Prediction of Prolonged Opioid Use After Surgery in Adolescents: Insights From Machine Learning.

Journal: Anesthesia and analgesia
PMID: 33939656

Abstract

BACKGROUND: Long-term opioid use has negative health care consequences. Patients who undergo surgery are at risk for prolonged opioid use after surgery (POUS). While risk factors have been previously identified, no methods currently exist to determine higher-risk patients. We assessed the ability of a variety of machine-learning algorithms to predict adolescents at risk of POUS and to identify factors associated with this risk.

Authors

  • Andrew Ward
    From the Department of Electrical Engineering, Stanford University, Stanford, California.
  • Trisha Jani
    Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California.
  • Elizabeth De Souza
    Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California.
  • David Scheinker
    Department of Management Science and Engineering (D.S.), Stanford University, CA.
  • Nicholas Bambos
    From the Department of Electrical Engineering, Stanford University, Stanford, California.
  • T Anthony Anderson
    Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California.

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