Impact of Positive End-Expiratory Pressure During Heterogeneous Lung Injury: Insights from Computed Tomographic Image Functional Modeling View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2008-06

AUTHORS

C. L. Bellardine Black, A. M. Hoffman, L. W. Tsai, E. P. Ingenito, B. Suki, D. W. Kaczka, B. A. Simon, K. R. Lutchen

ABSTRACT

Image Functional Modeling (IFM) synthesizes three dimensional airway networks with imaging and mechanics data to relate structure to function. The goal of this study was to advance IFM to establish a method of exploring how heterogeneous alveolar flooding and collapse during lung injury would impact regional respiratory mechanics and flow distributions within the lung at distinct positive end-expiratory pressure (PEEP) levels. We estimated regional respiratory system elastance from computed tomography (CT) scans taken in 5 saline-lavaged sheep at PEEP levels from 7.5 to 20 cmH(2)O. These data were anatomically mapped into a computational sheep lung model, which was used to predict the corresponding impact of PEEP on dynamic flow distribution. Under pre-injury conditions and during lung injury, respiratory system elastance was determined to be spatially heterogeneous and the values were distributed with a hyperbolic distribution in the range of measured values. Increases in PEEP appear to modulate the heterogeneity of the flow distribution throughout the injured lung. Moderate increases in PEEP decreased the heterogeneity of elastance and predicted flow distribution, although heterogeneity began to increase for PEEP levels above 12.5-15 cmH(2)O. By combining regional respiratory system elastance estimated from CT with our computational lung model, we can potentially predict the dynamic distribution of the tidal volume during mechanical ventilation and thus identify specific areas of the lung at risk of being overdistended. More... »

PAGES

980-991

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10439-008-9451-x

DOI

http://dx.doi.org/10.1007/s10439-008-9451-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023221323

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18340535


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