BEAGLE

Artificial Intelligence-Guided Screening for Atrial Fibrillation Using Electrocardiogram During Sinus Rhythm: a Prospective Non-Randomised Interventional Trial

Patient / Population Intervention / Exposition Comparaison Critère de jugement

In 1,003 patients with stroke risk factors and no known atrial fibrillation who had a routine ECG, from 40 US states, AI-ECG risk stratification followed by up to 30 days of continuous ambulatory rhythm monitoring increased detection of new atrial fibrillation compared with propensity-matched usual care.

N
1 003
Schéma
Prospective, non-randomised interventional trial with propensity-matched real-world controls, USA
Critère
Newly diagnosed atrial fibrillation
Pertinence
3
RésultatAF detected in 7.6% (48/633) of AI-high-risk vs 1.6% (6/370) of low-risk patients (OR 4.98, 95% CI 2.11–11.75; P=0.0002); vs propensity-matched usual care, detection 10.6% vs 3.6% in the high-risk group (P<0.0001) and 2.4% vs 0.9% in the low-risk group (P=0.12) over a median 9.9 months.
Noseworthy PA, et al. Lancet. 2022;400(10359):1206-1212. 10.1016/S0140-6736(22)01637-3
Discussion et critique

Showed that an AI read of a sinus-rhythm ECG can focus AF screening on the patients most likely to have it, a rationale later applied within VITAL-AF. Non-randomised: the usual-care comparison was built by propensity matching from eligible but unenrolled patients, and the endpoint is AF detection rather than stroke prevention, so whether AF found this way benefits from anticoagulation is untested.