Cardiovascular implications of sleep characteristics using real-world objective sleep data

使用真实世界客观睡眠数据的睡眠特征对心血管的影响

• 批准号: 10256810
• 负责人: Younghoon Kwon
• 金额: $ 12.31万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10256810
• 关键词: Academic Medical Centers; Address; Adult; Affect; Architecture; Area; Blood Pressure; Cardiovascular Diseases; Cardiovascular Manifestation; Cardiovascular system; Characteristics; Clinical; Clinical Data; Complex; Coupled; Data; Data Set; Development; Diagnostic; Dimensions; Engineering; Event; Future; Goals; Gold; Health; Health Personnel; Health system; Healthcare; Human; Hypertension; Impairment; Individual; Intervention; Investigation; Knowledge; Link; Machine Learning; Measurement; Measures; Mediating; Mediator of activation protein; Mission; Modeling; Morbidity - disease rate; National Heart, Lung, and Blood Institute; Nature; Obstructive Sleep Apnea; Outcome; Patients; Performance; Physiological; Polysomnography; Population; Prevention Measures; Process; Publishing; REM Sleep; Reporting; Research; Resources; Respiration Disorders; Risk; Risk Factors; Sleep; Sleep Apnea Syndromes; Sleep Architecture; Sleep Disorders; Sleep disturbances; Sleeplessness; Testing; Time; Universities; Virginia; base; blood pressure regulation; blood pressure variability; cardiovascular disorder prevention; cardiovascular disorder risk; cardiovascular health; cardiovascular risk factor; clinical predictors; cohort; cost effective; effective intervention; health care settings; improved; indexing; limb movement; mortality; poor sleep; pre-clinical; prognostic value; risk minimization; sleep abnormalities; sleep quality; sleep quantity; supervised learning

Project Summary Cardiovascular disease (CVD) is the leading cause of mortality in the U.S. Impaired sleep is recognized as a strong risk factor of CVD. Sleep disorders such as obstructive sleep apnea (OSA), insomnia, abnormal sleep duration, and poor sleep quality have each been associated with CVD-related morbidity and mortality. Major limitations of existing studies on sleep and CVD is the lack of objective sleep measurement and the lack of understanding of the multi-dimensional nature of sleep and its complex interactions with CVD. In addition, emerging evidence also suggests potential relationships between sleep disorders and preclinical CV conditions, which often occur before the clinical manifestation of CVD. Blood pressure parameters including systolic blood pressure variability (SBPV) and mean systolic blood pressure (SBP) are examples of preclinical CVD that have prognostic value for future CV events. However, no research has yet explored if sleep disorders beyond OSA (such as impaired sleep quality and abnormal sleep duration) are risk factors attributable to preclinical CV conditions. This is a critical area of inquiry since understanding the complex relationships between sleep and preclinical and clinical CV conditions will allow healthcare providers to implement targeted interventions to reduce CVD. The proposed study will examine whether PSG-derived objective measures of sleep obtained in the clinical setting would be predictive of CVD and preclinical CVD. Given that hypertension is one of the major important CV risks and has been most well studied CV risk factor in relation to sleep, SBPV, mean SBP, and other blood pressure metrics will be the focus of the preclinical CVD. Our main aim is to examine the relationships between multidimensional sleep characteristics (in terms of duration, efficiency, quality, and disordered sleep breathing) and clinical CV conditions, after adjusting for personal, clinical, and other confounders. We will use sleep data collected from more than 7,000 individuals who completed a diagnostic sleep study at the University of Virginia Health System in 2010 - 2018. Machine learning (ML) models will be used to analyze the multidimensional PSG measures. This proposed study represents the largest real-world dataset on objective sleep measures, which will allow us to simultaneously examine the entire spectrum of sleep and advance our understanding about the impact of sleep on CV outcomes.

项目摘要 心血管疾病（CVD）是美国睡眠受损的死亡率的主要原因，被认为是 CVD的强风险因素。睡眠障碍，例如阻塞性睡眠呼吸暂停（OSA），失眠，异常睡眠 持续时间和睡眠质量差与CVD相关的发病率和死亡率有关。主要的 现有研究对睡眠和CVD的局限性缺乏客观的睡眠测量和缺乏 了解睡眠的多维性质及其与CVD的复杂相互作用。此外， 新兴证据还表明睡眠障碍与临床前CV之间的潜在关系 条件通常发生在CVD的临床表现之前。血压参数包括 收缩压变异性（SBPV）和平均收缩压（SBP）是 临床前CVD对未来的简历事件具有预后价值。但是，尚无研究 OSA以外的睡眠障碍（例如睡眠质量受损和睡眠持续时间异常）是风险因素 归因于临床前简历条件。这是一个关键的询问领域，因为了解复杂 睡眠与临床前和临床简历条件之间的关系将使医疗保健提供者能够 实施目标干预措施以减少CVD。 拟议的研究将检查临床中获得的PSG衍生的睡眠客观测量 设置将预测CVD和临床前CVD。鉴于高血压是主要的 重要的简历风险，并且已经对睡眠，SBPV，平均SBP和 其他血压指标将是临床前CVD的重点。我们的主要目的是检查 多维睡眠特征之间的关系（就持续时间，效率，质量和 调整了个人，临床和其他 混淆者。我们将使用从完成诊断的7,000多人收集的睡眠数据 2010年至2018年在弗吉尼亚大学卫生系统的睡眠研究。机器学习（ML）模型将是 用于分析多维PSG测量。这项拟议的研究代表了最大的现实世界 有关客观睡眠度量的数据集，这将使我们能够同时检查整个频谱 睡眠并提高我们对睡眠对简历结果的影响的理解。