Photoplethysmography Analysis to Assess Cardio-Cerebrovascular Impact of Sleep

项目摘要 阻塞性睡眠呼吸暂停(OSA)在老年人群中很常见，并与 心血管疾病，包括脑血管损伤。反复暴露于急性简历反应，如 由于交感神经激增和血压(BP)在阻塞性呼吸事件后上升，是一个重要的 阻塞性睡眠呼吸暂停综合征与心血管疾病和脑血管损伤的中介机制。然而，如此尖锐的简历 传统的多导睡眠监测指标，如呼吸暂停，不能有效地捕捉到反应 阻塞性睡眠呼吸暂停低通气指数(AHI)是评估OSA的常用指标。这导致对患者的分类不准确 他们的简历风险条款，可能是流行病学和临床研究结果不一致的原因。 更重要的是，持续气道正压(CPAP)治疗的有效性在 在老年人的治疗决策中，降低心血管风险是一个重大挑战。 因此，识别额外的表型标记更好地量化不利的CV影响 OSA和提供更好的心血管预后预测对于改善风险分层和临床至关重要 治疗性决策。 在这里，我们建议研究新的生理测量，这些测量可以很容易地从单个 光体积描记(PPG)传感器，并调查PPG特征是否与 亚临床、临床脑血管疾病和脑血管损伤。我们将从PPG中提取功能 多民族动脉粥样硬化(MESA)睡眠研究和检查中获得的多导睡眠图 PPG特征是否与心血管预后相关，包括左心室重量、主动脉僵硬和 突发心血管事件，以及脑血管损伤的标志，包括脑结构 老年男性MRI异常与认知能力筛查仪的认知功能障碍 还有女人。我们将比较PPG特征与这些结果的关联与常规PPG特征的关联 OSA评估指标(如呼吸暂停低通气指数)。令人感兴趣的主要PPG特征是斜坡交通 时间到了。为了评估各种PPG功能在评估和分类结果方面的效用，我们将 使用新的机器学习方法，例如基于Gnosis(通用网络 信息系统优化综合)基于信息论的建模工具。 随后，我们将确定PPG特征如何预测CPAP和O2治疗对BP治疗的反应 使用心跳随机对照试验的数据。 这项研究试图确定以心血管为中心的新的PSG指标，并评估它们的临床 对老年人的效用。研究结果将为进一步开发更简单的 评估和监测阻塞性睡眠呼吸暂停综合征的方法。考虑到阻塞性睡眠呼吸暂停综合征在老年人群中的高患病率， 再加上它对不良健康后果的影响，该提案对公共卫生具有重大影响。

Project Summary Obstructive sleep apnea (OSA) is common in the aging population and is associated with increased risk of cardiovascular (CV) disease, including cerebrovascular injury. Repetitive exposure to acute CV response, such as sympathetic surge and blood pressure (BP) rise following obstructive respiratory events, is an important mediating mechanism linking OSA to CV disease and cerebrovascular injury. However, such acute CV responses are not effectively captured by conventional polysomnography metrics, such as the apnea hypopnea index (AHI) commonly used in OSA evaluation. This results in imprecise classification of patients in terms of their CV risk and may be responsible for inconsistent results in epidemiologic and clinical studies. More importantly, the uncertainty of the effectiveness of continuous positive airway pressure (CPAP) therapy in reducing the CV risk poses a significant challenge in therapeutic decision-making in older individuals. Therefore, the identification of additional phenotypic markers that better quantify the unfavorable CV effects of OSA and provide improved prediction of CV outcomes is crucial to improving risk stratification and clinical therapeutic decision-making. Herein, we propose to study novel physiologic measurements that can readily be retrieved from a single photoplethysmography (PPG) sensor and investigate whether PPG features are associated with markers of subclinical, clinical CV disease, and cerebrovascular injury. We will extract PPG features from polysomnography obtained in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study and examine whether PPG features are associated with CV outcomes, including left ventricular mass, aortic stiffness, and incident cardiovascular events, as well as markers of cerebrovascular injury, including brain structural abnormalities by MRI and cognitive impairment by Cognitive Abilities Screening Instrument in the older men and women. We will compare the association of PPG features with these outcomes with that of conventional OSA assessment metrics (such as apnea hypopnea index). The main PPG feature of interest is slope transit time. To assess the utility of the various PPG features for use in estimating and classifying outcomes, we will use novel machine learning methods, such as are based on the GNOSIS (Generalized Networks for the Optimal Synthesis of Information Systems) information-theory-based modeling tool. Subsequently, we will determine how PPG features predict BP treatment response to CPAP and O2 therapy using data from the HeartBEAT randomized controlled trial. This study attempts to identify novel PSG metrics that are cardiovascular-centric and to assess their clinical utility in the older adults. The findings of the study will provide a basis for further development of a simpler method by which to assess and monitor OSA. Considering the high prevalence of OSA in older populations, coupled with its impact on adverse health outcomes, the proposal has significant public health implications.