Automated Analysis of EIT Data for PEEP Setting

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

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.

Study Overview

• Status: Completed
• Conditions: Acute Hypoxemic Respiratory Failure; Post-cardiac Surgery
• Intervention / Treatment: Other: computerized algorithm for automated analysis

Detailed 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.

• Study Type: Observational
• Enrollment (Actual): 40

Contacts and Locations

Study Locations

• Netherlands
  • Maastricht, Netherlands, 622HX
    • Serge Heines

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Post cardiac- surgery and patients with acute hypoxic respiratory failure

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Maastricht University Medical Center
• Collaborators: Erasmus Medical Center; University Hospital Schleswig-Holstein
• Investigators: Study Chair: Dennis Bergmans, Maastricht University Medical Center

Publications and helpful links

General Publications

• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): November 21, 2017
• Primary Completion (Actual): May 31, 2020
• Study Completion (Actual): May 31, 2020

Study Registration Dates

• First Submitted: August 17, 2018
• First Submitted That Met QC Criteria: August 28, 2018
• First Posted (Actual): August 31, 2018

Study Record Updates

• Last Update Posted (Actual): November 5, 2020
• Last Update Submitted That Met QC Criteria: November 4, 2020
• Last Verified: November 1, 2020

More Information

Terms related to this study

• Keywords: PEEP; Electrical Impedance Tomography
• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Respiration Disorders; Respiratory Insufficiency
• Other Study ID Numbers: 17-4-053

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No