Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation

• Published in: BMC medical informatics and decision making Vol. 24; no. 1; pp. 225 - 10
• Main Authors: Qiu, Yue, Guo, Hongcheng, Wang, Shixin, Yang, Shu, Peng, Xiafeng, Xiayao, Dongqin, Chen, Renjie, Yang, Jian, Liu, Jiaheng, Li, Mingfang, Li, Zhoujun, Chen, Hongwu, Chen, Minglong
• Format: Journal Article
• Language: English
• Published: London BioMed Central 08.08.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Ablation; Ablation (Surgery); Accuracy; Aged; Algorithms; Atrial fibrillation; Atrial Fibrillation - surgery; Atrial fibrillation recurrence; Cardiac arrhythmia; Cardiovascular disease; Care and treatment; Catheter Ablation; Catheters; Clinical features; Computational linguistics; Deep Learning; Diseases; EKG; Electrocardiogram; Electrocardiography; Female; Fibrillation; Health Informatics; Humans; Information Systems and Communication Service; Language processing; Machine learning; Male; Management of Computing and Information Systems; Medical instruments; Medicine; Medicine & Public Health; Methods; Middle Aged; Natural language interfaces; Patients; Performance evaluation; Performance prediction; Prognosis; Pulmonary vein isolation; Radio frequency; Recurrence; Relapse; Risk factors; Sensitivity analysis; Survival analysis; Transformer; Transformers; Variables; China; Deep learning; Clinical features; Transformer; Atrial fibrillation recurrence; Electrocardiogram; Pulmonary vein isolation
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02616-x

Background Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence after ablation. This study aims to propose a novel algorithm based on Transformer using surface electrocardiogram (ECG) signals and clinical features can predict AF recurrence. Methods Between October 2018 to December 2021, patients who underwent index radiofrequency ablation for AF with at least one standard 10-second surface ECG during sinus rhythm were enrolled. An end-to-end deep learning framework based on Transformer and a fusion module was used to predict AF recurrence using ECG and clinical features. Model performance was evaluated using areas under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy and F1-score. Results A total of 920 patients (median age 61 [IQR 14] years, 66.3% male) were included. After a median follow-up of 24 months, 253 patients (27.5%) experienced AF recurrence. A single deep learning enabled ECG signals identified AF recurrence with an AUROC of 0.769, sensitivity of 75.5%, specificity of 61.1%, F1 score of 55.6% and overall accuracy of 65.2%. Combining ECG signals and clinical features increased the AUROC to 0.899, sensitivity to 81.1%, specificity to 81.7%, F1 score to 71.7%, and overall accuracy to 81.5%. Conclusions The Transformer algorithm demonstrated excellent performance in predicting AF recurrence. Integrating ECG and clinical features enhanced the models’ performance and may help identify patients at low risk for AF recurrence after index ablation.