Effect of inhaled gas density on the pendelluft-induced lung injury
Introduction
Pendelluft flow has long been documented as one of the crucial mechanisms for gas exchange augmentation during high frequency oscillatory ventilation (HFOV) (Alzahrany et al., 2014a, Chang, 1984, Ultman et al., 1988). The asymmetric resistance (R) and compliance (C) of the adjacent lung units causes a variation in time constant () (Otis et al., 1956) that causes a phase-shift in the ventilation cycle. As a result, an interregional gas exchange known as pendelluft occurs between neighboring lung units that are out-of-phase at the end of inspiration/expiration cycles (Chang, 1984). Several studies investigated pendelluft characteristics under different frequencies and tidal volume during HFOV (Alzahrany et al., 2014b, Elad et al., 1998, High et al., 1991). The majority of the earlier studies were simple in geometrical and mathematical representations. Otis et al. (1956) applied a theoretical analysis of lung ventilation under variation of the time constant in a single idealized bifurcation. A linear model consisting of airways’ resistance and compliance was proposed to capture pendelluft in small airways for nonuniform daughter branches resistances. Later, Ultman et al. (1988) extended Otis׳s theoretical model to include a non-uniform compliance and inertance (I) that accounts for the gas acceleration in large airways. The importance of the new inclusions was affirmed by the captured pendelluft in larger airways. A similar approach was later adopted (High et al., 1991) to explore optimum physiological transport under HFOV therapy. Lee et al. (2006) generated a pendelluft experimentally in a micro channel; and later Hirahara et al. (2011) implemented lumped-parameter and direct resistance and compliance in small airways model. Hitherto, the geometry models that have been used were either single or double simplified bifurcations. Recently, Alzahrany et al. (2014b) have utilized instantaneous pressure outlet boundary conditions based on the airways resistance–compliance (R–C) that was applied at outlets of a 3D Image-Based large-scale tracheobronchial model. The pendelluft flow was successfully captured at different regions in the whole geometry; these were in agreement with the in vitro physiological data obtained by (Andrey and Murphy, 2012).
HFOV is meant to replace conventional mechanical ventilation (CMV) to reduce the risk of lung injury associated with CMV while at the same time preserving adequate gas exchange. However, recent observations have suggested a potential of lung injury due to pendelluft (Alzahrany and Banerjee, 2015b, Hupp et al., 2015, Stawicki et al., 2009, Yoshida et al., 2013). For instance, Yoshida et al. (2013) evaluated in vitro pendelluft flow in anesthetized pigs and reported that lung injury was detected due to pendelluft during protective-lung ventilation with small volume. This conclusion was recently confirmed using a biomechanical model by the authors (Alzahrany and Banerjee, 2015b) by utilizing R–C boundary conditions (Alzahrany et al., 2014b) where a potential for lung injury was identified as a consequence of the gas entrapment and shear stress surrounding the carinas added by the existence of pendelluft. Physicians typically use cocktails of various gas mixtures during ventilation therapy. These gases have a wide range of density and dynamic viscosity, which alter the flow dynamics and consequently affect the ventilation process. In the present work, we have investigated the effect of changing the gas cocktail on lung injury mechanics related to PILI. Two non-reactive gases were utilized Helium (He) and sulfur hexafluoride–oxygen mixture (SF6–O2); the results were compared to our previous work that used air as a ventilation medium (Alzahrany and Banerjee, 2015a). Large eddy simulation (LES) along with R–C boundary conditions were implemented to model the gas transport and pendelluft in a patient-specific lung model. The current study aims to improve our understanding of HFOV mechanics and lead to a pathway towards improving lung-protective ventilation therapy.
Section snippets
Numerical method and experimental validation
A set of Computed-Tomography (CT) scans for a 60-year-old female patient was used to reconstruct the upper tracheobronchial tree. The segmented geometry encompasses seven generations beginning from the trachea (Generation: G0) up to G7 and comprising of 53 small, peripheral outlets. To account for the intubation effects during the artificial ventilation management, an endotracheal tube (ETT) with an inner diameter of 8 mm was inserted into the trachea. Fig. 1 shows the whole geometry considered
Flow–volume and volume–pressure loops
The gas volume delivered and expelled over the course of the ventilation cycle was plotted in Fig. 2a–d for the three inhaled gases. The data was obtained from two representative units: unit 2 at the right upper lobe that branches into outlet 3 and 4, and unit 26 at the left lower lobe that branches into outlets 44 and 45 (Fig. 1). For clarity, the inset plots show the curves’ behavior at the end of the cycle for the cases of using air and He. The volume curves showed large discrepancies among
Conclusion
The role of inspired gas density in pendelluft-induced lung injury was computationally investigated. LES along with physiological outlet boundary condition was implemented. The flow–volume and volume–pressure loops were used to characterize the effects of the gas density on the ventilation process under HFOV. The inhaled gas density has proven to impact the flow characteristics and ventilation complications strongly. In contrast to the heavier inhaled gas, the use of lower gas density resulted
Conflict of interest
The authors of this manuscript have nothing to disclose that would bias our work.
Competing interests
None.
Funding
None.
Ethical approval
Exempt from IRB review and approval in accordance with US Federal Regulations 45 CFR Part 46.101(B) by Lehigh University and UMKC-IRB.
Acknowledgement
The authors would like to thank Dr. Gary Salzman, M.D. at the University of Missouri, Kansas City for providing us with the CT-scans that were used to generate the lung model.
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