Viewing Study NCT06506123



Ignite Creation Date: 2024-10-26 @ 3:35 PM
Last Modification Date: 2024-10-26 @ 3:35 PM
Study NCT ID: NCT06506123
Status: RECRUITING
Last Update Posted: None
First Post: 2024-05-24

Brief Title: Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm
Sponsor: None
Organization: None

Study Overview

Official Title: Automated Detection and Classification of Patient-Ventilator Dyssynchrony With a Machine Learning Algorithm
Status: RECRUITING
Status Verified Date: 2024-05
Last Known Status: None
Delayed Posting: No
If Stopped, Why?: Not Stopped
Has Expanded Access: No
If Expanded Access, NCT#: N/A
Has Expanded Access, NCT# Status: N/A
Acronym: None
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 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
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 algorithm will be compared with the gold-standard defined as dyssynchronies detected and classified by mechanical ventilation experts

Experts will analyzed airway pressure flow volume and esophageal pressure waveforms to detect and classify dyssynchronies

Study Oversight

Has Oversight DMC: None
Is a FDA Regulated Drug?: None
Is a FDA Regulated Device?: None
Is an Unapproved Device?: None
Is a PPSD?: None
Is a US Export?: None
Is an FDA AA801 Violation?: None