Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea.

Journal: Circulation. Arrhythmia and electrophysiology
Published Date: Aug 1, 2020

Abstract

BACKGROUND: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD).

Authors

  • Demilade Adedinsewo
    Division of Cardiovascular Medicine (D.A.), Mayo Clinic, Jacksonville, FL.
  • Rickey E Carter
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida.
  • Zachi Attia
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Patrick Johnson
    Department of Health Sciences Research (R.E.C., P.J.), Mayo Clinic, Jacksonville, FL.
  • Anthony H Kashou
    Department of Medicine, Mayo Clinic, Rochester, Minnesota.
  • Jennifer L Dugan
    Division of Cardiovascular Medicine (Z.A., J.L.D., P.A.F., F.L.-J., P.A.N.), Mayo Clinic, Rochester, MN.
  • Michael Albus
    Department of Emergency Medicine (M.A., J.M.S.), Mayo Clinic, Jacksonville, FL.
  • Johnathan M Sheele
    Department of Emergency Medicine (M.A., J.M.S.), Mayo Clinic, Jacksonville, FL.
  • Fernanda Bellolio
    Department of Emergency Medicine (F.B.), Mayo Clinic, Rochester, MN.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Francisco Lopez-Jimenez
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.

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