Respiratory Failure
Conditions
Keywords
mechanical ventilation, artificial intelligence
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.
Interventions
DEVICEArtificial Intelligence Estimation of Muscle Pressure during Mechanical Ventilation
Estimation of inspiratory muscle pressure by an artificial intelligence algorithm integrated in the mechanical ventilator (FlexiMag, Magnamed, Brazil).
Sponsors
Magnamed Tecnologia Medica S/A
University of Sao Paulo General Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Concordance between muscle pressure amplitude (in cmH2O) estimation by artificial intelligence and esophageal balloon | 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. |
| Correlation between muscle pressure amplitude estimation (in cmH2O) by artificial intelligence and esophageal balloon | 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. |
| Detection of initiation time and ending time of a spontaneous breathing cycle by artificial intelligence compared with esophageal balloon | 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
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity and specificity of patient-ventilator asynchrony automated detection using the Artificial Intelligence Muscle Pressure estimator | 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. |
Countries
Brazil
Outcome results
None listed