Machine Learning To Rate Ataxic Breathing Severity
ID U-6676
Category Diagnostics
Subcategory Software
Researchers
Brief Summary
Algorithm that monitors for ataxic breathing events to determine risk of opioid-induced respiratory depression.
Problem Statement
Opioid-induced respiratory depression 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, the presence of ataxic breathing is not routinely monitored or scored. A major obstacle to widespread monitoring for ataxic breathing is the necessity for manual, offline analysis.
Technology Description
University of Utah researchers have developed a machine learning algorithm that enables real-time, quantitative monitoring of patients’ breathing patterns. This algorithm determines the severity of ataxic breathing events and has been verified to classify those events in a manner consistent with manual analysis. Accordingly, the algorithm should enable detection of opioid-induced respiratory depression events and determine their severity.
Stage of Development
Pre-Clinical Validation
Benefit
• Obviates the need for manual monitoring for ataxic breathing.
• Enables real-time detection of opioid-induced respiratory depression.
Publications
Ermer, Sean C et al. An Automated Algorithm Incorporating Poincaré Analysis Can Quantify the Severity of Opioid-Induced Ataxic Breathing. Anesthesia and analgesia vol. 130,5 (2020): 1147-1156. doi:10.1213/ANE.0000000000004498
Contact Info
Huy Tran
(801) 581-7792
huy.tran@utah.edu