Overview
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?
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.
Eligibility
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