Improving Outcomes in Pediatric Obstructive Sleep Apnea With Computational Fluid Dynamics
Status:
Recruiting
Trial end date:
2024-08-15
Target enrollment:
Participant gender:
Summary
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 is surprisingly low.
Therefore, there a need for a tool to improve the efficacy and predict which surgical option
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.