Cost Effectiveness of an Electrocardiographic Deep Learning Algorithm to Detect Asymptomatic Left Ventricular Dysfunction.

Journal: Mayo Clinic proceedings
PMID: 34116837

Abstract

OBJECTIVE: To evaluate the cost-effectiveness of an artificial intelligence electrocardiogram (AI-ECG) algorithm under various clinical and cost scenarios when used for universal screening at age 65.

Authors

  • Andrew S Tseng
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Viengneesee Thao
    Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN.
  • Bijan J Borah
    Mayo Clinic College of Medicine and Science and the Kern Center for the Science of Health Care Delivery, Rochester, MN, USA. Electronic address: borah.bijan@mayo.edu.
  • Itzhak Zachi Attia
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Jose Medina Inojosa
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
  • Suraj Kapa
    Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Rickey E Carter
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida.
  • 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.
  • Xiaoxi Yao
    Department of Health Sciences Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.

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