Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.

Journal: Circulation. Cardiovascular quality and outcomes
Published Date: Mar 1, 2019

Abstract

BACKGROUND: Patients with peripheral artery disease (PAD) are at risk of major adverse cardiac and cerebrovascular events. There are no readily available risk scores that can accurately identify which patients are most likely to sustain an event, making it difficult to identify those who might benefit from more aggressive intervention. Thus, we aimed to develop a novel predictive model-using machine learning methods on electronic health record data-to identify which PAD patients are most likely to develop major adverse cardiac and cerebrovascular events.

Authors

  • Elsie Gyang Ross
    Division of Vascular Surgery, Stanford Health Care, Stanford, Calif.
  • Kenneth Jung
    Program in Biomedical Informatics, Stanford University, Stanford, California, USA.
  • Joel T Dudley
    1Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
  • Nicholas J Leeper
    Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, California, USA.
  • Nigam H Shah
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

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