NCT05886803

Prediction of the Spontaneous Breathing Test Success Using Biosignal and Biomarker in Critical Care Unit by a Machine Learning Approach

Status unknown Last updated 2 June 2023

What this trial tests

trial testing Spontaneous ventilation test in Weaning From Mechanical Ventilation in Care Unit in 500 participants. Status unknown.

Timeline

1 January 2023

Primary endpoint
12 December 2024

12 December 2025

Quick facts

Lead sponsor	Centre Hospitalier Universitaire de Nice
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	500
Start date	1 January 2023
Primary completion	12 December 2024
Estimated completion	12 December 2025
Sites	1 location across France

Drugs / interventions tested

• Spontaneous ventilation test

Conditions studied

• Weaning From Mechanical Ventilation in Care Unit — all drugs for Weaning From Mechanical Ventilation in Care Unit →

Sponsor

Centre Hospitalier Universitaire de Nice

Who can join

Eligibility, any sex, with Weaning From Mechanical Ventilation in Care Unit. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Context:

Several authors have been interested in applying Artificial Intelligence (AI) to medicine, using various Machine Learning (ML) techniques: managing septic shock, predicting renal failure... \[1, 2\] AI has an important place in decision support for clinicians \[3\]. The weaning period is a really important time in the management of a patient on mechanical ventilation and can take up to half of the time spent in intensive care unit. The first weaning attempt is unsuccessful in 20% of patients However, mortality can be as high as 38% in patients with the most difficult weaning \[4\]. Only a few studies have looked at the application of machine learning in this area, and only one has looked at the use of biosignals (cardiac rate, ECG, ventilatory parameters…) \[5-7\]. To improve morbidity, mortality and reduce length of stay, it is essential to be able to predict the success of the spontaneous breathing test and extubation.

Investigators propose to develop a predictive algorithm for the success of a ventilatory weaning test based on biosignal records and others features.

Methods:

It is a critical care, oligo-centric and retrospective study the investigators included biosignal variables extracted from the electronic medical record, such as respiratory (RR, minute volume...), cardiac (systolic pressure, heart rate...), ventilator parameters and other discrete variables (age, comorbidity...). Most biosignal variables are minute-by-minute records. Recording starts 48 hours before the test and stops at the start of the weaning test. The investigators extracted features from these records, combined them with other biomarkers, and applied several machine learning algorithms: Logistic Regression, Random Forest Classifier, Support Vector Classifier (SVC), XGBoost, and Light Gradient Boosting Method (LGBM)…

Publications & conference data

1 peer-reviewed publication reference this trial (live from Europe PMC):

1. Using weak signals to predict spontaneous breathing trial success: a machine learning approach.
   Lombardi R, Jozwiak M, Dellamonica J, Pasquier C. · · 2025 · PMID 40100563 · DOI 10.1186/s40635-025-00724-0