Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach.

Journal: Anesthesia and analgesia
Published Date: May 1, 2020

Abstract

BACKGROUND: Heart failure with reduced ejection fraction (HFrEF) is a condition imposing significant health care burden. Given its syndromic nature and often insidious onset, the diagnosis may not be made until clinical manifestations prompt further evaluation. Detecting HFrEF in precursor stages could allow for early initiation of treatments to modify disease progression. Granular data collected during the perioperative period may represent an underutilized method for improving the diagnosis of HFrEF. We hypothesized that patients ultimately diagnosed with HFrEF following surgery can be identified via machine-learning approaches using pre- and intraoperative data.

Authors

  • Michael R Mathis
  • Milo C Engoren
    From the Department of Anesthesiology.
  • Hyeon Joo
    School of Information, University of Michigan, Ann Arbor, Michigan, USA.
  • Michael D Maile
    From the Department of Anesthesiology.
  • Keith D Aaronson
    Department of Internal Medicine - Cardiovascular Medicine Division, University of Michigan Health System, Ann Arbor, Michigan.
  • Michael L Burns
    Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan.
  • Michael W Sjoding
    1 Department of Internal Medicine, and.
  • Nicholas J Douville
    From the Department of Anesthesiology.
  • Allison M Janda
    From the Department of Anesthesiology.
  • Yaokun Hu
    From the Department of Anesthesiology.
  • Kayvan Najarian
  • Sachin Kheterpal
    Department of Anesthesiology, University of Michigan, Ann Arbor, MI, 48109, USA.

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