Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study.

Journal: Journal of medical systems
PMID: 39042285

Abstract

BACKGROUND:  Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalations (UCE) following post-anesthesia care unit (PACU) discharge. Studies indicate patient risk factors for UCE, but determining which factors are most important is unclear. Machine learning (ML) can predict clinical events. We hypothesized that ML could predict patient UCE after PACU discharge in surgical patients and identify specific risk factors.

Authors

  • Andrew B Barker
    Division of Critical Care Medicine, Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, 901 19th Street South, PBMR 302, Birmingham, AL, 35294, United States of America.
  • Ryan L Melvin
    a Department of Physics , Wake Forest University , Winston-Salem, NC , USA.
  • Ryan C Godwin
    Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States of America.
  • David Benz
    Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States of America.
  • Brant M Wagener
    Division of Critical Care Medicine, Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, 901 19th Street South, PBMR 302, Birmingham, AL, 35294, United States of America. bwagener@uabmc.edu.

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