Valvular & structural heart diseaseDiagnostic or prognostic study
Artificial intelligence-enhanced electrocardiography to predict regurgitant valvular heart diseases: an international study
- Prognostic model development and external validation study
- international
Authors' conclusion
This study developed AI-ECG models to diagnose and predict rVHDs and validated the models in a transnational and ethnically distinct cohort.
Study context
- Population
- Patients with paired ECG and transthoracic echocardiogram, Zhongshan Hospital, Shanghai, China; external outpatient set, Beth Israel Deaconess Medical Center, Boston, US988,618 ECG–echo pairs
- Participants
- 400,882 patients
- Comparison
- AI-ECG risk quartiles
- Outcome
- Future moderate or severe regurgitant valvular heart disease (VHD) (MR, TR, AR)
Performance
As reported in the abstract
Prediction of future significant rVHD (internal test set)
Highest risk quartile vs Lowest risk quartileHazard ratio (95% CI)P value
MR; age- and sex-adjusted Cox
7.6(5.8–9.9)
P < 0.0001AR
3.8(2.7–5.5)
TR
9.9(7.5–13.0)
- C-index, mitral regurgitation (MR)
- 0.774 (95% CI 0.753-0.792)
- C-index, aortic regurgitation (AR)
- 0.691 (95% CI 0.657-0.720)
- C-index, tricuspid regurgitation (TR)
- 0.793 (95% CI 0.777-0.808)
Further findings
External test set (34 214 outpatients, Boston)
- Findings confirmed in the transnational external test set
Imaging association
- AI-ECG predictions associated with subclinical chamber remodelling
Original publication
Artificial intelligence-enhanced electrocardiography to predict regurgitant valvular heart diseases: an international study. Eur Heart J. Published 16 July 2025. doi:10.1093/eurheartj/ehaf448
Summarised from the published abstract; every number is checked against it