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

Journal: American heart journal
PMID: 31710842

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.

Authors

  • Xiaoxi Yao
    Department of Health Sciences Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota.
  • Rozalina G McCoy
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Nilay D Shah
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
  • Barbara A Barry
    Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN.
  • Emma M Behnken
    Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN.
  • Jonathan W Inselman
    Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, 55905 Rochester, MN.
  • Zachi I Attia
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.

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