NCT06506123

Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm

Automated Detection and Classification of Patient-Ventilator Dyssynchrony With a Machine Learning Algorithm

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 80 (estimated)
• Sponsor: University of Sao Paulo General Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: —

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.

Conditions

• Respiratory Failure

Interventions

Type	Name	Description
DEVICE	Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies	Machine learning algorithm to detect and classify patient-ventilator dyssynchronies, which is integrated in the mechanical ventilator (Fleximag Max, Magnamed, Brazil).

Timeline

• Start date: 2024-05-25
• Primary completion: 2025-05-24
• Completion: 2025-12-24
• First posted: 2024-07-17
• Last updated: 2024-07-17

Locations

1 site across 1 country: Brazil