Dynamic Computational Modeling of Obstructive Sleep Apnea in Down Syndrome

描述（由申请人提供）：阻塞性睡眠呼吸暂停（OSA）发生在50-100%的唐氏综合征患者中，并可显著导致和加剧这些患者的医疗问题。目前对这些儿童的手术治疗是不完善的。一线手术[腭扁桃体切除术和腺样体切除术（T&A）]以及T&A失败时进行的二次手术的手术成功率各不相同。迫切需要一种考虑气道解剖结构、组织顺应性和顺应性的诊断模式，以便能够预测这些患者的手术结果并改善手术计划。我们的中心假设是，上气道流动结构相互作用（FSI）建模使用三维（3-D）的计算模拟动态MRI数据可以用来预测手术结果的儿童患有唐氏综合征谁有OSA，尽管以前的T&A。长期目标是通过建立准确的FSI预测模型来改善患有唐氏综合征和OSA的儿童的手术结果。这样的诊断工具将有助于使外科手术更有效，以及识别和避免不必要的（或无益的）外科手术。然后，这些创建的模型也可以调整并应用于其他患有OSA的人群。我们的具体目标包括：1）在患有持续性OSA的唐氏综合征儿童中，尽管先前进行了T&A，收集表征上气道解剖结构、组织顺应性和可操作性的数据; 2）针对特定目标1为每个儿童生成并验证个性化动态FSI模型;以及3）使用经验证的动态计算模型，预测患有持续性OSA的唐氏综合征儿童的手术治疗成功率，尽管先前进行了T&A。这项工作是创新的，因为它使用动态而不是静态的MR成像数据，并应用一个独特的计算模型，准确地捕捉流动的不稳定性，并考虑气流和周围气道柔性结构之间的相互作用。

DESCRIPTION (provided by applicant): Obstructive sleep apnea (OSA) occurs in 50-100% of patients with Down syndrome and can significantly cause and exacerbate medical problems in these patients. Current surgical management in these children is imperfect. There are variable surgical success rates for both first line surgery [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 MRI data can be used to predict surgical outcome for children with Down syndrome 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 then can also be adjusted and applied to other populations with OSA. Our specific aims include: 1) In children with Down syndrome with persistent OSA despite previous T&A, collect data characterizing upper airway anatomy, tissue compliance, and collapsibility; 2) Generate and validate individualized dynamic FSI models for each child in specific aim 1; and 3) Using the validated dynamic computational models, 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.