This is a proof-of-concept study to determine if a dynamic computational model can be used to predict surgical outcomes. If the results from the study are positive, they can be used to help design a larger subsequent study. The purpose of this research is to develop a computational model that simulates OSA and different surgical treatments for OSA in children and young adults with DS. Thus, the only population that will be studied is children and young adults with DS who have persistent OSA despite having previously undergone T\&A.

Obstructive sleep apnea (OSA) occurs in 50-100% of patients with Down syndrome (DS) and can significantly cause and exacerbate medical problems in these patients. Current surgical management of children with DS is imperfect. There are variable surgical success rates for both first line surgery of palatine tonsillectomy and adenoidectomy (T\&A) as well as secondary surgeries performed if and when T\&A fails. There is a critical need for a diagnostic modality that takes into account airway anatomy, tissue compliance, and collapsibility to be able to predict surgical outcome and improve surgical planning in these patients. Our central hypothesis is that upper airway flow-structure interaction (FSI) modeling using three-dimensional (3-D) computational simulations from dynamic magnetic resonance imaging (MRI or MR) data can be used to predict surgical outcomes for children with DS who have OSA despite previous T\&A. The long-term goal is to improve surgical outcome of children with Down syndrome and OSA by creating an accurate FSI predictive model. Such a diagnostic tool would help tailor surgical procedures to be more effective as well as identify and avoid unnecessary or unhelpful surgical procedures. These created models can in future be adjusted and applied to other populations with OSA. Our specific aims include: 1) In children with Down syndrome and persistent OSA despite previous T\&A, to collect data characterizing upper airway anatomy, tissue compliance, and collapsibility; 2) to generate and validate individualized dynamic FSI models for each child and 3) to use the validated dynamic computational models to predict the success of surgical treatment on children with Down syndrome who have persistent OSA despite previous T\&A. This work is innovative as it uses dynamic rather than static MR imaging data and applies a unique computational model that accurately captures the unsteadiness of the flow and accounts for the interaction between the airflow and the surrounding airway flexible structures.

Research components will involve two parts of the project. The first will be the generation, validation and use of computational models from MRI data. The second is the measure of critical closing pressure (Pcrit) on DS subjects who are at least three months post T\&A, have OSA and are being evaluated for possible additional airway surgery. The measurement of Pcrit will be done during the research PSG (in the Sleep Center) and during the clinical sleep MRI (in the MRI suite). Pcrit measurements will be acquired with the use of a Continuous Positive Air Pressure (CPAP) mask during sleep. Additionally, to measure improvement in OSA based on quality of life (QOL) and sleep, the Obstructive Sleep Apnea questionnaire (OSA18) will be administered both preoperatively and postoperatively.