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Using weak signals to predict spontaneous breathing trial success: a machine learning approach.

Romain Lombardi^1,2, Mathieu Jozwiak^3,4, Jean Dellamonica^5,4

¹Critical Care Unit, Pasteur 2 University Hospital, 30 Voie Romaine, 06000, Nice, France. lombardi.r@chu-nice.fr.

Intensive Care Medicine Experimental

|March 18, 2025

Summary

Machine learning models effectively predict spontaneous breathing trial success using biosignals. This AI application aids in predicting mechanical ventilation weaning, improving patient outcomes.

Keywords:

Biosignal Critical care ICU Machine learning Mechanical ventilation Spontaneous breathing trial Weak signals Weaning

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Area of Science:

Intensive Care Medicine
Biomedical Engineering
Artificial Intelligence in Healthcare

Background:

Mechanical ventilation weaning is critical in ICU care, with high mortality rates for difficult cases.
Predicting weaning success is challenging due to complex, multifactorial influences.
Biosignals, often considered 'weak signals', hold potential for improved prediction.

Purpose of the Study:

To evaluate machine learning (ML) models for predicting spontaneous breathing trial (SBT) success.
To identify key biosignals and variables crucial for accurate weaning prediction.
To explore the application of AI in analyzing complex patient data for better clinical decisions.

Main Methods:

Retrospective analysis of 232 ICU patients undergoing mechanical ventilation (MV) across two centers.
Development of ML algorithms using discrete variables and time-series biosignals from the 24 hours preceding SBT.
Data collected between January 2020 and April 2023.

Main Results:

Support Vector Classifier (SVC) model demonstrated superior performance in predicting SBT success.
SVC achieved an AUC-PR of 0.963 and AUROC of 0.922.
Statistical significance was achieved for both metrics (p < 0.001).

Conclusions:

ML models utilizing biosignals are effective predictors of SBT success.
AI offers a promising avenue for developing multidimensional models to analyze weak signals in mechanical ventilation weaning.
This approach can enhance the prediction of weaning outcomes and potentially reduce mortality.