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

Status: Recruiting
Location: See location...
Intervention Type: Other
Study Type: Observational
SUMMARY

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)…

Eligibility
Participation Requirements
Sex: All
Healthy Volunteers: f
View:

• Computerized health report (CHR)

• Spontaneous breathing test should have been performed

Locations
Other Locations
France
University Hospital of Nice
RECRUITING
Nice
Contact Information
Primary
Romain LOMBARDI
lombardi.r@chu-nice.fr
0669032616
Backup
Jean DELLAMONICA
dellamonica.j@chu-nice.fr
Time Frame
Start Date: 2023-01-01
Estimated Completion Date: 2025-12-12
Participants
Target number of participants: 500
Treatments
Spontaneous Breathing Test
The first group will be composed only by patients admitted in intensive care/critical care for ventilation support, and who successed the spontaneous breathing test.
Non Spontaneous Breathing Test
The second group will be composed only by patients admitted in intensive care/critical care for ventilation support, and who failed the spontaneous breathing test.
Related Therapeutic Areas
Sponsors
Leads: Centre Hospitalier Universitaire de Nice

This content was sourced from clinicaltrials.gov