Predicting atrial fibrillation ablation success: a novel approach using pre-procedure sinus rhythm ecg and deep learning

• Published in: Europace (London, England) Vol. 26; no. Supplement_1
• Main Authors: Mohsen, Y, Vatsaraj, I, Loeffler, S, Horlitz, M, Stoeckigt, F, Trayanova, N
• Format: Journal Article
• Language: English
• Published: 24.05.2024
• ISSN: 1099-5129; 1532-2092
• DOI: 10.1093/europace/euae102.021

Abstract Introduction Pulmonary vein isolation (PVI) has been the corner stone of the treatment of patients with atrial fibrillation (AF), yet its efficacy varies notably between patient groups. Specifically, it shows reduced efficacy in individuals with persistent AF and those with atrial fibrosis. Recent advancements suggest that additional substrate modification could enhance outcomes in specific patient groups. Therefore, predicting the outcomes of lone PVI could help in stratifying patients who would benefit most from this additional substrate modification. Addressing this gap, our study explores the prognostication of PVI outcomes using pre-procedural 12-lead ECG recordings taken in Sinus rhythm, employing advanced deep learning techniques. Methods This retrospective study analyzed 209 patients with paroxysmal and persistent AF who underwent PVI. These patients were followed up for 12 months after the blanking period. Pre-procedural sinus rhythm 12-lead ECGs were processed using an innovative deep learning architecture, integrating Long Short-Term Memory and Convolutional Neural Networks with a cross-attention module. This multimodal deep learning model was trained using ECG and clinical covariates like age, sex, and BMI to predict binary outcomes – AF type (Paroxysmal or Persistent) and AF recurrence. The model's efficacy was evaluated through a 5-fold stratified cross-validation to ensure accuracy and reliability. Model was also tested using only ECG data. Results The multimodal deep learning model performed significantly better as compared to the model trained using ECG data only. The multimodal deep learning model achieved an accuracy of 75.56% and a sensitivity of 82% in prognosticating ablation outcomes. Additionally, the model demonstrated a significant capability in classifying AF into paroxysmal or persistent types with an accuracy of 86.67% and a recall of 89%. Conclusion Our deep learning approach in analyzing sinus rhythm ECG recordings shows promising results in predicting PVI outcomes and identifying AF types. This approach could offer a more accessible and cost-effective tool to stratify AF patients undergoing PVI, identifying those less likely to benefit from standard procedures. Such insights can assist physicians and patients in determining ablation candidacy and selecting the most appropriate ablation strategy and technique.Proposed StrategyDeep learning model approach