Artificial Intelligence-Enabled ECGs for Atrial Fibrillation Identification and Enhanced Oral Anticoagulant Adoption: A Pragmatic Randomized Clinical Trial.

BACKGROUND

Atrial fibrillation (AF) is often underdiagnosed and undertreated by noncardiologists. This study evaluated whether artificial intelligence-enabled ECG (AI-ECG) alerts could improve AF diagnosis and non-vitamin K antagonist oral anticoagulant prescriptions by noncardiologists.

METHODS

In this open-label, cluster randomized controlled trial (NCT05127460) at 2 hospitals in Taiwan, noncardiologists were randomized to an intervention group (AI-ECG alerts) or control group (usual care). Alerts were sent to physicians when AI-ECG identified AF in emergency or hospitalized patients at risk of stroke (CHA₂DS₂-VASc ≥1 for men, ≥2 for women), excluding those with prior AF or oral anticoagulant use. Primary end points included a non-vitamin K antagonist oral anticoagulant prescription within 90 days after discharge, new AF diagnosis, echocardiogram arrangements, and cardiologist visits. Secondary end points were ischemic stroke, cardiovascular death, and all-cause death.

RESULTS

A total of 8857 and 8960 patients were treated by 120 and 113 noncardiologists in the intervention and control groups, respectively; 275 and 245 patients had AI-detected AF. The non-vitamin K antagonist oral anticoagulant prescription rate was significantly higher in the intervention group (23.3% versus 12.0%; hazard ratio [HR], 1.85 [95% CI, 1.11-3.07]). The intervention group also had a higher rate of AF diagnosis (HR, 1.40 [95% CI, 1.03-1.90]). No significant differences were observed in echocardiogram arrangements, cardiologist visits, or the rates of ischemic stroke, cardiovascular death, and all-cause death.

CONCLUSIONS

An AI-ECG alert for AF identification promoted non-vitamin K antagonist oral anticoagulant prescriptions among noncardiologists, thus reducing the disparity in AF care quality between cardiologists and noncardiologists.

REGISTRATION: URL: https://clinicaltrials.gov/; Unique identifier: NCT05127460.