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Eur. Heart J., Nov 29 2024, ehae790 | https://doi.org/10.1093/eurheartj/ehae790
Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study
Seunghoon Cho 1†, Sujeong Eom 2,3†, Daehoon Kim 1, Tae-Hoon Kim 1, Jae-Sun Uhm 1, Hui-Nam Pak 1, Moon-Hyoung Lee 1, Pil-Sung Yang 4, Eunjung Lee 5, Zachi Itzhak Attia 5, Paul Andrew Friedman 5, Seng Chan You 2,3,*, Hee Tae Yu 1,*, and Boyoung Joung 1,*
1Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea;
2Department of Biomedical Systems Informatics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea;
3Institute for Innovation in Digital Healthcare, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea;
4Department of Cardiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea; and
5Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
*Corresponding authors: S.C.Y.; H.T.Y.; B.J.
†The first two authors contributed equally to the study.
1Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea;
2Department of Biomedical Systems Informatics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea;
3Institute for Innovation in Digital Healthcare, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea;
4Department of Cardiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea; and
5Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
*Corresponding authors: S.C.Y.; H.T.Y.; B.J.
†The first two authors contributed equally to the study.
Abstract
Background and aims: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk.
Methods: An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap < 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed.
Results: The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), -.1 (6.0), 4.7 (8.7), and -1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24-2.78), 1.89 (1.46-2.43), 1.90 (1.55-2.33), and 1.76 (1.67-1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47-3.37), 1.94 (1.39-2.70), 1.58 (1.06-2.35), and 1.79 (1.62-1.97) in these cohorts compared with the normal group.
Conclusions: The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.
Methods: An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap < 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed.
Results: The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), -.1 (6.0), 4.7 (8.7), and -1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24-2.78), 1.89 (1.46-2.43), 1.90 (1.55-2.33), and 1.76 (1.67-1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47-3.37), 1.94 (1.39-2.70), 1.58 (1.06-2.35), and 1.79 (1.62-1.97) in these cohorts compared with the normal group.
Conclusions: The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.
논문정보
- Research article
- 2024년 11월 (BRIC 등록일 2024-12-04)
- 국내 연구진
- 의약학 > 응용의학
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