NCT06290310

Assessment of Patient-ventilator Asynchrony by Electric Impedance Tomography

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 10 (estimated)
• Sponsor: Kiskunhalas Semmelweis Hospital the Teaching Hospital of the University of Szeged · Other Government
• Sex: All
• Age: 18 Years – 100 Years
• Healthy volunteers: —

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

Conditions

• Acute Lung Injury

Interventions

Type	Name	Description
DEVICE	EIT	continuous electric impedance tomography measurement
DEVICE	patient-ventilator asynchrony assessment	patient-ventilator asynchrony assessment by flow/time curve and machine learning

Timeline

• Start date: 2024-04-12
• Primary completion: 2024-09-01
• Completion: 2024-09-01
• First posted: 2024-03-04
• Last updated: 2024-03-04

Locations

1 site across 1 country: Hungary