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
基本信息
- 批准号:10256810
- 负责人:
- 金额:$ 12.31万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:Academic Medical CentersAddressAdultAffectArchitectureAreaBlood PressureCardiovascular DiseasesCardiovascular ManifestationCardiovascular systemCharacteristicsClinicalClinical DataComplexCoupledDataData SetDevelopmentDiagnosticDimensionsEngineeringEventFutureGoalsGoldHealthHealth PersonnelHealth systemHealthcareHumanHypertensionImpairmentIndividualInterventionInvestigationKnowledgeLinkMachine LearningMeasurementMeasuresMediatingMediator of activation proteinMissionModelingMorbidity - disease rateNational Heart, Lung, and Blood InstituteNatureObstructive Sleep ApneaOutcomePatientsPerformancePhysiologicalPolysomnographyPopulationPrevention MeasuresProcessPublishingREM SleepReportingResearchResourcesRespiration DisordersRiskRisk FactorsSleepSleep Apnea SyndromesSleep ArchitectureSleep DisordersSleep disturbancesSleeplessnessTestingTimeUniversitiesVirginiabaseblood pressure regulationblood pressure variabilitycardiovascular disorder preventioncardiovascular disorder riskcardiovascular healthcardiovascular risk factorclinical predictorscohortcost effectiveeffective interventionhealth care settingsimprovedindexinglimb movementmortalitypoor sleeppre-clinicalprognostic valuerisk minimizationsleep abnormalitiessleep qualitysleep quantitysupervised learning
项目摘要
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)是美国死亡率的主要原因。
心血管疾病的高危因素。睡眠障碍,如阻塞性睡眠呼吸暂停(OSA)、失眠、睡眠异常
持续时间和睡眠质量差均与CVD相关的发病率和死亡率相关。主要
现有关于睡眠和心血管疾病研究的局限性是缺乏客观的睡眠测量,
了解睡眠的多维性质及其与CVD的复杂相互作用。此外,本发明还提供了一种方法,
新出现的证据也表明睡眠障碍与临床前CV之间存在潜在关系
这些疾病通常发生在CVD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
对未来CV事件具有预后价值的临床前CVD。然而,还没有研究探讨,如果
OSA以外的睡眠障碍(如睡眠质量受损和睡眠时间异常)也是危险因素
归因于临床前CV疾病。这是一个关键的调查领域,因为了解复杂的
睡眠与临床前和临床CV状况之间的关系将使医疗保健提供者能够
实施有针对性的干预措施,以减少心血管疾病。
这项拟议的研究将检查在临床上获得的PSG衍生的睡眠客观指标是否
该设置可以预测CVD和临床前CVD。鉴于高血压是主要的
重要的CV风险,并且是与睡眠、SBPV、平均SBP和
其他血压度量将是临床前CVD的焦点。我们的主要目的是检查
多维睡眠特征(持续时间、效率、质量和
睡眠呼吸障碍)和临床CV状况,在调整个人、临床和其他因素后,
混杂因素。我们将使用从7,000多名完成诊断的人中收集的睡眠数据,
2010 - 2018年弗吉尼亚大学卫生系统的睡眠研究。机器学习(ML)模型将
用于分析多维度PSG测量。这项拟议的研究代表了最大的现实世界
客观睡眠测量数据集,这将使我们能够同时检查整个频谱,
睡眠和推进我们对睡眠对心血管结局影响的理解。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Younghoon Kwon其他文献
Younghoon Kwon的其他文献
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{{ truncateString('Younghoon Kwon', 18)}}的其他基金
Sleep Apnea-Specific Nocturnal Blood Pressure Surge to Determine Cardiovascular Risks and Therapeutic Benefits in Patients with Obstructive Sleep Apnea
睡眠呼吸暂停特异性夜间血压升高可确定阻塞性睡眠呼吸暂停患者的心血管风险和治疗效果
- 批准号:
10686068 - 财政年份:2021
- 资助金额:
$ 12.31万 - 项目类别:
Sleep Apnea-Specific Nocturnal Blood Pressure Surge to Determine Cardiovascular Risks and Therapeutic Benefits in Patients with Obstructive Sleep Apnea
睡眠呼吸暂停特异性夜间血压升高可确定阻塞性睡眠呼吸暂停患者的心血管风险和治疗效果
- 批准号:
10277143 - 财政年份:2021
- 资助金额:
$ 12.31万 - 项目类别:
Photoplethysmography Analysis to Assess Cardio-Cerebrovascular Impact of Sleep
通过光电体积描记法分析评估睡眠对心脑血管的影响
- 批准号:
10266835 - 财政年份:2020
- 资助金额:
$ 12.31万 - 项目类别:
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