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NCT06290310 Clinical Trial

Assessment of Patient-ventilator Asynchrony by Electric Impedance Tomography

Acute Lung Injury Clinical Trial

— PAVELA  

Official title:
Assessment of Patient-ventilator Asynchrony by Electric Impedance Tomography and Artificial Intelligence

Clinical Trial Details — Status: Not yet recruiting

Administrative data
NCT number NCT06290310
Other study ID # PAVELA
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date April 12, 2024
Est. completion date September 1, 2024

Study information
Verified date March 2024
Source Kiskunhalas Semmelweis Hospital the Teaching Hospital of the University of Szeged
Contact András Lovas, M.D. Ph.D.
Phone 003677522000
Email landras@halasi-korhaz.hu
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.

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

Recruitment information / eligibility
Status Not yet recruiting
Enrollment 10
Est. completion date September 1, 2024
Est. primary completion date September 1, 2024
Accepts healthy volunteers
Gender All
Age group 18 Years to 100 Years
Eligibility Inclusion Criteria: - any patient ventilated invasively - any patient ventilated non-invasively Exclusion Criteria: - age under 18

Study Design

Related Conditions & MeSH terms

Intervention

Device:
• EIT
continuous electric impedance tomography measurement
• patient-ventilator asynchrony assessment
patient-ventilator asynchrony assessment by flow/time curve and machine learning

Locations
Country Name City State
Hungary Kiskunhalas Semmelweis Hopsital the Teaching Hospital of the University of Szeged Kiskunhalas

Sponsors (4)
Lead Sponsor Collaborator
Kiskunhalas Semmelweis Hospital the Teaching Hospital of the University of Szeged Budapest University of Technology and Economics, Hochschule Furtwangen University, Szeged University

Country where clinical trial is conducted

Hungary, 

References & Publications (2)

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. — View 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. — View Citation

Outcome
Type Measure Description Time frame Safety issue
Primary distribution gas distribution in lungs assessed by electric impedance tomography during mechanical ventilation
Secondary 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 during mechanical ventilation
Secondary 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 during mechanical ventilation
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