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NCT Number: NCT05820347

Muscle Pressure Estimation With Artificial Intelligence During Mechanical Ventilation

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.

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Key information

Conditions

Respiratory Failure Respiration Disorders Respiratory Insufficiency Respiratory Tract Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Heart Institute, University of São Paulo

São Paulo, 05403900, Brazil

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Artificial Intelligence Estimation of Muscle Pressure during Mechanical Ventilation

Device

Estimation of inspiratory muscle pressure by an artificial intelligence algorithm integrated in the mechanical ventilator (FlexiMag, Magnamed, Brazil).

Other names: Esophageal balloon measurements of Muscle Pressure, Transient Neuromuscular Blockade with Succinylcholine or Rocuronium to measure Respiratory Mechanics, Electrical Impedance Tomography Monitorization

Primary outcomes

  1. Concordance between muscle pressure amplitude (in cmH2O) estimation by artificial intelligence and esophageal balloon

    Time frame: 4 hours

    Analysis of the bias and limits of agreement (Bland-Altman plot) between muscle pressure estimated amplitude in cmH2O from artificial intelligence and measured by esophageal balloon.

  2. Correlation between muscle pressure amplitude estimation (in cmH2O) by artificial intelligence and esophageal balloon

    Time frame: 4 hours

    Correlation, reported as R-squared and a correlation plot, between amplitude in cmH2O of muscle pressure estimation by artificial intelligence and esophageal balloon.

  3. Detection of initiation time and ending time of a spontaneous breathing cycle by artificial intelligence compared with esophageal balloon

    Time frame: 4 hours

    Time difference (in ms) between initiation of a spontaneous breathing cycle and ending of a spontaneous breathing cycle between artificial intelligence and esophageal balloon.

Secondary outcomes

  1. Sensitivity and specificity of patient-ventilator asynchrony automated detection using the Artificial Intelligence Muscle Pressure estimator

    Time frame: 4 hours

    Number of patient-ventilator asynchronies detected using artificial intelligence compared with number of asynchronies detected by experts assessing airway pressure, flow and esophageal balloon waveforms.

Sponsors and collaborators

Lead sponsor

University of Sao Paulo General Hospital

Other

Collaborators

  • Magnamed Tecnologia Medica S/A

Registry information

Official study title

Validation of Inspiratory Muscle Pressure Estimation and Automated Detection of Asynchronies in Patients Under Assisted Mechanical Ventilation

Important dates

Study start
2022
Primary completion
2023
Study completion
2023
First posted
Apr 19, 2023
Registry last updated
Sep 6, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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