AI-Enabled ECG Screening Increases Early Diagnosis of Low Ejection Fraction in Primary Care
In a pragmatic cluster-randomized trial involving 120 primary care teams and 22,641 patients, an AI-powered ECG clinical decision support tool significantly increased the diagnosis of low ejection fraction within 90 days compared to usual care (2.1% vs 1.6%, OR 1.32). Among patients flagged as high-likelihood by the AI algorithm, echocardiogram utilization rose substantially in the intervention arm. This landmark trial provides the first randomized evidence that AI-ECG screening can enable earlier detection of a treatable but underdiagnosed cardiac condition in routine practice.
The original study
Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.
- Authors
- Yao X, Rushlow DR, Inselman JW, McCoy RG, Thacher TD, Behnken EM, et al.
- Journal
- Nature medicine
- Type
- Journal Article, Multicenter Study, Randomized Controlled Trial, Pragmatic Clinical Trial
- PMID
- 33958795
Original abstract
We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01-1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08-1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.