Generalisable artificial intelligence ECG trained on public data for outcome prediction after transcatheter aortic valve replacement.
Journal
Heart (British Cardiac Society)
ISSN
1468-201X
Date Issued
2026-07-20
Author(s)
Hsiao, Yu-Chung
Hung, Kuo-Hsuan
Yeh, Chih-Fan
Chen, Ying-Hsien
Ko, Tsung-Yu
Lai, Chun-Fu
Lin, Li-Chun
Chang, Wei-Tien
Tsao, Yu
Lin, Mao-Shin
Wu, Chau-Chung
Abstract
Background: Artificial intelligence ECG (AI-ECG) models can predict cardiovascular outcomes, but their clinical adoption is limited by restricted access to training data and uncertain generalisability. We developed and externally validated a generalisable AI-ECG model trained exclusively on publicly available data to predict outcomes after transcatheter aortic valve replacement (TAVR). Methods: A transformer-based AI-ECG model was trained on 341 151 publicly available ECGs with mortality labels. The model was externally validated in a prospective cohort of 439 patients undergoing TAVR and additionally tested in a surgical aortic valve replacement cohort. Model performance for predicting 30-day and 1-year mortality was assessed using area under the receiver operating characteristic curve (AUC) and associations with clinical outcomes were evaluated using multivariable regression. Results: In the TAVR cohort, a single pre-procedural ECG predicted 30-day mortality with an AUC of 0.85 (95% CI 0.78 to 0.92) and 1-year mortality with an AUC of 0.74 (95% CI 0.67 to 0.80). The derived AI-ECG risk score was independently associated with 1-year mortality (adjusted OR 1.70), major adverse cardiac events and major renal events. Predictive performance was consistent in an independent surgical cohort. High-risk ECG features included atrial fibrillation, prolonged QT interval, ST-T abnormalities and low voltage. Conclusions: An AI-ECG model trained solely on publicly available data provides accurate and generalisable prediction of outcomes after TAVR using a single ECG. This approach demonstrates the feasibility of transparent, shareable AI models for cardiovascular risk stratification and supports a general-to-specific paradigm for clinical deployment. © Author(s) (or their employer(s)) 2026. No commercial re-use. See rights and permissions. Published by BMJ Group.
Subjects
Risk Factors
Transcatheter Aortic Valve Replacement
Type
journal article