Patient-Ventilator Dyssynchrony Detection With a Machine Learning Algorithm

University of Sao Paulo General Hospital

Status: Enrolling

Conditions: Respiratory Failure

Treatments: Device: Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies

Study type: Observational

Funder types: Other

Identifiers

NCT06506123

CAAE 78855824.7.0000.0068

Details and patient eligibility

About

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?

Full 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.

Enrollment: 80 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

80 participants in 1 patient group

Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies

Description:

This is a single arm study, since all subjects included will be exposed to both diagnostic methods (artificial intelligence and experts). The proposed diagnostic method is a machine learning algorithm integrated in the mechanical ventilator FlexiMag Max 700 (Magnamed, Brazil), which will continuously record data from mechanical ventilation of included subjects for a time period of up to 72 hours. The gold-standard involves esophageal pressure waveform recording and offline analysis by experts.

Treatment:

Device: Artificial Intelligence Detection and Classification of Patient-Ventilator Dyssynchronies