عنوان انگلیسی مقاله:
ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial
ترجمه فارسی عنوان مقاله:
غربالگری هدایت شده با هوش مصنوعی ECG برای کسر کم دفع (EAGLE): منطق و طراحی یک آزمایش تصادفی خوشه عملی
Sciencedirect - Elsevier - American Heart Journal, 219 (2020) 31-36. doi:10.1016/j.ahj.2019.10.007
Xiaoxi Yao, PhD, a,b,c Rozalina G. McCoy, MD, MS, a,d Paul A. Friedman, MD, c Nilay D. Shah, PhD, a,b Barbara A. Barry, PhD, b Emma M. Behnken, e Jonathan W. Inselman, M.S., a Zachi I. Attia, M.S., c and Peter A. Noseworthy, MDc Rochester, MN
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
Design The EAGLE trial is a pragmatic two-arm cluster randomized trial (NCT04000087) that will randomize N100
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. (Am Heart J 2020;219:31-6.)