Prediction of Physiological Events in People with Sleep Disordered Breathing

睡眠呼吸障碍患者生理事件的预测

• 批准号: 7745752
• 负责人: Jonathan A Waxman
• 金额: $ 4.23万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7745752
• 关键词: Accidents; Address; Adverse event; Algorithms; Apnea; Arousal; Attention; Automobile Driving; Behavior; Behavioral; Biological Neural Networks; Breathing; Characteristics; Chicago; Cognitive deficits; Collaborations; Complex; Computers; Continuous Positive Airway Pressure; Cranial Nerves; Detection; Development; Devices; Disease; Effectiveness; Electric Stimulation; Electroencephalography; Electronics; Environmental air flow; Event; Excessive Daytime Sleepiness; Exhibits; Functional disorder; Future; Glean; Goals; Graph; Illinois; Imagery; Impaired cognition; Impairment; Individual; Injury; Laboratories; Lead; Machine Learning; Maintenance; Measurable; Medical; Memory; Methodology; Methods; Network-based; Obstructive Sleep Apnea; Outcome; Patients; Pattern; Performance; Physiological; Physiological Processes; Polysomnography; Population; Predictive Value; Predisposition; Process; Property; Public Health; Quality of life; Research; Retrieval; Risk; Scientist; Sex Characteristics; Signal Transduction; Sleep; Sleep Apnea Syndromes; Sleep Deprivation; Smoking; Social Interaction; Social Network; Societies; Source; System; Systems Theory; Techniques; Testing; Tidal Volume; Time; Time Series Analysis; Titrations; Universities; Wakefulness; Woman; Work; Workplace; Zebra; automobile accident; cost; design; executive function; experience; heart rate variability; improved; innovation; insight; men; neurophysiology; novel; novel strategies; pharynx muscle; respiratory; simulation; sleep onset; theories; tool

DESCRIPTION (provided by applicant): Sleep-disordered breathing (SDB) refers to a spectrum of disorders characterized by abnormal respiratory patterns or levels of ventilation during sleep. The most common is obstructive sleep apnea (OSA). People with OSA experience repetitive apnea (cessation of breathing) and hypopnea (marked decrease in tidal volume) during sleep in association with airway compromise and excessive daytime sleepiness (EDS). An arousal, or brief, often unnoticed, disruption of sleep is commonly associated with apnea. People with OSA also exhibit cognitive dysfunction, including impairment to memory, attention, and executive function. OSA- associated EDS and cognitive dysfunction are thought to significantly contribute to automobile accidents and workplace injuries. The first aim of this research is to predict the onset of nocturnal apnea, hypopnea, and arousal. Our proposal to accomplish this aim represents an entirely new approach to improving the effectiveness and tolerability of SBD therapy. The most common therapy is continuous positive airway pressure (CPAP), which is difficult for many patients to tolerate. Existing auto-adjusting PAP may be more tolerable but relies on detection of disordered breathing events and does not appear to improve quality of life compared with conventional CPAP. Predicting these events could lead to more effective titration of PAP levels and improved outcomes. Other therapies, such as the electrical stimulation of various cranial nerves or pharyngeal muscles, could also be improved by predicting disordered breathing. The second aim is to predict the onset of unintended daytime sleep while subjects undergo maintenance of wakefulness tests, which assess one's ability to resist sleep in a soporific condition. The third aim is to predict performance lapses during driving simulations. Accomplishing aims two and three could lead to the development of warning devices for at-risk individuals. Our novel prediction algorithms track the interactions between several physiological systems and reveal the most important predictors. The fourth aim is to contrast the key predictors between OSA, acutely sleep-deprived, and control subjects, and between men and women. By doing so, we expect to gain insight into the underlying pathophysiology of SDB and EDS, and will investigate sex differences in OSA and sleep deprivation. OSA is a major public health problem whose effects on society are comparable to those of smoking. The capability to predict its adverse consequences will be an invaluable tool to improve the quality of life of people with SDB, reduce the associated costs, and improve public health

描述（由申请人提供）：睡眠呼吸障碍（SDB）是指以睡眠期间呼吸模式或通气水平异常为特征的一系列疾病。最常见的是阻塞性睡眠呼吸暂停（OSA）。 OSA 患者在睡眠期间会出现重复性呼吸暂停（呼吸停止）和呼吸不足（潮气量显着减少），这与气道受损和日间过度嗜睡 (EDS) 相关。睡眠的觉醒或短暂的、经常被忽视的中断通常与呼吸暂停有关。患有 OSA 的人还表现出认知功能障碍，包括记忆、注意力和执行功能受损。 OSA 相关的 EDS 和认知功能障碍被认为是导致车祸和工作场所伤害的重要原因。这项研究的首要目的是预测夜间呼吸暂停、呼吸不足和觉醒的发生。我们为实现这一目标而提出的建议代表了一种提高 SBD 治疗有效性和耐受性的全新方法。最常见的治疗方法是持续气道正压通气（CPAP），这对许多患者来说难以耐受。现有的自动调节 PAP 可能更容易耐受，但依赖于呼吸紊乱事件的检测，与传统 CPAP 相比，似乎无法改善生活质量。预测这些事件可以更有效地调整 PAP 水平并改善结果。其他疗法，例如对各种脑神经或咽肌的电刺激，也可以通过预测呼吸紊乱来改善。第二个目标是在受试者接受维持清醒测试时预测无意的白天睡眠的开始，该测试评估一个人在催眠状态下抵抗睡眠的能力。第三个目标是预测驾驶模拟期间的性能下降。实现第二个和第三个目标可能会导致为高危人群开发预警设备。我们新颖的预测算法跟踪多个生理系统之间的相互作用并揭示最重要的预测因子。第四个目标是对比 OSA、严重睡眠剥夺和对照受试者之间以及男性和女性之间的关键预测因素。通过这样做，我们希望深入了解 SDB 和 EDS 的潜在病理生理学，并将调查 OSA 和睡眠剥夺的性别差异。 OSA 是一个重大的公共卫生问题，其对社会的影响与吸烟相当。预测其不良后果的能力将成为改善 SDB 患者的生活质量、降低相关成本和改善公共卫生的宝贵工具