Sleep-Related Respiratory and Electrophysiological Atrial Fibrillation Predictor

NHLBI心血管疾病司战略计划（2008年9月）强调需要 确定遏制房颤流行及其相关发病率的策略。识别心房肌电活动的新触发因素 包括睡眠呼吸障碍（SDB）和自主神经功能障碍在内的房颤（AF）至关重要，因为 已知的风险因素不能完全解释AF的发生率。虽然> 110万多导睡眠图（PSG） 每年在美国进行一次，所有这些都包括连续呼吸、EEG和ECG监测， 没有利用来自这些信号的可能潜在地识别处于未来AF风险中的患者的信息。我们 因此，我们的目标是从标准的夜间PSG评分和新的分析中识别标志物， 利用现有严格收集数据（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的结果; n~3000名参与者）提供了一个战略机会， 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显示，房颤相关的风险高出2-4倍， SDB（主要是中枢性呼吸暂停）以及SDB与氧化应激和全身炎症的关系， 先前的报道还没有阐明PSG标记物（包括ECG衍生的标记物）作为 偶发性房颤，未表征潜在的相关病理生理学。该研究旨在 目的：1）识别预测偶发性AF的基于PSG的SDB表型，包括中枢神经系统的相对贡献。 呼吸暂停和周期性呼吸与阻塞性呼吸暂停，并探讨全身炎症和 氧化应激，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 它们作为偶发AF的预测因子的效用和3）开发和测试AF临床预测规则， 显著PSG和ECG标记AF预测因子。我们的团队有能力承担所给出的研究目标 我们在流行病学，睡眠医学，生物统计学，电生理学，动力系统和信号方面的专业知识 处理.

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.