Functional Regions of Interest in Electrical Impedance Tomography: A Secondary Analysis of Two Clinical Studies

• Published in: PloS one Vol. 11; no. 3; p. e0152267
• Main Authors: Becher, Tobias, Vogt, Barbara, Kott, Matthias, Schädler, Dirk, Weiler, Norbert, Frerichs, Inéz
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.03.2016; Public Library of Science (PLoS)
• Subjects: Adult respiratory distress syndrome; Anesthesiology; Biology and Life Sciences; Case-Control Studies; Clinical trials; Coefficient of variation; Confidence intervals; Data processing; Diagnosis; Electric Impedance; Electric properties; Electrical impedance; Engineering and Technology; Feasibility studies; Humans; Image processing; Impedance; Inhomogeneity; Intensive care; Low flow; Lungs; Mechanical ventilation; Medical imaging; Medicine; Medicine and Health Sciences; Methods; Morbidity; Patient outcomes; Patients; Physical Sciences; Physiological aspects; Pixels; Research and Analysis Methods; Respiratory distress syndrome; Respiratory therapy; Retrospective Studies; ROC Curve; Secondary analysis; Tidal energy; Tidal power; Tomography; Tomography - methods; Ventilation; Ventilators; United Kingdom; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0152267

Patients with acute respiratory distress syndrome (ARDS) typically show a high degree of ventilation inhomogeneity, which is associated with morbidity and unfavorable outcomes. Electrical impedance tomography (EIT) is able to detect ventilation inhomogeneity, but it is unclear which method for defining the region of interest (ROI) should be used for this purpose. The aim of our study was to compare the functional region of interest (fROI) method to both the lung area estimation method (LAEM) and no ROI when analysing global parameters of ventilation inhomogeneity. We assumed that a good method for ROI determination would lead to a high discriminatory power for ventilation inhomogeneity, as defined by the area under the receiver operating characteristics curve (AUC), comparing patients suffering from ARDS and control patients without pulmonary pathologies. We retrospectively analysed EIT data from 24 ARDS patients and 12 control patients without pulmonary pathology. In all patients, a standardized low-flow-pressure volume maneuver had been performed and was used for EIT image generation. We compared the AUC for global inhomogeneity (GI) index and coefficient of variation (CV) between ARDS and control patients using all EIT image pixels, the fROI method and the LAEM for ROI determination. When analysing all EIT image pixels, we found an acceptable AUC both for the GI index (AUC = 0.76; 95% confidence interval (CI) 0.58-0.94) and the CV (AUC = 0.74; 95% CI 0.55-0.92). With the fROI method, we found a deteriorating AUC with increasing threshold criteria. With the LAEM, we found the best AUC both for the GI index (AUC = 0.89; 95% CI 0.78-1.0) and the CV (AUC = 0.89; 95% CI 0.78-1.0) using a threshold criterion of 50% of the maximum tidal impedance change. In the assessment of ventilation inhomogeneity with EIT, functional regions of interest obscure the difference between patients with ARDS and control patients without pulmonary pathologies. The LAEM is preferable to the fROI method when assessing ventilation inhomogeneity.