Respiratory Failure
Conditions
Keywords
mechanical ventilation, artificial intelligence
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.
Interventions
DEVICEArtificial 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).
Sponsors
Magnamed Tecnologia Medica S/A
University of Sao Paulo General Hospital
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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 |
Countries
Brazil
Contacts
Primary ContactGlauco M Plens, MD
Outcome results
None listed