Dynamic Computational Modeling of Obstructive Sleep Apnea in Down Syndrome

唐氏综合症阻塞性睡眠呼吸暂停的动态计算模型

• 批准号: 8013356
• 负责人: Raouf S. Amin
• 金额: $ 96.28万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-17 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8013356
• 关键词: 3-Dimensional; Accounting; Adenoidectomy; Adult; Airway Resistance; Anatomy; Apnea; Child; Collaborations; Computer Simulation; Congenital chromosomal disease; Continuous Positive Airway Pressure; Data; Development; Diagnostic; Down Syndrome; Effectiveness; Elasticity; Functional disorder; Goals; Health; Hypercapnia; Hypoxemia; Image; Incidence; Infant; Interdisciplinary Study; Knowledge; Magnetic Resonance Imaging; Measurement; Measures; Medical; Methodology; Modality; Modeling; Nature; Nose; Obstructive Sleep Apnea; Operative Surgical Procedures; Outcome; Output; Patients; Population; Procedures; Public Health; Publishing; Refractory; Research; Sleep; Sleep Apnea Syndromes; Structure; Surgical Management; Techniques; Testing; Tissues; Tonsillectomy; United States; Work; abstracting; flexibility; improved; indexing; innovation; member; novel; pharyngeal critical pressure; predictive modeling; pressure; public health relevance; rapid growth; success; tool

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. PUBLIC HEALTH RELEVANCE: The proposed research is relevant to public health because it seeks to create a diagnostic tool that will better predict the need and effectiveness of specific surgical procedures in the treatment of obstructive sleep apnea (OSA) in children with Down syndrome. Such a diagnostic tool would help tailor surgical procedures to be more effective as well as identify and avoid unnecessary (or unhelpful) surgical procedures. The creation of this diagnostic tool is important in children with Down syndrome because of the very high rate and refractory nature of OSA in these children but also the creation of the tool may also be applied and benefit other populations of children and adults with OSA, which constitutes a major health problem in the USA. (End of Abstract)

描述（由申请人提供）：阻塞性睡眠呼吸暂停（OSA）在50-100%的唐氏综合征患者中发生，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[腭扁桃体切除术和腺样体切除术（T&A）]的手术成功率不同，如果T&A失败，则进行二次手术。目前迫切需要一种能够考虑气道解剖、组织顺应性和可折叠性的诊断模式，以便能够预测手术结果并改善这些患者的手术计划。我们的中心假设是，使用动态MRI数据的三维（3-D）计算模拟的上气道流量-结构相互作用（FSI）模型可用于预测患有OSA的唐氏综合征儿童的手术结果，尽管之前有过T&A。长期目标是通过建立准确的FSI预测模型来改善唐氏综合征和OSA患儿的手术效果。这样的诊断工具将有助于定制更有效的外科手术，并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OSA患者。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，收集表征上呼吸道解剖、组织顺应性和可折叠性的数据；2)在特定目标1中为每个孩子生成并验证个性化动态FSI模型；3)使用经过验证的动态计算模型，预测唐氏综合征患儿手术治疗的成功率，尽管既往有T&A，但仍存在持续性OSA。这项工作是创新的，因为它使用动态而不是静态磁共振成像数据，并应用独特的计算模型，准确捕获流动的不稳定性，并解释气流与周围气道柔性结构之间的相互作用。公共卫生相关性：这项拟议的研究与公共卫生相关，因为它寻求创造一种诊断工具，以更好地预测治疗唐氏综合症儿童阻塞性睡眠呼吸暂停（OSA）的特定外科手术的必要性和有效性。这样的诊断工具将有助于定制更有效的外科手术，并识别和避免不必要的（或无益的）外科手术。这一诊断工具的创建对于患有唐氏综合症的儿童非常重要，因为这些儿童的OSA发病率很高且难治性，而且该工具的创建也可以应用于其他患有OSA的儿童和成人人群，并使其受益，这是美国的一个主要健康问题。（摘要结束）