Arrhythmias & electrophysiology
Multicenter validation of an artificial intelligence–enabled electrocardiogram (ECG) model to predict 1-year risk of atrial fibrillation or flutter
Authors' conclusion
The results validate the performance of the Tempus ECG-AF model and support its clinical use for AF risk stratification.
Study context
- Population
- Patients aged 65+ years with no previous atrial fibrillation (AF) or history of pacer/defibrillator use240 (6.0%) developing AF within 1 year
- Participants
- 4,017 patients
- Exposure
- Sensitivity and specificity of a risk score from an ECG-AI model evaluated against prespecified minimum values of 20% and 85%, respectively
- increased riskn = 391
- no increased riskn = 3,626
Methodological details3 clinical sites, external validation
- 3 clinical sites
- External validation
Main finding
As reported in the abstract
New AF diagnosis within 1 year — model performance · 1 year
Sensitivity (95% confidence interval 25-37)
31%
Specificity (95% confidence interval 91-92)
92%
Observational data: associations, not effects.
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
Multicenter validation of an artificial intelligence–enabled ECG model to predict 1-year risk of atrial fibrillation or flutter. Heart Rhythm. Published 7 April 2026. doi:10.
Summarised from the published abstract; every number is checked against it