Sleep-Related Respiratory and Electrophysiological Atrial Fibrillation Predictor

睡眠相关呼吸和电生理心房颤动预测器

• 批准号: 8243087
• 负责人: Reena Mehra
• 金额: $ 29.49万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-12-01 至 2013-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8243087
• 关键词: Apnea; Arousal; Atrial Fibrillation; Autonomic Dysfunction; Baroreflex; Biological Markers; Biometry; Breathing; Cardiovascular Diseases; Central Sleep Apnea; Clinical; Cohort Studies; Data; Diabetes Mellitus; Dose; Electrocardiogram; Electroencephalogram; Electrophysiology (science); Epidemic; Epidemiology; Evaluation; Event; Frequencies; Functional disorder; Future; Goals; Heart Atrium; Heart Rate; Hour; Hypoxemia; Hypoxia; Incidence; Individual; Inflammation; Link; Measures; Mediating; Mediation; Medicine; Metric; Monitor; Morbidity - disease rate; National Heart, Lung, and Blood Institute; Obesity; Obstructive Sleep Apnea; Outcome; Oxidative Stress; Oxygen; Participant; Patients; Phenotype; Positioning Attribute; Prevention strategy; Relative (related person); Reporting; Research; Research Infrastructure; Risk; Risk Factors; Role; Screening procedure; Severities; Signal Transduction; Sleep; Sleep Apnea Syndromes; Sleep Disorders; Strategic Planning; System; Testing; Time; Work; adjudicate; base; clinical practice; cohort; computerized data processing; design; follow-up; hazard; heart rate variability; high risk; improved; indexing; innovation; mortality; novel; older men; respiratory; response; tool; treatment strategy

DESCRIPTION (provided by applicant): The NHLBI Division of Cardiovascular Diseases strategic plan (September 2008) highlighted the need to identify strategies to halt the AF epidemic and its associated morbidity. Recognizing novel triggers of atrial fibrillation (AF) including sleep-disordered breathing (SDB) and autonomic dysfunction is critical, as the incidence of AF is not fully explained by known risk factors. Although >1.1 million polysomnograms (PSGs) are performed annually in the US, all of which include continuous respiratory, EEG and ECG monitoring, information from these signals that may potentially identify patients at risk for future AF is not utilized. Our goal, therefore, is to identify markers from both standard nocturnal PSG scoring and from the new analyses of the PSG ECG channel that predict incident AF. Capitalizing on existing rigorously collected data (PSG including ECG monitoring, biomarkers, etc) and the multicenter infrastructure of a longitudinal cohort study (Outcomes of Sleep Disorders in Older Men Study; n~3000 participants) provides a strategic opportunity to assess the ability of PSG markers to predict individuals at increased risk for clinically adjudicated AF over an 8 year follow-up period. Although our prior cross-sectional work has shown a 2-4 fold higher odds of AF related to SDB (largely central apnea) and a relationship of SDB with both oxidative stress and systemic inflammation, prior reports have not clarified the role of PSG markers including ECG-derived markers as predictors of incident AF and have not characterized underlying linking pathophysiology. The proposed research is designed to: 1) Identify PSG-based SDB phenotypes that predict incident AF, including the relative contribution of central apnea and periodic breathing versus obstructive apnea, and explore mediation by systemic inflammation and oxidative stress, 2) Identify PSG-derived ECG markers of atrial ectopy and autonomic imbalance and evaluate their utility as predictors of incident AF and 3) Develop and test an AF clinical prediction rule incorporating significant PSG and ECG marker AF predictors. Our group is well-positioned to undertake the study aims given our expertise in epidemiology, sleep medicine, biostatistics, electrophysiology, dynamical systems and signal processing. PUBLIC HEALTH RELEVANCE: We expect that an innovative and readily applicable clinical tool utilizing routinely collected data from the PSGs will inform future screening and treatment AF approaches, and thus will be of high clinical impact. This information, collected in a cohort at high risk for AF and its complications is key for developing new strategies to reduce AF-related morbidity and mortality.

描述（由申请人提供）：NHLBI心血管疾病部战略计划（2008年9月）强调需要确定阻止AF流行及其相关发病率的策略。识别房颤（AF）的新触发因素（包括睡眠呼吸障碍（SDB）和自主神经功能障碍）至关重要，因为已知的风险因素无法完全解释AF的发生率。尽管在美国每年进行> 110万次多导睡眠图（PSG），所有这些都包括连续的呼吸、EEG和ECG监测，但是来自这些信号的可能潜在地识别未来AF风险的患者的信息没有被利用。因此，我们的目标是从标准夜间PSG评分和PSG ECG通道的新分析中识别预测AF事件的标志物。（PSG包括ECG监测、生物标志物等）和纵向队列研究的多中心基础设施老年男性睡眠障碍研究结果; n约3000名参与者）提供了一个战略机会，以评估PSG标记物预测临床判定AF风险增加的个体的能力，年随访期。尽管我们先前的横断面研究表明，与SDB（主要是中枢性呼吸暂停）相关的AF几率高2-4倍，SDB与氧化应激和全身炎症相关，但先前的报告尚未阐明PSG标记物（包括ECG衍生标记物）作为AF事件预测因子的作用，也未表征潜在的相关病理生理学。拟议的研究旨在：1）识别预测AF事件的基于PSG的SDB表型，包括中枢性呼吸暂停和周期性呼吸相对于阻塞性呼吸暂停的相对贡献，并探索全身炎症和氧化应激的介导，2）识别心房异位和自主神经失衡的PSG衍生ECG标记物，并评估其作为偶发AF预测因子的效用，以及3）开发并测试AF临床预测规则，包括重要的PSG和ECG标记AF预测因子。我们的团队在流行病学、睡眠医学、生物统计学、电生理学、动力系统和信号处理方面的专业知识使我们能够很好地承担这项研究目标。 公共卫生相关性：我们预计，利用PSG常规收集的数据的创新且易于应用的临床工具将为未来的房颤筛查和治疗方法提供信息，因此将具有较高的临床影响。在房颤及其并发症高风险队列中收集的这些信息是制定降低房颤相关发病率和死亡率的新策略的关键。