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

Nov 12, 2019American heart journal

Using AI to Screen Heart Function by ECG: Reasoning and Plan for a Practical Group-Based Trial

AI simplified

Abstract

The EAGLE trial will involve over 20,000 patients in a randomized evaluation of an AI tool for detecting low ejection fraction.

The trial will compare the effectiveness of an AI screening tool against usual care in primary care settings.
Primary endpoint is the identification of newly discovered ejection fraction ≤50% among previously undiagnosed adults.
Eligible participants are adults undergoing ECG for any reason without prior low EF diagnosis.
Data will be collected from electronic health records without direct patient contact.
A qualitative study will explore the facilitators and barriers to using the AI screening tool among clinicians.

AI simplified

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 routine primary care practices and will be among the first to prospectively evaluate the value of AI in real-world practice. Its findings will inform future implementation strategies for the translation of other AI-enabled algorithms.

Full Text

Full text is available at the source.

View full text (XML) ↗View full text ↗

Abstract

Full Text

Related papers

what lands in your inbox each week: