Effect of Nasal Obstruction on Continuous Positive Airway Pressure Treatment: Computational Fluid Dynamics Analyses

Objective Nasal obstruction is a common problem in continuous positive airway pressure (CPAP) therapy for obstructive sleep apnea and limits treatment compliance. The purpose of this study is to model the effects of nasal obstruction on airflow parameters under CPAP using computational fluid dynamics (CFD), and to clarify quantitatively the relation between airflow velocity and pressure loss coefficient in subjects with and without nasal obstruction. Methods We conducted an observational cross-sectional study of 16 Japanese adult subjects, of whom 9 had nasal obstruction and 7 did not (control group). Three-dimensional reconstructed models of the nasal cavity and nasopharynx with a CPAP mask fitted to the nostrils were created from each subject’s CT scans. The digital models were meshed with tetrahedral cells and stereolithography formats were created. CPAP airflow simulations were conducted using CFD software. Airflow streamlines and velocity contours in the nasal cavities and nasopharynx were compared between groups. Simulation models were confirmed to agree with actual measurements of nasal flow rate and with pressure and flow rate in the CPAP machine. Results Under 10 cmH2O CPAP, average maximum airflow velocity during inspiration was 17.6 ± 5.6 m/s in the nasal obstruction group but only 11.8 ± 1.4 m/s in the control group. The average pressure drop in the nasopharynx relative to inlet static pressure was 2.44 ± 1.41 cmH2O in the nasal obstruction group but only 1.17 ± 0.29 cmH2O in the control group. The nasal obstruction and control groups were clearly separated by a velocity threshold of 13.5 m/s, and pressure loss coefficient threshold of approximately 10.0. In contrast, there was no significant difference in expiratory pressure in the nasopharynx between the groups. Conclusion This is the first CFD analysis of the effect of nasal obstruction on CPAP treatment. A strong correlation between the inspiratory pressure loss coefficient and maximum airflow velocity was found.

[1]  H. Lee,et al.  Assessment of septal deviation effects on nasal air flow: A computational fluid dynamics model , 2009, The Laryngoscope.

[2]  T. Furukawa,et al.  Relationship between Oral Flow Patterns, Nasal Obstruction, and Respiratory Events during Sleep. , 2015, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[3]  Max Hirshkowitz,et al.  Practice parameters for the use of continuous and bilevel positive airway pressure devices to treat adult patients with sleep-related breathing disorders. , 2006, Sleep.

[4]  Julien Cisonni,et al.  Effect of the velopharynx on intraluminal pressures in reconstructed pharynges derived from individuals with and without sleep apnea. , 2013, Journal of biomechanics.

[5]  David Wexler,et al.  Aerodynamic effects of inferior turbinate reduction: computational fluid dynamics simulation. , 2005, Archives of otolaryngology--head & neck surgery.

[6]  R. Grunstein,et al.  Chapter 107 – Positive Airway Pressure Treatment for Obstructive Sleep Apnea–Hypopnea Syndrome , 2011 .

[7]  Guilherme J M Garcia,et al.  Atrophic rhinitis: a CFD study of air conditioning in the nasal cavity. , 2007, Journal of applied physiology.

[8]  K. Hörmann,et al.  Arousal responses to olfactory or trigeminal stimulation during sleep. , 2007, Sleep.

[9]  P. Dalton,et al.  Numerical Modeling of Nasal Obstruction and Endoscopic Surgical Intervention: Outcome to Airflow and Olfaction , 2006, American journal of rhinology.

[10]  M. Kryger,et al.  Principles and Practice of Sleep Medicine , 1989 .

[11]  Guilherme J M Garcia,et al.  Toward personalized nasal surgery using computational fluid dynamics. , 2011, Archives of facial plastic surgery.

[12]  Richard B Berry,et al.  Positive airway pressure treatment for obstructive sleep apnea. , 2007, Chest.

[13]  Heow Pueh Lee,et al.  Changes of Airflow Pattern in Inferior Turbinate Hypertrophy: A Computational Fluid Dynamics Model , 2009, American journal of rhinology & allergy.

[14]  Tracie Barber,et al.  Computational fluid dynamics for the assessment of upper airway response to oral appliance treatment in obstructive sleep apnea. , 2013, Journal of biomechanics.

[15]  H. Okuda,et al.  Evaluations of Steady and Unsteady Blood Vessel Wall Stresses of an Artery with a Cerebral Aneurysm , 2013 .

[16]  Raanan Arens,et al.  Noninvasive estimation of pharyngeal airway resistance and compliance in children based on volume-gated dynamic MRI and computational fluid dynamics. , 2011, Journal of applied physiology.

[17]  K. Hörmann,et al.  Chemosensory event-related potentials during sleep—A pilot study , 2006, Neuroscience Letters.