Using machine learning to predict success in weaning patients off mechanical ventilation

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational study aims to develop a predictive algorithm using machine learning techniques to assess the likelihood of success in spontaneous breathing tests for patients in critical care units. By analyzing biosignals such as cardiac rate and ventilatory parameters, the study seeks to improve decision-making during the weaning process from mechanical ventilation. The goal is to enhance patient outcomes by reducing morbidity and mortality associated with difficult weaning attempts. The study will include patients who have undergone spontaneous breathing tests and have relevant biosignal data recorded in their health reports.

Why it matters

Who can join: the full rules

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.

Where it's running

• University Hospital of Nice
  Nice, France Recruiting

Study contacts

• Study coordinator: Romain LOMBARDI
• Email: lombardi.r@chu-nice.fr
• Phone: 0669032616

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.