Electrocardiogram-based prediction of conduction disturbances after transcatheter aortic valve replacement with convolutional neural network

作者：Yuheng Jia, Yiming Li, Gadeng Luosang, Jianyong Wang, Gang Peng, Xingzhou Pu, Weili Jiang, Wenjian Li, Zhen‐Gang Zhao, Yong Peng, Yuan Feng, Jiafu Wei, Yuanning Xu, Xingbin Liu, Yi Zhang, Mao Chen · 发表于：European Heart Journal - Digital Health · 年份：2024 · DOI：10.1093/ehjdh/ztae007 · 被引用次数：8 · 研究领域：Cardiac Valve Diseases and Treatments、Atrial Fibrillation Management and Outcomes、Congenital Heart Disease Studies

Aims: Permanent pacemaker implantation and left bundle branch block are common complications after transcatheter aortic valve replacement (TAVR) and are associated with impaired prognosis. This study aimed to develop an artificial intelligence (AI) model for predicting conduction disturbances after TAVR using pre-procedural 12-lead electrocardiogram (ECG) images. Methods and results: We collected pre-procedural 12-lead ECGs of patients who underwent TAVR at West China Hospital between March 2016 and March 2022. A hold-out testing set comprising 20% of the sample was randomly selected. We developed an AI model using a convolutional neural network, trained it using five-fold cross-validation and tested it on the hold-out testing cohort. We also developed and validated an enhanced model that included additional clinical features. After applying exclusion criteria, we included 1354 ECGs of 718 patients in the study. The AI model predicted conduction disturbances in the hold-out testing cohort with an area under the curve (AUC) of 0.764, accuracy of 0.743, F1 score of 0.752, sensitivity of 0.876, and specificity of 0.624, based solely on pre-procedural ECG images. The performance was better than the Emory score (AUC = 0.704), as well as the logistic (AUC = 0.574) and XGBoost (AUC = 0.520) models built with previously identified high-risk ECG patterns. After adding clinical features, there was an increase in the overall performance with an AUC of 0.779, accuracy of 0.774, F1 score ...