Automated Detection of Patient-Ventilator Asynchrony in Mechanically Ventilated Patients

Master thesis (2024)

Authors

M.J. de Bie Mechanical Engineering

Contributors

D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
A. Schoe Leiden University Medical Center (supervisor 1)
D. van Westerloo Leiden University Medical Center (supervisor 2)
Faculty
Mechanical Engineering
Machine learning Intensive Care Unit (ICU) Mechanical Ventilation Patient-ventilator asynchrony
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Published Date

12-04-2024

Language

English

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Faculty

Mechanical Engineering

Abstract

Introduction
Patient-ventilator asynchrony (PVA) poses a significant challenge in the management of mechanically ventilated patients, contributing to adverse clinical outcomes. Current methods of detecting PVA rely on visual assessment by clinicians, leading to subjectivity and inconsistency. Therefore, there is a need for automated techniques to identify PVA accurately and efficiently. In this study, we explore the application of supervised and unsupervised machine learning algorithms to develop an automatic detection system for PVA.

Methods
This study was conducted at the ICU of the LUMC in Leiden, the Netherlands. Patients eligible for inclusion were mechanically ventilated with an esophageal balloon inserted. Data collected included flow, Paw, and Pes curves, which were labelled using an open-source data labeling platform and processed in Python. Supervised CNN models were trained for different ventilation modes, while unsupervised techniques, utilizing Mahalanobis distance, were explored for data pre-labeling. The discriminative capability of the models was assessed using AUROC values.
Results
25 patients were included in this study and labelled by clinicians. Using an unsupervised machine learning technique based on the Mahalanobis distance for data pre-labeling, a threshold of 3.5 was selected, resulting in a 95% accuracy in correctly identifying normal breaths. Creating different CNN models for automating the detection of PVA the results demonstrate the discriminative capability of the various models across all ventilation modes, PSV and PCV ventilation. They can differentiate between normal and abnormal breaths, as indicated by the AUROC values of 0.85(±0.08), 0.83 (±0.12), and 0.80 (±0.28) respectively.

Discussion
This study investigated the application of machine learning techniques to analyse ventilation data in critical care settings. Through a combination of unsupervised and supervised learning methods, we have explored the automation of the labelling process and the development of predictive models for identifying patient-ventilator asynchronies (PVAs).