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

Area of Science:

• Anesthesiology
• Artificial Intelligence
• Respiratory Physiology

Background:

• Opioid-induced respiratory depression (OIRD) assessment traditionally relies on respiratory rate, oxygen saturation, CO2 levels, and mental status.
• Ataxic breathing is a recognized opioid effect, but lacks standardized severity assessment.
• This study addresses the need for objective quantification of ataxic breathing.

Purpose of the Study:

• To explore the feasibility of using a machine-learning algorithm to quantify opioid-induced ataxic breathing severity.
• To evaluate the interrater agreement between domain experts and a machine-learning algorithm in assessing ataxic breathing.

Main Methods:

• Healthy volunteers received propofol and remifentanil infusions to simulate OIRD.
• Respiration data were collected using respiratory inductance plethysmography (RIP) and an intranasal pressure transducer.
• A support vector machine (SVM) was trained on expert-labeled data to quantify ataxic breathing severity.

Main Results:

• High interrater agreement was observed among 3 domain experts (Krippendorff alpha = 0.93).
• The machine-learning algorithm demonstrated strong agreement with domain experts (Vanbelle kappa = 0.98 for RIP, 0.96 for intranasal pressure).
• Both sensor inputs (RIP and intranasal pressure) yielded high agreement with expert assessments.

Conclusions:

• A machine-learning algorithm can effectively quantify ataxic breathing severity, consistent with expert consensus.
• This AI-driven methodology shows promise for improving OIRD identification.
• The approach may complement existing clinical monitoring tools for OIRD.