Actively Recruiting
Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm
Led by University of Sao Paulo General Hospital · Updated on 2024-07-17
80
Participants Needed
1
Research Sites
82 weeks
Total Duration
On this page
Sponsors
U
University of Sao Paulo General Hospital
Lead Sponsor
M
Magnamed Tecnologia Medica S/A
Collaborating Sponsor
AI-Summary
What this Trial Is About
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 diagnosed and classified by mechanical ventilator and esophageal pressure waveforms analyzed by experts. The main question of this study is: • Are patient-ventilator dyssynchronies accurately detected and classified by an artificial intelligence algorithm when compared to experts analyzing esophageal pressure and mechanical ventilator waveforms?
CONDITIONS
Official Title
Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Subjects under assisted or assist-controlled mechanical ventilation and monitored with esophageal pressure balloon.
You will not qualify if you...
- Refusal from patient's family or attending physician
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Heart Institute, University of São Paulo
São Paulo, São Paulo, Brazil, 05403900
Actively Recruiting
Research Team
G
Glauco M Plens, MD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
Not the Right Trial for You?
Explore thousands of other clinical trials that might be a better match.
Sign up to get personalized trial recommendations delivered to your inbox.
Already have an account? Log in here