Prediction of the Spontaneous Breathing Test Success Using Biosignal and Biomarker in Critical Care Unit by a Machine Learning Approach
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Spontaneous ventilation test.
- Who it may be relevant to
- Registry conditions: Weaning From Mechanical Ventilation in Care Unit. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- France
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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)…
Interventions
- Other Spontaneous ventilation test
The purpose is to mimic ventilation conditions after extubation and thus to help the clinician predict the outcome of an extubation decision.
Primary outcome measures
- Prediction of the spontaneous breathing test outcome. [Time frame: 2 years]
Eligibility criteria
Inclusion criteria
- Computerized health report (CHR)
- Spontaneous breathing test should have been performed
Exclusion criteria
- Spontaneous breathing test has not been performed,
- Biosignal (cardiac, respiratory) are not registered in the CHR
- Patient died before the spontaneous breathing test
- Opposition to the study has been expressed.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Case-control
Study locations
France · 1 center
- University Hospital of Nice — Nice
Identifiers
NCT: NCT05886803 · 23Rea01