ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial

Yao, Xiaoxi; McCoy, Rozalina G.; Friedman, Paul A.; Shah, Nilay D.; Barry, Barbara A.; Behnken, Emma M.; Inselman, Jonathan W.; Attia, Zachi I.; Noseworthy, Peter A.

doi:10.1016/j.ahj.2019.10.007

Cited by 98Artificial Intelligence in Healthcare and EducationECG Monitoring and AnalysisArtificial Intelligence in Healthcare

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial

Yao, Xiaoxi, McCoy, Rozalina G., Friedman, Paul A., Shah, Nilay D., Barry, Barbara A., Behnken, Emma M., Inselman, Jonathan W., Attia, Zachi I. +1 more·2020·American Heart Journal

DOI 10.1016/j.ahj.2019.10.007

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Abstract

Background A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a confirmatory transthoracic echocardiogram (TTE) for previously undiagnosed patients, thereby facilitating early diagnosis and treatment. Objectives To prospectively evaluate a novel artificial intelligence (AI) screening tool for detecting low EF in primary care practices. Design The EAGLE trial is a pragmatic two-arm cluster randomized trial (NCT04000087) that will randomize >100 clinical teams (i.e., clusters) to either intervention (access to the new AI screening tool) or control (usual care) at 48 primary care practices across Minnesota and Wisconsin. The trial is expected to involve approximately 400 clinicians and 20,000 patients. The primary endpoint is newly discovered EF ≤50%. Eligible patients will include adults who undergo ECG for any reason and have not been previously diagnosed with low EF. Data will be pulled from the EHR, and no contact will be made with patients. A positive deviance qualitative study and a post-implementation survey will be conducted among select clinicians to identify facilitators and barriers to using the new screening report. Summary This trial will examine the effectiveness of the AI-enabled ECG for detection of asymptomatic low EF in rout…

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AI-ECG outputs do not improve care unless responsibility for action is clearly assigned.
“AI-ECG outputs do not improve care unless responsibility for action is clearly assigned.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [55]
In the EAGLE study, 358 primary care clinicians were randomized to intervention or control across 48 upper Midwest clinics/hospitals; the primary endpoint was a new diagnosis of EF less than or equal to 50% within 3 months.
“In this study, 358 primary care clinicians were randomized to either an intervention group (access to AI screening results) or a control group (usual care) across 48 upper Midwest clinics/hospitals.The primary endpoint was a new diagnosis of EF less than or equal to 50% within 3 months.”
From Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. (2022) · cites this paper as [14]
Electrocardiograms were considered positive if the probability of having low EF was higher than a threshold selected to have similar sensitivity and specificity for a cohort with a prevalence of 9.0%.
“Electrocardiograms were considered positive if the probability of having low EF was higher than a threshold selected to have similar sensitivity and specificity for a cohort with a prevalence of 9.0%.14”
From Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. (2022) · cites this paper as [14]
The EAGLE study showed an increase in detecting low EF from 178 (1.6%) in the control group to 244 (2.1%) in the intervention group (OR 1.32; 95% CI 1.01-1.61; P=.007).
“The study showed an increase in detecting low EF from 178 (1.6%) in the control group to 244 (2.1%) for the intervention group (odds ratio, 1.32; 95% CI, 1.01 to 1.61]; P¼0.007).”
From Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. (2022) · cites this paper as [14]

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Thao, V., Zhu, Y., Tseng, A. S., Inselman, J., Borah, B. J., McCoy, R. G., Attia, Z. I., López-Jiménez, F. A., Pellikka, P. A., Rushlow, D. R., et al. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.10.001 · Cited by 19 · see citing works
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Authors: Yao, X., McCoy, R. G., Friedman, P. A., Shah, N. D., Barry, B. A., Behnken, E. M., Inselman, J. W., Attia, Z. I., & Noseworthy, P. A.
Affiliations: Mayo Clinic (US)
Corresponding author: Yao, Xiaoxi
ORCID: Yao, Xiaoxi
McCoy, Rozalina G.
Friedman, Paul A.
Shah, Nilay D.
Barry, Barbara A.
Behnken, Emma M.
Inselman, Jonathan W.
Attia, Zachi I.
Noseworthy, Peter A.
Venue: American Heart Journal
Publisher: Elsevier BV
Published: 2020-01
Volume: 219
Pages: 31-36
ISSN: 0002-8703 (ISSN-L)
DOI: 10.1016/j.ahj.2019.10.007
OpenAlex: W2981624778
Cited by: 98
References: 24
Citations by year: 2026: 11 · 2025: 26 · 2024: 15 · 2023: 6 · 2022: 10 · 2021: 16 · 2020: 13 · 2019: 1
FWCI: 3.17
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: American Heart Journal (publishedVersion)
PubMed (publishedVersion)
Funders: Mayo Clinic
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: reduced ejection fraction, artificial intelligence, electrocardiogram, deep learning, primary care, electronic health records, transthoracic echocardiography, asymptomatic heart failure, implementation barriers and facilitators, real-world effectiveness
MeSH terms: Deep Learning, Artificial Intelligence, Cardiac Output, Low, Cost-Benefit Analysis, Echocardiography, Electrocardiography, Heart Failure, Humans, Informed Consent, Prospective Studies, Multicenter Studies as Topic, Randomized Controlled Trials as Topic, Sample Size, Electronic Health Records, Asymptomatic Diseases, Pragmatic Clinical Trials as Topic

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ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial

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