Background and Aims
Valvular heart disease (VHD) is a significant source of morbidity and mortality, though early intervention can improve outcomes. This study aims to develop artificial intelligence-enhanced electrocardiogram (AI-ECG) models to diagnose and predict future moderate or severe regurgitant VHDs (rVHDs), including mitral regurgitation (MR), tricuspid regurgitation (TR), and aortic regurgitation (AR).

Methods:
The AI-ECG models were developed in a dataset of 988,618 ECG and transthoracic echocardiogram pairs from 400,882 patients from Zhongshan Hospital, Shanghai, China. The AI-ECG models used a residual convolutional neural network with a discrete-time survival loss function. External evaluation was performed in outpatients from a secondary care dataset from Beth Israel Deaconess Medical Center, Boston, USA, consisting of 34,214 patients with linked echocardiography.

Results:
In the internal test set, the AI-ECG models accurately predicted future significant MR (C-index 0.774, 95%CI 0.753-0.792), AR (0.691, 95%CI 0.657-0.720) and TR (0.793, 95%CI 0.777-0.808). In age- and sex-adjusted Cox models, the highest risk quartile had a hazard ratio (HR) of 7.6 (95%CI 5.8-9.9, P < 0.0001) for risk of future significant MR, compared to the lowest risk quartile. For future AR and TR, the equivalent HRs were 3.8 (95%CI 2.7-5.5) and 9.9 (95%CI 7.5-13.0), respectively. These findings were confirmed in the transnational external test set. Imaging association analyses demonstrated AI-ECG predictions were associated with subclinical chamber remodeling.

Conclusions: This study developed AI-ECG models to diagnose and predict future rVHDs and validated the models in a transnational and ethnically distinct cohort. AI-ECG could be utilized to guide surveillance echocardiography in patients at risk of future rVHDs, to facilitate early detection and intervention.

REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT06475157.