ≥1 ECG performed at Massachusetts General Hospital (MGH) within 3 years prior to start of follow-up. We then fit a Cox proportional hazards model with incident AF as the outcome and a) logit-transformed ECG-AI AF probability, and b) the Cohorts for Aging and Genomic Epidemiology AF (CHARGE-AF) score, as covariates (“CH-AI”). We compared the discrimination and calibration of CHARGE-AF versus CH-AI in three independent samples: MGH (n=4,166), Brigham and Women’s Hospital (BWH, n=37,963) and the UK Biobank (n=41,034). Based on available follow-up, AF was evaluated at 5 years in MGH and BWH, and 2 years in the UK Biobank. Results: ECG-AI was trained in 36,081 individuals with an ECG performed at MGH (mean age 55±17, 53% female). CH-AI had substantially better discrimination (area under the receiver operating characteristic curve [AUROC]: MGH 0.838, BWH 0.777, UK Biobank 0.746; average precision [AP] 0.30, 0.21, 0.06) versus CHARGE-AF (AUROC: 0.802, 0.752, 0.732; AP 0.21, 0.17, 0.02, Figure ). CH-AI was well-calibrated in MGH (calibration error 0.012) and BWH (0.019), but overestimated AF risk in the UK Biobank (0.068). Calibration in the UK Biobank was excellent after recalibration to the sample-level 2-year AF hazard (error 7.1x10 -5 ). Conclusions: A model combining clinical AF risk factors with deep learning-derived ECG-based AF risk is favorable for predicting 5-year risk of AF.