Muscle Pressure Estimation With Artificial Intelligence During Mechanical Ventilation

University of Sao Paulo General Hospital

Status: Completed

Conditions: Respiratory Failure

Treatments: Device: Artificial Intelligence Estimation of Muscle Pressure during Mechanical Ventilation

Study type: Interventional

Funder types: Other

Identifiers

NCT05820347

CAAE: 59353422.9.0000.0068

Details and patient eligibility

About

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.

Full 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.

Enrollment: 50 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Diagnostic

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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50 participants in 1 patient group

Artificial Intelligence Estimation of Muscle Pressure during Mechanical Ventilation

Other group

Description:

All included subjects will be monitored simultaneously with the esophageal balloon (gold standard) and with the artificial intelligence algorithm integrated in the mechanical ventilator. Electrical Impedance Tomography will be used to monitor ventilatory patterns during different degrees of spontaneous effort. First, a single intravenous bolus of neuromuscular blockade (succinylcholine 1mg/kg or rocuronium 1.2mg/kg) will be performed to measure respiratory system mechanics (compliance and resistance). In cases where rocuronium is used, a single dose of sugammadex 4mg/kg will be administered intravenously to reverse neuromuscular blockade after measuring compliance and resistance. After initiation of spontaneous breathing effort, pressure support will be titrated from 20 cmH2O to 2 cmH2O, in decremental steps during 20 minutes each. After completing titrating of pressure support, the esophageal balloon will be removed.

Treatment:

Device: Artificial Intelligence Estimation of Muscle Pressure during Mechanical Ventilation