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

重新构想阻塞性睡眠呼吸暂停的诊断，超越呼吸暂停低通气指数

• 批准号: 10915769
• 负责人: Ankit Ashok Parekh
• 金额: $ 12.68万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10915769
• 关键词: Affect; Age; Apnea; Area; Arousal; Big Data; Breathing; Cardiac health; Cardiovascular Diseases; Cardiovascular system; Cerebrovascular Disorders; Chronic; Classification; Cohort Studies; Congestive Heart Failure; Data; Data Set; Development; Diagnosis; Discrimination; Disease; Drowsiness; Electroencephalography; Epidemiology; Equation; Ethnic Origin; Event; Excessive Daytime Sleepiness; Female; Frequencies; Functional disorder; Funding; Funding Opportunities; Future; Gender; Heart Rate; Home; Hour; Hypertension; Hypoxemia; Hypoxia; Machine Learning; Manuals; Measures; Medicine; Methodology; Morbidity - disease rate; Multi-Ethnic Study of Atherosclerosis; Myocardial Infarction; National Heart, Lung, and Blood Institute; Nature; Neurocognitive; Neurocognitive Deficit; Obstruction; Obstructive Sleep Apnea; Outcome; Oxygen; Oxyhemoglobin; Patients; Persons; Phenotype; Physiological; Physiology; Polysomnography; Prediction of Response to Therapy; Probability; Prognosis; Reproducibility; Risk; Risk Assessment; Severities; Severity of illness; Sleep; Sleep Fragmentations; Stroke; Testing; Thinking; United States National Institutes of Health; Weight; Wisconsin; Withdrawal; Work; adverse outcome; airway obstruction; cardiometabolism; cerebrovascular; disease diagnosis; epidemiology study; high reward; high risk; improved; indexing; inter-individual variation; male; mortality; novel; randomized, clinical trials; rate of change; respiratory; risk prediction; risk stratification; tool; trait

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.

项目摘要/摘要 阻塞性睡眠呼吸暂停（OSA）是一种常见的慢性疾病 损伤，高血压和入射脑血管和心血管疾病。 OSA当前已定义 通过测量由数量/小时定义的阻塞事件频率（呼吸暂停指数[AHI]） 呼吸持续至少10秒的障碍物，并与皮质唤醒和/或氧气有关 睡眠期间的饱和度。 AHI仅适度用于诊断疾病严重程度和 在大型流行病学研究中预测短期和长期后果，并越来越多地证据 生理和方法论基础的局限 我们目前诊断OSA。该项目建议从标准睡眠研究中提取指标来表征 OSA在三个不同领域的负担：通风，低氧和唤醒。通风负担将是 整个夜晚的呼吸大小的分布自动定量 每次呼吸都是阻碍的。低氧负担以自动化的方式定量过夜氧气 去饱和（基线饱和度和去饱和事件之间的面积）。使用唤醒负担定量 与睡眠深度和/或唤醒性间接相关的脑电图指标过夜。这两个总体目的 项目是：1）使用通风负担比AHI更好地识别OSA的存在，2）结合使用 通气/低氧/唤醒负担，在这里进行组合方程式，以预测 短期（白天嗜睡）和长期（心血管死亡率，中风和事件充血性心脏 失败/心肌梗塞）OSA的后果。通过使用现有数据（LAB和在家 多个术语）来自> 9000名受试者，拟议项目将利用大数据的力量完善和 改善我们诊断OSA的方式并评估不良结果的风险。