Patient-ventilator asynchrony (PVA) has deleterious effects on the lungs. PVA can lead to acute lung injury and worsening hypoxemia through biotrauma. Little is known about how PVA affects lung aeration estimated by electric impedance tomography (EIT). Artificial intelligence can promote the detection of PVA and with its help, EIT measurements can be correlated to asynchrony.
Patient-ventilator asynchrony (PVA) is a common phenomenon with invasively- and non-invasively ventilated patients. PVA has deleterious effects on the lungs. It causes not just patient discomfort and distress but also leads to acute lung injury and worsening hypoxemia through biotrauma. The latter significantly impacts outcomes and increases the duration of mechanical ventilation and intensive care unit stay. However, PVA is a widely investigated incident related to mechanical ventilation, though little is known about how it affects lung aeration estimated by electric impedance tomography (EIT). EIT is a non-invasive, real-time monitoring technique suitable for detecting changes in lung volumes during ventilation. Artificial intelligence can promote the detection of PVA by flow versus time assessment. If continuous EIT recording is correlated with the latter, impedance tomography changes evoked by asynchrony can be estimated
Study Type
OBSERVATIONAL
Enrollment
10
continuous electric impedance tomography measurement
patient-ventilator asynchrony assessment by flow/time curve and machine learning
Kiskunhalas Semmelweis Hopsital the Teaching Hospital of the University of Szeged
Kiskunhalas, Hungary
distribution
gas distribution in lungs assessed by electric impedance tomography
Time frame: during mechanical ventilation
connecting asysnchrony cycles with electric impedance tomography measurements
connecting machine learning assessed patient-ventilator asynchrony respiratory cycles with the inherent respiratory cycle recorded by the electric impedance tomography
Time frame: during mechanical ventilation
identifying unic electric impedance tomography signs of asynchrony
following connection described under "outcome 2", identification if single patient-ventilator asynchrony types (delayed cycling, premature cycling, auto trigger, ineffective effort, double trigger) present specific electric impedance tomography changes
Time frame: during mechanical ventilation
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