Detecting patient-ventilator mismatches using machine learning
Automated Detection and Classification of Patient-Ventilator Dyssynchrony With a Machine Learning Algorithm
University of Sao Paulo General Hospital · NCT06506123
This study is testing a new machine learning tool to see if it can better spot problems between patients and their ventilators compared to the traditional methods used by experts.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 80 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | University of Sao Paulo General Hospital (other) |
| Locations | 1 site (São Paulo, São Paulo) |
| Trial ID | NCT06506123 on ClinicalTrials.gov |
What this trial studies
This observational study aims to evaluate the effectiveness of a machine learning algorithm in detecting and classifying patient-ventilator dyssynchronies compared to traditional methods used by mechanical ventilation experts. The study will analyze data from patients on assisted or assist-controlled mechanical ventilation, utilizing esophageal pressure waveforms alongside mechanical ventilator data. By comparing the algorithm's accuracy to the gold-standard assessments made by experts, the study seeks to improve the detection of dyssynchronies, which can significantly impact patient outcomes.
Who should consider this trial
Good fit: Ideal candidates for this study are patients receiving assisted or assist-controlled mechanical ventilation and monitored with an esophageal pressure balloon.
Not a fit: Patients whose families refuse participation or those not under mechanical ventilation will not benefit from this study.
Why it matters
Potential benefit: If successful, this study could lead to more accurate and timely detection of patient-ventilator dyssynchronies, improving patient care in respiratory failure.
How similar studies have performed: While the use of machine learning in medical diagnostics is growing, this specific approach to detecting patient-ventilator dyssynchronies is relatively novel and has not been extensively tested in prior studies.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Subjects under assisted or assist-controlled mechanical ventilation and monitored with esophageal pressure balloon. Exclusion Criteria: * Refusal from patient's family or attending physician
Where this trial is running
São Paulo, São Paulo
- Heart Institute, University of São Paulo — São Paulo, São Paulo, Brazil (RECRUITING)
Study contacts
- Study coordinator: Glauco M Plens, MD
- Email: glaucomplens@gmail.com
- Phone: +5511982213020
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Respiratory Failure, mechanical ventilation, artificial intelligence