Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm

Recruiting

• Conditions: Respiratory Failure

• Registration Number: NCT06506123
• Lead Sponsor: University of Sao Paulo General Hospital

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 Timeline

• Status Verified: 2024-05
• Study First Submitted: 2024-05-24
• Study Start: 2024-05-25
• Study First Submitted QC: 2024-07-10
• Last Update Submitted: 2024-07-10
• Study First Posted: 2024-07-17
• Last Update Posted: 2024-07-17
• Primary Completion: 2025-05-24
• Study Completion: 2025-12-24

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 80

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Diagnostic Accuracy of the Artificial Intelligence algorithm	3 days	Sensitivity, specificity, positive predictive value, negative predictive value of the artificial intelligence algorithm to detect and classify patient-ventilator dyssynchronies. These accuracy indexes will be estimated for each kind of dyssinchrony: ineffective effort, autotriggering, double triggering, reverse triggering, reverse triggering with a double cycle

Secondary Outcome Measures

Name	Time	Method
Pendelluft detection	3 days	Percentage of cycles with pendelluft detected with the artificial intelligence algorithm compared to the percentage of cycles with pendelluft detected with the esophageal pressure

Trial Locations

Locations (1)

Heart Institute, University of São Paulo; 🇧🇷 Sao Paulo, SP, Brazil