Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm

Last updated: July 10, 2024
Sponsor: University of Sao Paulo General Hospital
Overall Status: Active - Recruiting

Phase

N/A

Condition

Lung Injury

Respiratory Failure

Treatment

Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies

Clinical Study ID

NCT06506123
CAAE 78855824.7.0000.0068
  • Ages > 18
  • All Genders

Study 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?

Eligibility Criteria

Inclusion

Inclusion Criteria:

  • Subjects under assisted or assist-controlled mechanical ventilation and monitoredwith esophageal pressure balloon.

Exclusion

Exclusion Criteria:

  • Refusal from patient's family or attending physician

Study Design

Total Participants: 80
Treatment Group(s): 1
Primary Treatment: Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies
Phase:
Study Start date:
May 25, 2024
Estimated Completion Date:
December 24, 2025

Study 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.

Connect with a study center

  • Heart Institute, University of São Paulo

    Sao Paulo, SP 05403900
    Brazil

    Active - Recruiting

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