Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular Hospitalization.

Journal: Circulation. Arrhythmia and electrophysiology
PMID: 39445440

Abstract

BACKGROUND: Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertable cardiac monitors (ICMs), may be useful in predicting near-term CVH.

Authors

  • James Peacock
    White Plains Hospital, NY (J.P.).
  • Evan J Stanelle
    Medtronic, Inc, Minneapolis, MN (E.J.S., L.C.J., R.K., D.S.).
  • Lawrence C Johnson
    Medtronic, Inc, Minneapolis, MN (E.J.S., L.C.J., R.K., D.S.).
  • Elaine M Hylek
    Department of Medicine, Boston University School of Medicine, Boston, MA.
  • Rahul Kanwar
    Medtronic, Inc, Minneapolis, MN (E.J.S., L.C.J., R.K., D.S.).
  • Dhanunjaya R Lakkireddy
    The Kansas City Heart Rhythm Institute and Research Foundation, Overland Park (D.R.L.).
  • Suneet Mittal
    The Valley Health System, Ridgewood, NJ (S.M.).
  • Rod S Passman
    Division of Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, IL (R.S.P.).
  • Andrea M Russo
    Electrophysiology and Arrhythmia Services, Cooper University Hospital, Camden, NJ (A.M.R.).
  • Dana Soderlund
    Medtronic, Inc, Minneapolis, MN (E.J.S., L.C.J., R.K., D.S.).
  • Mellanie True Hills
    StopAfib.org, American Foundation for Women's Health, Decatur, TX (M.T.H.).
  • Jonathan P Piccini
    Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.P.P.).

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