Abstract 13572: Electrocardiographic Aging Derived From Artificial Intelligence is Associated With the Risk of Early- and New-Onset Atrial Fibrillation: A Multi-National Cohort Study

Abstract only Background: The application of artificial intelligence (AI) algorithms to ECG provides promising age prediction methods. We investigated whether the age discrepancy between AI-predicted age from ECG (AI-ECG age) and chronological age, known as the AI-ECG age gap or electrocardiographic...

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Published in:Circulation (New York, N.Y.) N.Y.), 2023-11, Vol.148 (Suppl_1)
Main Authors: Cho, Seunghoon, Eom, Sujeong, You, Seng Chan, Yu, Hee Tae, JOUNG, BOYOUNG
Format: Article
Language:English
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Summary:Abstract only Background: The application of artificial intelligence (AI) algorithms to ECG provides promising age prediction methods. We investigated whether the age discrepancy between AI-predicted age from ECG (AI-ECG age) and chronological age, known as the AI-ECG age gap or electrocardiographic aging (ECG-aging), could predict atrial fibrillation (AF) risk. Methods: We developed an AI-ECG age prediction model using a single-center dataset (1,533,042 ECGs from 689,639 participants) and validated it using five independent, multi-national datasets (637,177 ECGs from 230,838 participants). The AI-ECG age gap was calculated in two cohorts from South Korea and the UK. Based on this age gap, participants were classified into two study groups: Normal ECG-aging (Normal EA, age gap
ISSN:0009-7322
1524-4539
DOI:10.1161/circ.148.suppl_1.13572