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

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.