Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation

Hanjin Park, Oh Seok Kwon, Jaemin Shim, Daehoon Kim, Je Wook Park, Yun Gi Kim, Hee Tae Yu, Tae Hoon Kim, Jae Sun Uhm, Jong Il Choi, Boyoung Joung, Moon Hyoung Lee, Hui Nam Pak

Research output: Contribution to journalArticlepeer-review

Abstract

The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.

Original languageEnglish
Article number234
Journalnpj Digital Medicine
Volume7
Issue number1
DOIs
Publication statusPublished - 2024 Dec

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Health Informatics
  • Computer Science Applications
  • Health Information Management

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