Clinical trial NCT05820347: Muscle Pressure Estimation With Artificial Intelligence During Mechanical Ventilation
Official title: Validation of Inspiratory Muscle Pressure Estimation and Automated Detection of Asynchronies in Patients Under Assisted Mechanical Ventilation
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Respiratory Failure
Keywords: mechanical ventilation
artificial intelligence
Lead sponsor: University of Sao Paulo General Hospital
Sponsor class: OTHER
Collaborators: name: Magnamed Tecnologia Medica S/A
First posted: 2023-04-19
Enrollment: 50
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief summary: The goal of this diagnostic study is to validate estimation of inspiratory muscle pressure by an artificial intelligence algorithm compared to the gold standard, the measure from an esophageal catheter balloon, in patients under assisted mechanical ventilation. The main questions it aims to answer are:

• Are inspiratory muscle pressure estimates from an artificial intelligence algorithm accurate when compared to the direct measure from an esophageal balloon?

Participants will be monitored with an esophageal balloon and with an artificial intelligence algorithm simultaneously, with inspiratory muscle pressure estimation during assisted mechanical ventilation with decremental levels of pressure support.

Detailed description: This is a diagnostic study to validate estimation of inspiratory muscle pressure during assisted ventilation from an artificial intelligence algorithm integrated in a mechanical ventilator (FlexiMag, Magnamed, Brazil) compared to direct measure of muscle pressure from esophageal catheter balloon (gold standard). This is a novel non-invasive method to estimate inspiratory muscle pressure.

After obtaining informed consent, participants will be monitored simultaneously with the esophageal balloon and the artificial intelligence algorithm, with decremental levels of pressure support (20 to 2 cmH2O, in steps of 20 minutes). Esophageal balloon will be removed after completing the last pressure support step.

The investigators estimated a sample of 50 participants, considering 3 cmH2O as a clinically relevant discordance between methods and 10% of missing data. Concordance analysis and correlation analysis will be performed.

Procedures will follow a specific Standard Operating Procedures and participants inclusion data will be inserted in REDCap.

Eligibility criteria: Inclusion Criteria:

* Patients under assisted or assist-control mechanical ventilation

Exclusion Criteria:

* Contraindication to esophageal catheter insertion (esophageal cancer or bleeding, esophageal fistula, skull base fracture, uncontrolled coagulopathies)
* Contraindication to transient neuromuscular blockade
* Bronchopleural fistula (persistent air leak)
* Hemodynamic instability (norepinephrine \> 1mcg/kg/min)
* Gestation
* Current sinus infection
* Refusal from patient's family of attending physician
* Palliative care

Primary outcomes:
measure: Concordance between muscle pressure amplitude (in cmH2O) estimation by artificial intelligence and esophageal balloon
timeFrame: 4 hours
measure: Correlation between muscle pressure amplitude estimation (in cmH2O) by artificial intelligence and esophageal balloon
timeFrame: 4 hours
measure: Detection of initiation time and ending time of a spontaneous breathing cycle by artificial intelligence compared with esophageal balloon
timeFrame: 4 hours
Secondary outcomes:
measure: Sensitivity and specificity of patient-ventilator asynchrony automated detection using the Artificial Intelligence Muscle Pressure estimator
timeFrame: 4 hours
Other outcomes:
Locations:
city: Sao Paulo
country: Brazil
facility: Heart Institute, University of São Paulo
state: SP
Study officials:
affiliation: University of Sao Paulo
name: Eduardo LV Costa, MD, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: University of Sao Paulo
name: Marcelo BP Amato, MD, PhD
role: STUDY_CHAIR
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