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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 of 0.776, sensitivity of 0.794, and specificity of 0.752. Conclusion: Artificial intelligence-enhanced ECGs may offer better predictive value than traditionally defined high-risk ECG patterns.
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INTRODUCTION: Decisions regarding the optimal timing of intervention for asymptomatic aortic stenosis (AS) are controversial. The study aims to identify potential risk factors for asymptomatic patients with severe AS that are associated with worse prognosis and to evaluate the benefits of early interventions for asymptomatic patients presenting with one or more additional risk factors. METHODS AND ANALYSIS: This is a non-interventional, prospective, open-label, multicentre registry study across China. A total of 1000 patients will be enrolled and categorised as symptomatic or asymptomatic. The primary endpoint is the occurrence of all-cause mortality, stroke, acute myocardial infarction and heart failure-related hospitalisation at 1-year follow-up. In asymptomatic severe AS patients presenting with one or more risk factors, the occurrence rate of the primary endpoint between those who undergo transcatheter aortic valve replacement (TAVR) and those who do not will be compared. We will also compare the occurrence rate of the primary endpoint for asymptomatic severe AS patients with additional risk factors who undergo TAVR with those presenting with symptoms. This study is believed to provide additional evidence to help clinicians identify and refer severe AS patients who are asymptomatic but present with additional risk factors for early intervention of TAVR. ETHICS AND DISSEMINATION: The study protocol has been approved by the local ethics committee of each participating site: West China Hospital, Sichuan University; Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Second Hospital of Hebei Medical University; Tianjin Chest Hospital; and First Affiliated Hospital of Nanchang University. All participants will provide written informed consent. Study results will be published through academic conferences and peer-reviewed journals. TRIAL REGISTRATION: This study was registered at the Chinese Clinical Trial Registry (https:// www.chictr.org.cn), with the registration number ChiCTR2200064853.