A novel AI-ECG tool for detecting non-pulmonary vein trigger before atrial fibrillation ablation
J I Choi, C O Seo, Y S Baek, Y S Cho, J H Jeong, Y G Kim, J Shim, D H Kim, Y H KimAbstract
Background
Pulmonary vein (PV) triggers are the main sources initiating atrial fibrillation (AF), and PV isolation remains the cornerstone of rhythm control. However, non-pulmonary vein (non-PV) triggers are increasingly recognized as key factors—not only contributing to post-ablation recurrence but also guiding the selection of optimal ablation modalities, such as pulse field ablation or radiofrequency catheter ablation. Identifying patients with a higher likelihood of non-PV triggers before ablation could improve procedural strategy and rhythm outcomes.
Purpose
To evaluate whether an AI-based 12-lead ECG model can predict PV and non-PV triggers before AF ablation and to identify independent predictors of non-PV triggers.
Methods
A total of 1,546 patients who underwent AF catheter ablation with standardized trigger testing according to the University of Pennsylvania protocol were analyzed. For paroxysmal AF, 12-lead sinus rhythm ECGs obtained on the day of ablation were used, and for persistent AF, ECGs recorded within one year after direct-current cardioversion were included. AF triggers were classified as PV or non-PV according to their anatomical origin. An AI-ECG model integrating clinical and ECG waveform features (morphologic and frequency-domain characteristics) was developed using an 8:1:1 split for training, validation, and testing datasets.
Results
Among patients with non-PV triggers, the most frequent sites of origin were the superior vena cava (79.2%), left atrial posterior wall (55.3%), and interatrial septum (left atrial 24.3%, right atrial 11.2%), followed by the coronary sinus ostium (7.2%) and crista terminalis (2.4%). Among 1,546 patients (mean age 61 ± 11 years, 67% male), non-PV triggers were identified in 22% of cases. Patients with non-PV triggers were older (64 ± 10 vs. 60 ± 11 years, p < 0.01), more frequently female (41% vs. 29%, p = 0.02), and had longer QTc intervals (448 ± 25 vs. 435 ± 22 ms, p < 0.001) and higher AI-ECG scores compared with PV-trigger patients. The AI-ECG model showed good discrimination for predicting PV triggers (AUROC = 0.759) and maintained consistent performance across subgroups. In multivariable logistic regression, independent predictors of non-PV triggers included older age (OR 1.38 [1.12–1.70], p = 0.002), female sex (OR 1.42 [1.09–1.84], p = 0.009), prolonged QTc interval (OR 1.16 [1.08–1.26], p < 0.001), and a higher AI-ECG score for predicting non-PV trigger (OR 1.48 [1.23–1.79], p < 0.001).
Conclusion
A novel AI-derived 12-lead ECG model demonstrated the potential to distinguish PV from non-PV triggers prior to AF ablation. By combining AI-ECG analysis with simple clinical and ECG parameters, this approach may assist in tailoring individualized ablation strategies and is expected to contribute to improved rhythm-control outcomes in patients with AF.