Clinical trial NCT06290310: Assessment of Patient-ventilator Asynchrony by Electric Impedance Tomography
Official title: Assessment of Patient-ventilator Asynchrony by Electric Impedance Tomography and Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Acute Lung Injury
Keywords: artificial intelligence
biotrauma
electric impedance tomography
patient-ventilator asynchrony
Lead sponsor: Kiskunhalas Semmelweis Hospital the Teaching Hospital of the University of Szeged
Sponsor class: OTHER_GOV
Collaborators: name: Hochschule Furtwangen University
name: Budapest University of Technology and Economics
name: Szeged University
First posted: 2024-03-04
Enrollment: 10
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: mechanically ventilated patients

Brief summary: 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.

Detailed description: 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

Eligibility criteria: Inclusion Criteria:

* any patient ventilated invasively
* any patient ventilated non-invasively

Exclusion Criteria:

* age under 18

Primary outcomes:
measure: distribution
timeFrame: during mechanical ventilation
Secondary outcomes:
measure: connecting asysnchrony cycles with electric impedance tomography measurements
timeFrame: during mechanical ventilation
measure: identifying unic electric impedance tomography signs of asynchrony
timeFrame: during mechanical ventilation
Other outcomes:
Locations:
city: Kiskunhalas
country: Hungary
facility: Kiskunhalas Semmelweis Hopsital the Teaching Hospital of the University of Szeged
Study officials:
References:
citation: Sottile PD, Albers D, Smith BJ, Moss MM. Ventilator dyssynchrony - Detection, pathophysiology, and clinical relevance: A Narrative review. Ann Thorac Med. 2020 Oct-Dec;15(4):190-198. doi: 10.4103/atm.ATM_63_20. Epub 2020 Oct 10.
citation: Bachmann MC, Morais C, Bugedo G, Bruhn A, Morales A, Borges JB, Costa E, Retamal J. Electrical impedance tomography in acute respiratory distress syndrome. Crit Care. 2018 Oct 25;22(1):263. doi: 10.1186/s13054-018-2195-6.