Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study.

IF 37.6 1区医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS European Heart Journal Pub Date : 2025-03-03 DOI:10.1093/eurheartj/ehae790

Seunghoon Cho, Sujeong Eom, Daehoon Kim, Tae-Hoon Kim, Jae-Sun Uhm, Hui-Nam Pak, Moon-Hyoung Lee, Pil-Sung Yang, Eunjung Lee, Zachi Itzhak Attia, Paul Andrew Friedman, Seng Chan You, Hee Tae Yu, Boyoung Joung

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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.

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人工智能衍生的心电图老化和房颤风险：一项多国研究。

背景与目的：人工智能（AI）算法在12导联心电图（ECG）中的应用为年龄预测提供了很有前景的方法。本研究调查了心电图预测年龄（AI-ECG年龄）与实足年龄之间的差异，即心电图老化（ECG老化）是否与房颤（AF）风险相关。方法：使用大规模数据集（来自689 639名参与者的1 533 042张心电图）建立AI-ECG年龄预测模型，并使用6个独立的跨国数据集（来自330 794名参与者的737 133张心电图）进行验证。计算了两个韩国队列的AI-ECG年龄差距[平均（标准差）随访：111 483名参与者4.1（4.3）年，37 517名参与者6.1（3.8）年]，一个英国队列[3.0（1.6）年；40973名参与者]和一个美国队列[12.9（8.6）岁；90639人]。参与者分为正常组（年龄差距< 7岁）和心电图组（年龄差距≥7岁）。评估心电图老化对新发和早发房颤风险的预测能力。结果：患者AI-ECG平均年龄分别为51.9（16.2）、47.4（12.5）、68.4（7.8）、56.7（14.6）岁，年龄差距分别为0（6.8）、- 0.1在韩国、英国和美国的两个队列中分别为（6.0）、4.7（8.7）和-1.4（8.9）年。在心电图年龄组中，观察到新发房颤的风险增加，在韩国、英国和美国两个队列中，风险比（95%置信区间）分别为2.50（2.24-2.78）、1.89（1.46-2.43）、1.90（1.55-2.33）和1.76（1.67-1.86）。与正常组相比，早发性房颤的比值比分别为2.89（2.47-3.37）、1.94（1.39-2.70）、1.58（1.06-2.35）和1.79（1.62-1.97）。结论：人工智能得出的心电图老化与新发和早发房颤的风险相关，表明其在不同人群中识别房颤预防个体的潜在效用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Heart Journal 医学-心血管系统

CiteScore

39.30

自引率

6.90%

发文量

3942

审稿时长

1 months

期刊介绍： The European Heart Journal is a renowned international journal that focuses on cardiovascular medicine. It is published weekly and is the official journal of the European Society of Cardiology. This peer-reviewed journal is committed to publishing high-quality clinical and scientific material pertaining to all aspects of cardiovascular medicine. It covers a diverse range of topics including research findings, technical evaluations, and reviews. Moreover, the journal serves as a platform for the exchange of information and discussions on various aspects of cardiovascular medicine, including educational matters. In addition to original papers on cardiovascular medicine and surgery, the European Heart Journal also presents reviews, clinical perspectives, ESC Guidelines, and editorial articles that highlight recent advancements in cardiology. Additionally, the journal actively encourages readers to share their thoughts and opinions through correspondence.