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Patterns in pharyngeal airflow associated with sleep-disordered breathing.

Domenii publicaţii > Stiinte ingineresti + Tipuri publicaţii > Articol în revistã ştiinţificã

Autori: Powell N., Mihaescu M., Mylavarapu G., Weaver M, Guilleminault C, Gutmark E.

Editorial: Elsevier, Sleep Medicine, 12(10) , p.966-974, 2011.

Rezumat:

Objective: To establish the feasibility of a noninvasive method to identify pharyngeal airflow characteristics in sleep-disordered breathing. Methods: Four patients with sleep-disordered breathing who underwent surgery or used positive airway pressure devices and four normal healthy controls were studied. Three-dimensional CT imaging and computational fluid dynamics modeling with standard steady-state numerical formulation were used to characterize pharyngeal airflow behavior in normals and pre-and post-treatment in patients. Dynamic flow simulations using an unsteady approach were performed in one patient. Results: The pre-treatment pharyngeal airway below the minimum cross-sectional area obstruction site showed airflow separation. This generated recirculation airflow regions and enhanced turbulence zones where vortices developed. This interaction induced large fluctuations in airflow variables and increased aerodynamic forces acting on the pharyngeal wall. At post-treatment, for the same volumetric flow rate, airflow field instabilities vanished and airflow characteristics improved. Mean maximum airflow velocity during inspiration reduced from 18.3 ± 5.7 m/s pre-treatment to 6.3 ± 4.5 m/s post-treatment (P = 0.002), leading to a reduction in maximum wall shear stress from 4.8 ± 1.7 Pa pre-treatment to 0.9 ± 1.0 Pa post-treatment (P = 0.01). The airway resistance improved from 4.3 ± 1.4 Pa/L/min at pre-treatment to 0.7 ± 0.7 Pa/L/min at post-treatment (P = 0.004). Post-treatment airflow characteristics were not different from normal controls (all P > 0.39). Conclusion: This study demonstrates that pharyngeal airflow variables may be derived from CT imaging and computational fluid dynamics modeling, resulting in high quality visualizations of airflow characteristics of axial velocity, static pressure, and wall shear stress in sleep-disordered breathing.

Cuvinte cheie: Computational fluid dynamics, airway, airflow, sleep apnea, pre and post-treatment

URL: http://www.ncbi.nlm.nih.gov/pubmed/22036604