Computational fluid dynamics for the assessment of upper airway response to oral appliance treatment in obstructive sleep apnea Moyin Zhao a , Tracie Barber a , Peter Cistulli b,c , Kate Sutherland b,c , Gary Rosengarten a,d,n a School of Mechanical Engineering, University of New South Wales, Sydney, New South Wales 2032, Australia b NHMRC Centre for Integrated Research and Understanding of Sleep (CIRUS), The University of Sydney, New South Wales, Australia c Centre for Sleep Health and Research, Department of Respiratory Medicine, Royal North Shore Hospital, St Leonards, New South Wales, Australia d School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Victoria 3053, Australia article info Article history: Accepted 26 October 2012 Keywords: OSA Upper airway MAS MRI CFD Pressure drop abstract Mandibular advancement splints (MAS), which protrude the lower jaw during sleep, are recognized as an effective treatment for obstructive sleep apnea (OSA) through their action of enlarging the airway space and preventing upper airway collapse. However a clinical challenge remains in preselecting patients who will respond to this form of therapy. We aimed to use computational fluid dynamics (CFD) in conjunction with patient upper airway scans to understand the upper airway response to treatment. Seven OSA patients were selected based on their varied treatment response (assessed by the apnea– hypopnoea index (AHI) on overnight polysomnography). Anatomically-accurate upper airway compu- tational models were reconstructed from magnetic resonance images with and without MAS. CFD simulations of airflow were performed at the maximum flow rate during inspiration. A physical airway model of one patient was fabricated and the CFD method was validated against the pressure profile on the physical model. The CFD analysis clearly demonstrated effects of MAS treatment on the patient’s UA airflow patterns. The CFD results indicated the lowest pressure often occurs close to the soft palate and the base of the tongue. Percentage change in the square root of airway pressure gradient with MAS (D ffiffiffiffiffiffiffiffiffiffiffiffiffi DP Max p %) was found to have the strongest relationship with treatment response (DAHI%) in correlation analysis (r ¼0.976, p ¼0.000167). Changes in upper airway geometry alone did not significantly correlate with treatment response. We provide further support of CFD as a potential tool for prediction of treatment outcome with MAS in OSA patients without requiring patient specific flow rates. & 2012 Elsevier Ltd. All rights reserved. 1. Introduction Obstructive sleep apnea (OSA) is a common disorder charac- terized by repetitive episodes of complete (apnea) or partial (hypopnea) collapse of the upper airway during sleep, resulting in sleep disturbance and oxygen desaturation (American Academy of Sleep Medicine Task Force, 1999). OSA severity is defined by the apnea–hypopnea index (AHI), the total number of apneas and hypopnoeas per hour of sleep (Ferguson et al., 2006). OSA sequelae include excessive day time sleepiness, cardiovascular and cerebral vascular diseases (Roux et al., 2000). Standard treatment is continuous positive airway pressure (CPAP) applied via a mask interface during sleep, which pneumatically splints the upper airway, preventing collapse. An alternative approach is mandibular advancement splint (MAS) treatment (Cistulli et al., 2004), which uses custom-made dental devices that hold the lower jaw in a protruded position. MAS stiffens upper airway tissues and reduces airway collapse, likely mediated through an increase in pharyngeal area predominantly in the lateral dimension (Ng et al., 2003; Chan et al., 2007). MAS treatment is often preferred by patients due to its simplicity of use and portability, which often leads to better treatment adherence (Ng et al., 2003). While 60–70% of patients achieve clinical benefit, a complete treatment success (AHI o5 after treatment) is only achieved in approximately 35–40% (Chan et al., 2007). Therefore treatment responses vary and pre- identifying which patients will respond to MAS therapy is currently not possible. Particular characteristics of OSA patients, OSA severity, obesity and craniofacial structure, have been asso- ciated with MAS treatment outcome, however such predictors have not been conclusively validated (Ferguson et al., 2006). Prediction of individual treatment outcome remains an elusive goal due to incomplete understanding of the mechanisms of MAS treatment (De Backer et al., 2007). Contents lists available at SciVerse ScienceDirect journal homepage: www.elsevier.com/locate/jbiomech www.JBiomech.com Journal of Biomechanics 0021-9290/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jbiomech.2012.10.033 n Corresponding author. Tel.: þ61 3 99258020. E-mail address: gary.rosengarten@rmit.edu.au (G. Rosengarten). Journal of Biomechanics 46 (2013) 142–150