Valvular & structural heart disease

Generalisable artificial intelligence ECG trained on public data for outcome prediction after transcatheter aortic valve replacement

HeartPublished Development and external validation of a transformer-based Artificial intelligence ECG (AI-ECG) model

Authors' conclusion

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.

Study context

Population
Patients undergoing transcatheter aortic valve replacement (TAVR)prospective TAVR validation cohort, model trained on 341,151 publicly available ECGs with mortality labels
Participants
439 patients
Predictors
Transformer-based AI-ECG model applied to a single pre-procedural ECG
Predicted outcome
30-day and 1-year mortality
Methodological detailstrained exclusively on publicly available data, prospective external validation cohort, additionally tested in a surgical aortic valve replacement cohort
  • Trained exclusively on publicly available data
  • Prospective external validation cohort
  • Additionally tested in a surgical aortic valve replacement cohort

Model performance

As reported in the abstract

AUC
30-day mortality
0.85(95% CI 0.78 to 0.92)
1-year mortality
0.74(95% CI 0.67 to 0.80)
  • TAVR cohort, single pre-procedural ECG

Further findings

Predictive performance in independent surgical cohort

Predictive performance was consistent in an independent surgical cohort

1-year mortality, major adverse cardiac events and major renal events (AI-ECG risk score association)

AI-ECG risk score, adjusted OR for 1-year mortality
1.70
Independently associated with major adverse cardiac events and major renal events
  • Multivariable regression

High-risk ECG features

Atrial fibrillation, prolonged QT interval, ST-T abnormalities and low voltage

Original publication

Generalisable artificial intelligence ECG trained on public data for outcome prediction after transcatheter aortic valve replacement. Heart. Published 28 May 2026. doi:10.1136/heartjnl-2025-327462

Composed by AI from the published abstract and checked against it. How Briefs are made →

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