Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

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

Abstract

BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitoring data from cardiac implantable electronic devices, we sought to evaluate if machine learned signatures of AF burden could provide prognostic information on near-term risk of stroke when compared to conventional risk scores.

Authors

  • Lichy Han
    Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
  • Mariam Askari
    Veterans Affairs Palo Alto Health Care System, Palo Alto, CA (M.A., S.K.S., J.F., M.P.T.).
  • Russ B Altman
    Departments of Medicine, Genetics and Bioengineering, Stanford University, Stanford, California, United States of America.
  • Susan K Schmitt
    Veterans Affairs Palo Alto Health Care System, Palo Alto, CA (M.A., S.K.S., J.F., M.P.T.).
  • Jun Fan
    Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jason P Bentley
    Quantitative Sciences Unit (J.P.B.), Stanford University School of Medicine, CA.
  • Sanjiv M Narayan
    Biomedical Informatics Training Program (L.H., S.M.N.), Stanford University, CA.
  • Mintu P Turakhia
    Department of Medicine and Center for Digital Health, Stanford University School of Medicine, Stanford, CA, USA.

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