Status:
RECRUITING
Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm
Lead Sponsor:
University of Sao Paulo General Hospital
Collaborating Sponsors:
Magnamed Tecnologia Medica S/A
Conditions:
Respiratory Failure
Eligibility:
All Genders
18+ years
Brief Summary
This is a diagnostic study aiming to compare accuracy to detect and classify patient-ventilator dyssynchronies by a machine learning algorithm, compared to the gold-standard defined as dyssynchronies ...
Detailed Description
This is a diagnostic, observational study, aiming to assess patient-ventilator dyssynchrony automated detection and classification by a machine learning algorithm. Accuracy of the machine learning alg...
Eligibility Criteria
Inclusion
- Subjects under assisted or assist-controlled mechanical ventilation and monitored with esophageal pressure balloon.
Exclusion
- Refusal from patient's family or attending physician
Key Trial Info
Start Date :
May 25 2024
Trial Type :
OBSERVATIONAL
Allocation :
ESTIMATED
End Date :
December 24 2025
Estimated Enrollment :
80 Patients enrolled
Trial Details
Trial ID
NCT06506123
Start Date
May 25 2024
End Date
December 24 2025
Last Update
July 17 2024
Active Locations (1)
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1
Heart Institute, University of São Paulo
São Paulo, São Paulo, Brazil, 05403900