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.
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Data availability
The data are not publicly available because they are electronic health records, consented for research use by Mayo Clinic investigators. Making the data publicly available without additional consent or ethical approval might compromise patients’ privacy and the original ethical approval. If other investigators are interested in performing additional analyses, requests can be made to the corresponding author (X.Y.) and analyses will be performed in collaboration with the Mayo Clinic.
Code availability
The AI algorithm, which was previously published, cannot be made publicly available because it is proprietary intellectual property (patent pending). The AI algorithm cannot be used in routine practice before getting FDA approval, and this algorithm is currently undergoing a submission/review process with the FDA. The AI algorithm is available upon request for research studies.
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Mayo Clinic has licensed the AI-ECG algorithm to EKO, a maker of digital stethoscopes with embedded ECG electrodes. At no point will Mayo Clinic benefit financially from the use of the AI-ECG for the care of patients at the Mayo Clinic. P.A.F., F.L.-J., S.K. and Z.I.A. may receive financial benefit from this agreement for use of the AI-ECG outside of the Mayo Clinic. All other authors declare no competing interests.
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Peer review information Nature Medicine thanks Jill Waalen, Giorgio Quer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Extended data
Extended Data Fig. 1 Subgroup analyses for echocardiogram performed.
Outcome was echocardiogram being performed. An odds ratio greater than 1 means a higher likelihood of echocardiogram being performed. Error bars indicate 95% confidence intervals; mixed effect logistic regressions were used for the statistical test; it was two-sided; no adjustment for multiple comparison was made.
Extended Data Fig. 2 Subgroup analyses for echocardiogram performed among patients with positive screening results.
Outcome was echocardiogram being performed. An odds ratio greater than 1 means a higher likelihood of echocardiogram being performed. Error bars indicate 95% confidence intervals; mixed effect logistic regressions were used for the statistical test; it was two-sided; no adjustment for multiple comparison was made.
Extended Data Fig. 3
Clinician-facing action recommendation report for screening results.
Extended Data Fig. 4
Internal resources website for clinicians.
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Yao, X., Rushlow, D.R., Inselman, J.W. et al. Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med 27, 815–819 (2021). https://doi.org/10.1038/s41591-021-01335-4
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DOI: https://doi.org/10.1038/s41591-021-01335-4
