Computational Fluid Dynamics Study on the Effects of Ambient Air Temperature on Obstructive Sleep Apnea

Computational fluid dynamics (CFD) analysis may quantify the severity of anatomical airway restriction in obstructive sleep apnea syndrome (OSAS) better than anatomical measurements alone. However, optimal CFD model endpoints to characterize or assess OSAS have not been determined. To model upper airway fluid dynamics using CFD and investigate the strength of correlation between various CFD endpoints, anatomical endpoints, and OSAS severity, in obese children with OSAS and controls. CFD models derived from magnetic resonance images were solved at subject-specific peak tidal inspiratory flow; pressure at the choanae was set by nasal resistance. Model endpoints included airway wall minimum pressure (Pmin), flow resistance in the pharynx (Rpharynx), and pressure drop from choanae to a minimum cross section where tonsils and adenoids constrict the pharynx ( dP TAmax). Significance of endpoints was analyzed using paired comparisons ( t-test or Wilcoxon signed rank test) and Spearman correlation. Fifteen subject pairs were analyzed. Rpharynx and dP TAmax were higher in OSAS than control and most significantly correlated to obstructive apnea-hypopnea index (oAHI), r = 0.48 and r = 0.49, respectively ( P < 0.01). Airway minimum cross-sectional correlation to oAHI was weaker ( r = −0.39); Pmin was not significantly correlated. CFD model endpoints based on pressure drops in the pharynx were more closely associated with the presence and severity of OSAS than pressures including nasal resistance, or anatomical endpoints. This study supports the usefulness of CFD to characterize anatomical restriction of the pharynx and as an additional tool to evaluate subjects with OSAS.

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Predicting the Treatment Response of Oral Appliances for Obstructive Sleep Apnea Using Computational Fluid Dynamics and Fluid-Structure Interaction Simulations

Volume 3A: Biomedical and Biotechnology Engineering ◽

10.1115/imece2013-62904 ◽

2013 ◽

Cited By ~ 3

Author(s):

Moyin Zhao ◽

Tracie Barber ◽

Peter Cistulli ◽

Kate Sutherland ◽

Gary Rosengarten

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Pressure Drop ◽

Treatment Response ◽

Fluid Structure Interaction ◽

Maximum Pressure ◽

Structure Interaction ◽

Obstructive Sleep

In this study we used computational fluid dynamics (CFD) to analyze the therapeutic effect of an oral device (mandibular advancement splint – MAS, that protrudes the lower jaw during sleep) as a treatment for Obstructive Sleep Apnea (OSA). Anatomically-accurate upper airway (UA) computational models were reconstructed from magnetic resonance images (MRI) of 7 patients with and without a MAS device fitted. CFD simulations of UA airflow were performed at the maximum flow rate during inspiration. The CFD results indicated the lowest pressure often occurs close to the soft palate and the base of the tongue. The airway pressure gradient was estimated as the best indicator for treatment response since the change in the pressure drop forms a linear correlation with the change in patients’ Apnea-Hypopnea Index (AHI). This correlation has the potential to be developed into a model for predicting the outcome of the MAS treatment. However the rigid wall assumption of CFD models is the major uncertainty. To overcome this uncertainty we set up a full fluid-structure interaction model for a typical responder case with a compliant UA wall. The results demonstrated the different UA flow field associated with using MAS alleviated the airway collapse, which was successfully predicted for the untreated patient. We thus show for the first time that FSI is more accurate than CFD with rigid walls for the study of OSA, and can predict treatment response. Comparison of the FSI and CFD results for the UA flow and pressure profiles showed variation between the models. The structural deflection in oropharynx effectively reformed the flow pattern, however, the maximum pressure drops of both results were close. This supports the competence of the CFD method in clinical applications, where maximum pressure drop data can be used to develop a treatment-predicting model.

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