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%的唐氏综合症患者中，并可显著导致和加剧这些患者的医疗问题。目前对这些儿童的外科治疗并不完善。一线手术[扁桃体切除术和腺样体切除术(T&A)]和二次手术(如果和当T&A失败时)的手术成功率各不相同。迫切需要一种考虑到呼吸道解剖、组织顺应性和塌陷性的诊断方法，以便能够预测手术结果并改进这些患者的手术计划。我们的中心假设是，使用来自动态MRI数据的三维(3-D)计算模拟的上呼吸道流动-结构相互作用(FSI)建模可以用于预测患有唐氏综合征的儿童的手术结果，尽管有过Ta&A。长期目标是通过创建准确的FSI预测模型来改善患有Down综合征和OSA的儿童的手术结果。这样的诊断工具将有助于量身定做手术程序，使其更有效，并识别和避免不必要的(或无用的)手术程序。然后，这些创建的模型也可以调整并应用于其他OSA人群。我们的具体目标包括：1)在唐氏综合征儿童中，收集表征上呼吸道解剖、组织顺应性和塌陷性的数据；2)在特定的目标1中为每个儿童生成和验证个性化的动态FSI模型；以及3)使用经过验证的动态计算模型，预测尽管先前的T&A但仍患有持续性OSA的唐氏综合征儿童的手术治疗成功。这项工作具有创新性，因为它使用动态而不是静态的磁共振成像数据，并应用独特的计算模型来准确捕捉气流的不稳定性，并解释气流与周围气道灵活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.