Weaning From Mechanical Ventilation in Care UnitNCT05886803
Prediction of the Spontaneous Breathing Test Success Using Biosignal and Biomarker in Critical Care Unit by a Machine Learning Approach
An observational study of Weaning From Mechanical Ventilation in Care Unit, run by Centre Hospitalier Universitaire de Nice at 1 site in France.
Trial 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]. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Weaning From Mechanical Ventilation in Care Unit
- What is being tested?
- Spontaneous ventilation test
- How many people
- 500 participants
- Where
- 1 siteFrance
- Study dates
- 1 Jan 2023to 25 Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: All patient admited in Intensive Care/Critical Care who needed ventilatory support, whatever the etiology requiring it.
Who may be able to join
- Computerized health report (CHR)
- Spontaneous breathing test should have been performed
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Jean DELLAMONICA contact
France1 site, 1 recruiting
- Recruiting
University Hospital of Nice
Nice, 06200
Romain LOMBARDI, 0669032616 ext. +33, lombardi.r@chu-nice.fr
Jean DELLAMONICA, dellamonica.j@chu-nice.fr
About this study
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.
Read more
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)…
- Being tested
- Spontaneous ventilation test
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Spontaneous ventilation testOther
The purpose is to mimic ventilation conditions after extubation and thus to help the clinician predict the outcome of an extubation decision.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Spontaneous ventilation 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. Receives: Other: Spontaneous ventilation test |
What is being measured
The primary outcome is the main question the study is designed to answer.
Prediction of the spontaneous breathing test outcome.
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.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Jun 2023
- Study start1 Jan 2023
- Primary completion12 Dec 2024
- Study completion25 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Weaning From Mechanical Ventilation in Care UnitStudies of the same condition, open and under way.
- More trials run by Centre Hospitalier Universitaire de NiceThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Centre Hospitalier Universitaire de Nice
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05886803
- Sponsor's protocol ID
- 23Rea01
- Status last verified
- Jun 2026
- First submitted
- 24 May 2023
- Last updated
- 29 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of the Spontaneous Breathing Test Success Using Biosignal and Biomarker in Critical Care Unit by a Machine Learning Approach
- ClinicalTrials.gov ID
- NCT05886803
- Acronym / sponsor ID
- 23Rea01
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Centre Hospitalier Universitaire de NiceUniversity, hospital or other
- Study start
- 1 Jan 2023 (actual)
- Primary completion
- 12 Dec 2024 (actual)
- Study completion
- 25 Jun 2027 (estimated)
- First posted
- 2 Jun 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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)…
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Spontaneous Breathing Test | – | Other: Spontaneous ventilation testThe 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 | – | Other: Spontaneous ventilation testThe second group will be composed only by patients admitted in intensive care/critical care for ventilation support, and who failed the spontaneous breathing test. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All patient admited in Intensive Care/Critical Care who needed ventilatory support, whatever the etiology requiring it.
Key inclusion criteria (2 listed)
- Computerized health report (CHR)
- Spontaneous breathing test should have been performed
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Spontaneous ventilation testThe purpose is to mimic ventilation conditions after extubation and thus to help the clinician predict the outcome of an extubation decision. | Other | Non Spontaneous Breathing Test, Spontaneous Breathing Test |
Primary outcome
Prediction of the spontaneous breathing test outcome.
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jun 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Jean DELLAMONICA contact
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Weaning From Mechanical Ventilation in Care Unit, run by Centre Hospitalier Universitaire de Nice at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 29 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Romain LOMBARDI (0669032616 ext. +33, lombardi.r@chu-nice.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction of the spontaneous breathing test outcome. (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Jun 2026.