This project aims to create a validated computational tool to predict surgical outcomes for pediatric patients with obstructive sleep apnea (OSA). The first line of treatment for children with OSA is to remove their tonsils and adenoids; however, these surgeries do not always cure the patient. Another treatment, continuous positive airway pressure (CPAP) is only tolerated by 50% of children. Therefore, many children undergo surgical interventions aimed at soft tissue structures surrounding the airway, such as tonsils, tongue, and soft palate, and/or the bony structures of the face. However, the success rates of these surgeries, measured as a reduction in the obstructive apnea-hypopnea index (obstructive events per hour of sleep), is surprisingly low. Therefore, there is a clear need for a tool to improve the efficacy of these surgeries and predict which of the various surgical options is going to benefit each individual patient most effectively. Computational fluid dynamics (CFD) simulations of respiratory airflow in the upper airways can provide this predictive tool, allowing the effects of various surgical options to be compared virtually and the option most likely to improve the patient's condition to be chosen. Previous CFD simulations have been unable to provide information about OSA as they were based on rigid geometries, or did not include neuromuscular motion, a key component in OSA. This project uses real-time magnetic resonance imaging (MRI) to provide the anatomy and motion of the airway to the CFD simulation, meaning that the exact in vivo motion is modeled for the first time. Furthermore, since the modeling is based on MRI, a modality which does not use ionizing radiation, it is suitable for longitudinal assessment of patients before and after surgical procedures. In vivo validation of these models will be achieved for the first time through comparison of CFD-based airflow velocity fields with those generated by phase-contrast MRI of inhaled hyperpolarized 129Xe gas. This research is based on data obtained from sleep MRIs achieved with the subject under sedation. While sedating the patient post-operatively is slightly more than minimal risk, the potential benefits to each patient outweigh this risk. As 58% of patients have persistent OSA postsurgery and the average trajectory of OSA severity is an increase over time, post-operative imaging and modeling can benefit the patient by identifying the changes to the airway made during surgery and which anatomy should be targeted in future treatments.
Pediatric obstructive sleep apnea (OSA) is a sleep-related breathing disorder characterized by upper airway obstruction. This disorder affects 2.2 million children in the US alone.1 If untreated, OSA can result in behavioral, cognitive, metabolic, and cardiovascular morbidities.2,3 Although adenotonsillectomy (T\&A) is the first-line treatment, a large percentage of children have persistent OSA after T\&A.4-11 Continuous positive airway pressure (CPAP) is generally the second-line treatment;12 however, children have a compliance rate of only 50%.13 Children with persistent OSA who are noncompliant with CPAP often undergo surgery targeting soft tissue and/or bony structures surrounding the upper airway, with success rates ranging from 17% to 72%.14-17. The investigators preliminary data shows that 58% of patients who underwent soft tissue surgery post-T\&A had persistent moderate or severe OSA after the subsequent surgery. The goal of this study is therefore to provide a predictive model that determines which post-T\&A surgical procedure is most likely to be effective in each individual surgical candidate. This goal will be achieved through patient-specific computational fluid dynamics (CFD) models of airflow and upper airway collapse in these children. Novel CFD models of OSA that uniquely incorporate airway motion derived from 3 dimensional (3D) dynamic magnetic resonance imaging (MRI) obtained synchronously with airflow measurement were developed.18,19 Clinicians currently have no method of determining the contribution of neuromuscular control and air pressure forces in causing airway collapse or determining if the resistance to airflow in one portion of the upper airway induces collapse at another portion of the airway. Patient-specific CFD can provide this information and thereby become an invaluable tool in assisting clinicians in choosing the surgical procedure that is most likely to optimize outcomes.
The overall hypothesis is that the application of novel CFD models will produce a validated approach to accurately predict the surgical option with the most successful outcome. This hypothesis will be tested by (1) validating CFD for surgical planning, (2) identifying anatomic and aerodynamic factors (eg, changes in local resistance and flow-induced pressure forces due to post surgical changes in anatomy) that determine surgical outcomes, and (3) developing a virtual surgery platform to identify patient-specific surgical procedures that will lead to successful outcomes.
