An Automated Algorithm Incorporating Poincaré Analysis Can Quantify the Severity of Opioid-Induced Ataxic Breathing.

Journal: Anesthesia and analgesia
Published Date:

Abstract

BACKGROUND: Opioid-induced respiratory depression (OIRD) is traditionally recognized by assessment of respiratory rate, arterial oxygen saturation, end-tidal CO2, and mental status. Although an irregular or ataxic breathing pattern is widely recognized as a manifestation of opioid effects, there is no standardized method for assessing ataxic breathing severity. The purpose of this study was to explore using a machine-learning algorithm for quantifying the severity of opioid-induced ataxic breathing. We hypothesized that domain experts would have high interrater agreement with each other and that a machine-learning algorithm would have high interrater agreement with the domain experts for ataxic breathing severity assessment.

Authors

  • Sean C Ermer
    From the Department of Anesthesia, University of Utah, Salt Lake City, Utah.
  • Robert J Farney
    Department of Medicine, Pulmonary Division, University of Utah, Salt Lake City, Utah.
  • Ken B Johnson
    From the Department of Anesthesia, University of Utah, Salt Lake City, Utah.
  • Joseph A Orr
    From the Department of Anesthesia, University of Utah, Salt Lake City, Utah.
  • Talmage D Egan
    From the Department of Anesthesia, University of Utah, Salt Lake City, Utah.
  • Lara M Brewer
    From the Department of Anesthesia, University of Utah, Salt Lake City, Utah.