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

点击翻译按钮获取中文摘要

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.