Validation of Inspiratory Muscle Pressure Estimation and Automated Detection of Asynchronies in Patients Under Assisted Mechanical Ventilation
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Respiratory Failure
- Sponsor
- University of Sao Paulo General Hospital
- Enrollment
- 50
- Locations
- 1
- Primary Endpoint
- Correlation between muscle pressure amplitude estimation (in cmH2O) by artificial intelligence and esophageal balloon
- Status
- Completed
- Last Updated
- 2 years ago
Overview
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.
Investigators
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
Outcomes
Primary Outcomes
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.
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.
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
- Sensitivity and specificity of patient-ventilator asynchrony automated detection using the Artificial Intelligence Muscle Pressure estimator(4 hours)