EAGLE (AI-ECG)

Artificial Intelligence-Enabled Electrocardiograms for Identification of Patients with Low Ejection Fraction: a Pragmatic, Randomized Clinical Trial

Paciente / Población Intervención / Exposición Comparación Desenlace

In 22,641 adults without prior heart failure having routine ECGs at 120 primary care teams in 45 clinics or hospitals, clinician access to an AI-ECG low-ejection-fraction prediction increased new diagnoses of low ejection fraction within 90 days compared with usual care without the AI result.

N
22.641
Diseño
Pragmatic cluster RCT, USA (120 primary care teams)
Desenlace
New diagnosis of low EF (≤50%) within 90 days of the ECG
Relevancia
1
ResultadoLow-EF diagnosis 2.1% vs 1.6% (OR 1.32, 1.01–1.61; P=0.007); among AI-ECG-positive patients (6% of the cohort) 19.5% vs 14.5% (OR 1.43, 1.08–1.91; P=0.01); echocardiogram use similar overall (19.2% vs 18.2%, P=0.17).
Yao X, et al. Nat Med. 2021;27(5):815-819. 10.1038/s41591-021-01335-4
Discusión y crítica

First randomised evidence that an AI-ECG decision-support tool changes diagnosis in routine primary care, and the template for the later AI-ECG case-finding trials. The absolute gain was small, the endpoint is a diagnosis at 90 days rather than a clinical outcome, and the trial was open-label by nature. Its gains came only when clinicians acted on the alert — clinician adoption was the limiting step (see the Rushlow entry), and TRICORDER later showed the same mechanism with an AI stethoscope.