Opioid-Induced Respiratory Depression, a Comprehensive Data Analysis

Opioid-Induced Respiratory Depression, a Comprehensive Data Analysis: Unravelling Opioid-Induced Respiratory Depression through Unsupervised Machine Learning on Respiratory Flow Data

Leenen, Femke (TU Delft Mechanical Engineering)

Dauwels, J.H.G. (mentor)
Boon, M. (mentor)
van Lemmen, M. (mentor)
Dahan, A. (graduation committee)

Delft University of Technology

Technical Medicine | Sensing and Stimulation

2024-04-25

Introduction
Opioids are vital for pain management but are highly addictive and may lead to opioid-induced respiratory depression (OIRD), which is the primary cause of death related to both prescription and illicit opioid use. This study employed unsupervised machine learning (ML) to examine potential changes in cluster patterns post-opioid administration and their relationship with respiratory depression levels. Additionally, comprehensive data analysis was conducted based on questions that resulted from observations of cluster behaviour.

Methods
Following preprocessing, a preliminary study evaluated three different models, each trained on the baseline and post-opioid epochs, aiming to identify the most efficient distance metrics and feature space combinations. These models included principal component analysis with Euclidean distance, computed feature space with Euclidean distance, and time series with dynamic time warping. Next, the preferred approach, determined by testing different hypotheses regarding the desired cluster behaviour, underwent further refinement, and predictions were generated for post-antagonist epochs. Finally, feature influence was determined and questions were identified for the data-analysis.

Results
In total, 34 subjects were included, resulting in a total of 6630 epochs for model development. Based on the preliminary study, it was decided to opt for a fuzzy clustering approach using calculated features as input, resulting in membership values indicating the probability of an epoch belonging to a certain cluster. A change in membership values was observed post-opioid as well as a recovery to baseline values post-antagonist in the majority of subjects when clustering was obtained for each subject individually. However, it was expressed as a sudden switch followed by a prolonged plateau phase rather than a gradual transition which was expected. Conversely, when trained collectively over all subjects, the majority of subjects showed no difference, probably due to the presence of inter-subject variability. Nevertheless, SHAP value analysis identified the same feature behaviour among subjects, despite variations in orientation and positioning, hinting at the potential for adjustment using static features. Yet, no significant correlations were found between static features and feature behaviour within this study.

Conclusion
In this study, a fuzzy clustering model was implemented, incorporating SHAP value analysis to enhance the interpretability of the clustering results. Although the model successfully identified changes in respiratory flow patterns associated with OIRD and subsequent recovery after naloxone administration, it requires further refinement.

anesthesia monitoring
Opioid Overdose
respiratory depression
Unsupervised Machine Learning

http://resolver.tudelft.nl/uuid:b0769402-7069-4403-8c8a-a7eeac9e4407

Student theses

master thesis

© 2024 Femke Leenen