Heart failure & cardiomyopathiesCohort study
Predicting Heart Failure From 12-Lead ECGs Using AI
- Pooled cohort study
- pooled data from 3 cohorts (Framingham Heart Study, MESA, Cardiovascular Health Study)
Authors' conclusion
The addition of ECG-AI to PREVENT-HF improved discrimination of near-term HF risk.
Study context
- Population
- Participants of the Framingham Heart Study, Multi-Ethnic Study of Atherosclerosis and Cardiovascular Health Study with data for both ECG-AI and PREVENT-HF
- Participants
- 14,126 participants
- Exposure
- ECG-AI added to PREVENT-HF vs PREVENT-HF alone
- Outcome
- Incident HF prediction (Harrell's C-statistic and net reclassification improvement)
- Follow-up
- 1, 3, 5 and 10 years
Main finding
As reported in the abstract
Net reclassification improvement, ECG-AI added to PREVENT-HF
1-directional NRI at PREVENT-HF threshold 10%
0.086 to 0.125
1-directional NRI at PREVENT-HF threshold 20%
0.327 to 0.403
Positive composite ECG-AI screen: higher risk of developing heart failure (HF) vs negative screen
10- to 20-fold
Observational data: associations, not effects.
Further findings
| Outcome | ECG-AI LEF | ECG-AI DD | Composite ECG-AI | PREVENT-HF |
|---|---|---|---|---|
| Positive screening rates | 2.9% | 11.1% | 11.9% | |
| Score ≥10% | Not reported in the abstract | Not reported in the abstract | Not reported in the abstract | 25.1% |
| Score ≥20% | Not reported in the abstract | Not reported in the abstract | Not reported in the abstract | 5.8% |
Incident HF and death
- Incident HF
- 7.7%
- Death
- 15.1%
Original publication
Predicting Heart Failure From 12-Lead ECGs Using AI. J Am Coll Cardiol. Published 8 November 2025. doi:10.1016/j.jacc.2025.10.065
Summarised from the published abstract; every number is checked against it