Reimagining the diagnosis of obstructive sleep apnea beyond the apnea-hypopnea index

项目摘要/摘要 阻塞性睡眠呼吸暂停(OSA)是一种与神经认知相关的常见慢性疾病 损伤、高血压以及突发的脑血管和心血管疾病。OSA当前已定义 通过测量阻塞性事件的频率(呼吸暂停低通气指数[AHI])，定义为 持续至少10秒的呼吸障碍，并与皮质觉醒和/或氧气有关 睡眠时的去饱和度。AHI仅对疾病严重程度的诊断和 在大型流行病学研究中预测短期和长期后果和越来越多的AHI证据 生理学和方法论上的限制导致了对重新思考方式的强烈抗议 我们目前诊断为阻塞性睡眠呼吸暂停综合征。该项目建议从标准的睡眠研究中提取指标来表征 阻塞性睡眠呼吸暂停综合征在三个不同领域的负担：呼吸、缺氧和觉醒。通风负担将是 根据整夜呼吸大小的分布自动量化，并结合 每一次呼吸都是阻碍。低氧负荷以自动方式从过夜氧气定量 减饱和度(基线饱和度和减饱和度事件之间的面积)。唤醒负荷的量化使用 与夜间睡眠深度和/或唤醒能力间接相关的脑电指标。这样做的两个首要目标是 项目是：1)使用呼吸负荷比AHI更好地识别OSA的存在，以及2)结合使用 呼吸性/缺氧性/觉醒负荷，其中组合的方程是机器学习的，以预测 短期(日间嗜睡)和长期(心血管死亡率、中风和充血性心脏事件 衰竭/心肌梗死)OSA的后果。通过使用现有数据(实验室内和家中 多导睡眠图)，拟议的项目将利用大数据的力量来提炼和 改进我们诊断阻塞性睡眠呼吸暂停和评估不良后果风险的方法。

PROJECT SUMMARY/ABSTRACT Obstructive sleep apnea (OSA) is a common chronic condition variably associated with neurocognitive impairment, hypertension, and incident cerebrovascular and cardiovascular disease. OSA is currently defined by measuring the frequency of obstructive events (apnea-hypopnea index [AHI]) defined by the number/hour of obstructions to breathing lasting at least 10 seconds and associated with cortical arousals and/or oxygen desaturation during sleep. The AHI has been only modestly useful for diagnosis of disease severity and for predicting short- and long-term consequences in large epidemiologic studies and mounting evidence of AHI's limitations on both physiological as well as methodological grounds has led to an outcry for rethinking the way we currently diagnose OSA. This project proposes to extract, from standard sleep studies, metrics to characterize the burden of OSA in three distinct domains: ventilatory, hypoxic and arousal. The ventilatory burden will be quantitated automatically from the distribution of breath sizes in a whole night combined with the probability that each breath is obstructive. Hypoxic burden is quantitated in an automated fashion from overnight oxygen desaturation (area between baseline saturation and desaturation events). Arousal burden is quantitated using EEG metrics that indirectly relate to sleep depth and/or arousability overnight. The two overarching aims of this project are: 1) use ventilatory burden to identify presence of OSA better than AHI, and 2) use combination of ventilatory/hypoxic/arousal burdens, where the equation for combination is machine-learned, to predict risk of short- (daytime sleepiness) and long-term (cardiovascular mortality, stroke, and incident congestive heart failure/myocardial infarction) consequences of OSA. By using existing data (in-lab and at-home polysomnography) from >9000 subjects, the proposed project will harness the power of big data to refine and improve the way we diagnose OSA and assess risk of adverse outcomes.