Abstract

The inspired sinewave technique (IST) is a non-invasive method to measure lung heterogeneity indices (including both uneven ventilation and perfusion or heterogeneity), which reveal multiple conditions of the lung and lung injury. To evaluate the reproducibility and predicted clinical outcomes of IST heterogeneity values, a comparison with a quantitative lung computed tomography (CT) scan is performed. Six anaesthetised pigs were studied after surfactant depletion by saline-lavage. Paired measurements of lung heterogeneity were then taken with both the IST and CT. Lung heterogeneity measured by the IST was calculated by (a) the ratio of tracer gas outputs measured at oscillation periods of 180 s and 60 s, and (b) by the standard deviation of the modelled log-normal distribution of ventilations and perfusions in the simulation lung. In the CT images, lungs were manually segmented and divided into different regions according to voxel density. A quantitative CT method to calculate the heterogeneity (the Cressoni method) was applied. The IST and CT show good Pearson correlation coefficients in lung heterogeneity measurements (ventilation: 0.71, and perfusion, 0.60, p < 0.001). Within individual animals, the coefficients of determination average ventilation (R2 = 0.53) and perfusion (R2 = 0.68) heterogeneity. Strong concordance rates of 98% in ventilation and 89% when the heterogeneity changes were reported in pairs measured by CT scanning and IST methods. This quantitative method to identify heterogeneity has the potential to replicate CT lung heterogeneity, and to aid individualised care in ARDS.

Details

Title
Quantifying heterogeneity in an animal model of acute respiratory distress syndrome, a comparison of inspired sinewave technique to computed tomography
Author
Tran, Minh C. 1 ; Crockett, Douglas C. 1 ; Tran, Tu K. 2 ; Phan, Phi A. 1 ; Federico, Formenti 3 ; Bruce, Richard 4 ; Perchiazzi, Gaetano 5 ; Payne, Stephen J. 6 ; Farmery, Andrew D. 1 

 University of Oxford, Nuffield Division of Anesthetics, Nuffield Department of Clinical Neurosciences, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948) 
 University of Oxford, Nuffield Division of Anesthetics, Nuffield Department of Clinical Neurosciences, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948); University of Oxford, Department of Engineering and Science, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948) 
 University of Oxford, Nuffield Division of Anesthetics, Nuffield Department of Clinical Neurosciences, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948); King’s College London, Centre for Human and Applied Physiology, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764); The University of Nebraska Omaha, Department of Biomechanics, Omaha, USA (GRID:grid.266815.e) (ISNI:0000 0001 0775 5412) 
 King’s College London, Centre for Human and Applied Physiology, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764) 
 Uppsala University, Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala, Sweden (GRID:grid.8993.b) (ISNI:0000 0004 1936 9457) 
 University of Oxford, Department of Engineering and Science, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948) 
Pages
4897
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2932706833
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.