Prediction of the Spontaneous Breathing Test Success Using Biosignal and Biomarker in Critical Care Unit by a Machine Learning Approach
- Conditions
- Weaning From Mechanical Ventilation in Care Unit
- Interventions
- Other: Spontaneous ventilation test
- Registration Number
- NCT05886803
- Lead Sponsor
- Centre Hospitalier Universitaire de Nice
- Brief Summary
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)...
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 500
- Computerized health report (CHR)
- Spontaneous breathing test should have been performed
- 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.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Spontaneous Breathing Test Spontaneous ventilation 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 Spontaneous ventilation 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.
- Primary Outcome Measures
Name Time Method Prediction of the spontaneous breathing test outcome. 2 years Two modalities of test are performed in clinical : either a T-tube test or a spontaneous ventilation test at low level of Inspiratory Support and PEEP (7AI 4PEEP, 7Ai 0PEEP).
A successful weaning test is defined by the absence of the following criteria at the end of the test: (i) increase in respiratory rate \> 35cpm, (ii) SpO2 \<90%, (iii) change of more than 20% in heart rate or blood pressure, (iv) modification of consciousness.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
University Hospital of Nice
🇫🇷Nice, France