Improving Outcomes in Pediatric Obstructive Sleep Apnea (OSA) with Computational Fluid Dynamics
Children with complex obstructive sleep apnea (OSA) often undergo surgeries aimed at treating their airway collapse. We aim to improve the outcome of these surgeries be matching each patient to the surgical approach which is best suited to that specific patient. We analyze each patient’s airway anatomy, airway collapse, and airflow to understand why their airway collapses and how the muscles controlling structures like the tongue play a part by moving during breathing.
OSA is a common condition, affecting 2.2 million children in the USA alone. It is characterized as upper airway obstruction during sleep, which causes disrupted sleep and leads to developmental delay, cardiovascular complications, and impaired growth. Current surgical approaches to treat OSA do not always “cure” the patient, therefore the goal of this project is to create a computational tool to predict which surgical approach will provide the most successful outcome for each patient.
The goals of this project are to validate our computational model of the airway using phase contrast MRI of hyperpolarized xenon gas, identify characteristics of patients with OSA who benefit from different types of surgery, and to develop a predictive model of which treatments will work best for each patient.