Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation.
NPJ Digit Med
; 7(1): 234, 2024 Sep 05.
Article
in En
| MEDLINE
| ID: mdl-39237703
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
NPJ Digit Med
Year:
2024
Document type:
Article
Country of publication:
United kingdom