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战略。认识房颤的新触发因素 纤颤(AF)包括睡眠呼吸紊乱(SDB)和自主神经功能障碍是至关重要的，因为 已知的危险因素并不能完全解释房颤的发生。尽管&gt；有110万个多导睡眠图(PSG) 在美国每年进行一次，所有这些都包括持续的呼吸，脑电和心电监测， 来自这些信号的信息可能会潜在地识别患者未来发生房颤的风险，但这些信息并未被利用。我们的 因此，目标是从标准的夜间PSG评分和从新的分析中识别标记 预测发生房颤的PSG心电频道。充分利用现有严格收集的数据(PSG 包括心电监测、生物标志物等)和纵向队列研究的多中心基础设施 (老年男性睡眠障碍的结果研究；约3000名参与者)提供了一个战略机会 评估PSG标记物预测8岁以上临床诊断为房颤风险增加的个体的能力 年随访期。尽管我们之前的横断面研究显示房颤相关的几率要高出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， 以前的报告没有阐明包括心电衍生的标记物在内的PSG标记物作为预测因素的作用 发生房颤，尚未确定潜在的相关病理生理学特征。建议的研究是设计的 TO：1)确定预测房颤事件的基于PSG的SDB表型，包括 呼吸暂停和周期性呼吸与阻塞性呼吸暂停的比较，并探讨全身炎症和 氧化应激，2)确定心房异位和自主神经失衡的PSG心电标志物并评估 它们作为发生房颤的预测指标的有效性以及3)开发和测试房颤临床预测规则 显著的PSG和心电标记物房颤预测指标。我们团队处于有利地位，能够承担既定的研究目标 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 正在处理。

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.