A mechanistic understanding of treatment-related outcomes of sleep disordered breathing using functional near infrared spectroscopy

项目总结 睡眠呼吸障碍(SDB)，以打呼噜和睡眠中断为特征，影响十分之一的儿童。 SDB的不良后果，如问题行为、嗜睡和较低的生活质量，都会导致不良 课堂表现，被认为与前额叶的结构和功能改变有关 大脑皮层(PFC)。因此，所有临床学会都支持在儿童中普遍筛查SDB及其 扁桃体和腺样体摘除治疗(腺扁桃体切除术或‘AT’)，手术进行了500,000名儿童- 每年仅在美国的儿童。然而，一些儿童也经历了自发的解决 对于这些儿童来说，SDB和因此警惕的等待也是一种可以接受的治疗选择。目前有 对于可能受益于AT的儿童，没有统一接受的标准，而不是警惕的等待。多项睡眠- 目前用于对SDB严重程度进行分层。然而，多导睡眠图参数并不对应于 SDB儿童的行为、认知或生活质量迟缓，也不影响AT的结果。因此，有两个主要原因 知识方面存在差距，其中包括：(I)缺乏对AT影响SDB的机制的了解 结果，以及(Ii)选择可能受益于AT的儿童的预测模型。解决这些差距 可以改善患者选择，减少危害，并提高父母满意度，这与最复杂的 我的儿科外科手术。 我们已经证明，SDB和儿童行为之间的关系是通过结构变化来调节的 在PFC内或接近PFC的。利用功能近红外光谱(FNIRS)，我们进一步证明了 前额叶皮质(PFC)的区域大脑激活与儿童的行为测量相关 与SDB合作。使用一系列数据科学技术，我们还证明了多导睡眠图参数- 参数不能预测SDB或AT的结果。在这里，我们建议使用一种新的生物标记物来进行功能激活 用fNIRS和基线患者特征预测接受SDB的儿童的预后 治疗。在200名接受早期AT或两岁时警惕等待策略的SDB儿童中 大型和多样化的临床实践，我们的具体目标包括：目标1)确定认知能力的程度 AT的结果是通过PFC的激活来调节的，目的2)以确定父母报告的- AT的发生是由PFC激活介导的，目的3)开发SDB治疗的预测模型 使用人口学、人体测量学、多导睡眠图和fNIRS变量的结果。使用多学科 凭借世界级的数据科学方法专业知识，这项建议旨在解决我们在 对AT结果的机械性理解，并建立一个客观的、对儿童友好的、经济高效的AP- 确定外科候选人的方法。

PROJECT SUMMARY Sleep disordered breathing (SDB), characterized by snoring and sleep disruption, affects one in ten children. Adverse outcomes of SDB such as problem behaviors, sleepiness, and lower quality of life contribute to poor classroom performance and are thought to be related to structural and functional alterations within the prefrontal cortex (PFC) of the brain. All clinical societies therefore support universal screening for SDB in children and its treatment by removal of tonsils and adenoids (adenotonsillectomy or ‘AT’), a surgery performed in 500,000 chil- dren annually in the United States alone. However, some children also experience spontaneous resolution of SDB and therefore watchful waiting is also an acceptable treatment option for these children. Currently there are no uniformly accepted criteria for children likely to benefit from AT as opposed to watchful waiting. Polysomnog- raphy is currently used for stratification of SDB severity. However, polysomnographic parameters neither corre- late with behavior, cognition, or quality of life nor the outcomes of AT in children with SDB. Therefore, two major gaps in knowledge exist, which include the lack of (i) understanding the mechanism by which AT impacts SDB outcomes, and (ii) a predictive model for selection of children likely to benefit from AT. Addressing these gaps could improve patient selection, reduce harm, and increase parental satisfaction related to one of the most com- mon pediatric surgical procedures. We have shown that the relationship between SDB and children’s behavior is mediated by structural alterations within or close to the PFC. Using functional near-infrared spectroscopy (fNIRS), we have further demonstrated that regional brain activation within the prefrontal cortex (PFC) correlates with behavioral measures in children with SDB. Using a range of data science techniques, we have additionally shown that polysomnographic param- eters do not predict SDB or AT outcomes. Here we propose the use of a novel biomarker for functional activation of the PFC using fNIRS and baseline patient characteristics to predict outcomes of children undergoing SDB treatment. In 200 children undergoing management of SDB by early AT or a strategy of watchful waiting at two large and diverse clinical practices, our specific aims include: Aim 1) To determine the extent to which cognitive outcomes of AT are mediated by PFC activation, Aim 2) To determine the extent to which parent-reported out- comes of AT are mediated by PFC activation, and Aim 3) To develop a predictive model for SDB treatment outcomes using demographic, anthropometric, polysomnographic, and fNIRS variables. Using a multidisciplinary approach with world-class expertise in data science approaches, this proposal seeks to address gaps in our mechanistic understanding of AT outcomes and establish an objective, child-friendly, and cost-effective ap- proach for identification of surgical candidates.