Automated Analysis of EIT Data for PEEP Setting

Acute Hypoxemic Respiratory Failure Clinical Trial

Official title:

Comparing the Results of a Computer Analysis Algorithm With Clinical Decisions in a Patient With Electrical Impedance Tomography Guided Ventilator Settings Regarding Optimal Positive End Expiratory Pressure and Inspiratory Pressure

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03653806
• Other study ID #: 17-4-053
• Status: Completed
• Start date: November 21, 2017
• Est. completion date: May 31, 2020

Study information

• Verified date: November 2020
• Source: Maastricht University Medical Center
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

First: to develop a computerized algorithm for automated analysis of the electrical impedance tomography (EIT) data. The algorithm calculates the "optimal" positive end-expiratory pressure (PEEP) and inspiratory pressure defined as the "optimal" balance between stretch, ventilation distribution and collapse.

Second: to compare the results of the algorithm with the current standard of care clinical judgement of an experienced ventilation practitioner.

Description:

The study will be performed at the Intensive Care Unit, Maastricht University Medical Centre. The investigators routinely apply EIT (Pulmovista, Dräger, Lübeck. Germany) in mechanically ventilated patients to optimize the ventilator settings .

An algorithm will be developed by the Institute of Technical Medicine, Furtwangen University, Germany. The algorithm will automatically detect changes in both PEEP and inspiratory pressures. For each PEEP step and/or changes in inspiratory pressure the difference in regional alveolar overdistension and alveolar collapse will be calculated. This makes it possible to select the optimal ventilator setting depending on the best compromise between alveolar overdistension and alveolar collapse.

The algorithm will be tested in 40 EIT guided mechanically ventilated patients. EIT measurements will be performed during an incremental and decremental PEEP trial. The EIT measurement will be performed in the same way as during standard clinical care. EIT data will be analysed offline by a ventilation practitioner with experience in EIT and with the newly developed algorithm. The resulting advice on optimal ventilator settings will be compared for inter-observer variability.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 40
• Est. completion date: May 31, 2020
• Est. primary completion date: May 31, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Mechanically ventilated in a volume or pressure controlled mode

• ventilator settings guided by EIT

Exclusion Criteria:

• Participants who specifically opt-out regarding the use of the data for research purpose

• Internal pacemaker, Implantable Cardioverter Defibrillator

• Skin lesions, dressings at the thorax, hindering belt placement

• Thoracic circumference < 70 cm

• Thoracic circumference > 150 cm

• BMI > 50

Related Conditions & MeSH terms

• Acute Hypoxemic Respiratory Failure
• Post-cardiac Surgery
• Respiratory Insufficiency

Intervention

Other:
• computerized algorithm for automated analysis
First: to develop a computerized algorithm for automated analysis of the electrical impedance tomography (EIT) data. The algorithm calculates the "optimal" positive end-expiratory pressure (PEEP) and inspiratory pressure defined as the "optimal" balance between stretch, ventilation distribution and collapse. Second: to compare the results of the algorithm with the current standard of care clinical judgement of experienced EIT users

Locations

Country	Name	City	State
Netherlands	Serge Heines	Maastricht

Sponsors (3)

Lead Sponsor	Collaborator
Maastricht University Medical Center	Erasmus Medical Center, University Hospital Schleswig-Holstein

Country where clinical trial is conducted

Netherlands, 

References & Publications (3)

Bodenstein M, David M, Markstaller K. Principles of electrical impedance tomography and its clinical application. Crit Care Med. 2009 Feb;37(2):713-24. doi: 10.1097/CCM.0b013e3181958d2f. Review. — View Citation

Costa EL, Borges JB, Melo A, Suarez-Sipmann F, Toufen C Jr, Bohm SH, Amato MB. Bedside estimation of recruitable alveolar collapse and hyperdistension by electrical impedance tomography. Intensive Care Med. 2009 Jun;35(6):1132-7. doi: 10.1007/s00134-009-1447-y. Epub 2009 Mar 3. — View Citation

Long Y, Liu DW, He HW, Zhao ZQ. Positive End-expiratory Pressure Titration after Alveolar Recruitment Directed by Electrical Impedance Tomography. Chin Med J (Engl). 2015 Jun 5;128(11):1421-7. doi: 10.4103/0366-6999.157626. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	develop automated EIT data algorithm for PEEP setting	The automated algorithm will give an advise on PEEP and delta pressure settings, based upon the EIT data, which is in accordance with the decision of the investigator	4 months