Sleep-Related Respiratory and Electrophysiological Atrial Fibrillation Predictor

说明(由申请人提供)：NHLBI心血管疾病部门战略计划(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。识别包括睡眠呼吸紊乱(SDB)和自主神经功能障碍在内的房颤(AF)的新触发因素是至关重要的，因为已知的危险因素并不能完全解释房颤的发生。尽管美国每年进行110万次多导睡眠图(PSG)，所有这些都包括持续的呼吸、脑电和心电监测，但从这些信号中获得的可能识别患者未来房颤风险的信息并未得到利用。因此，我们的目标是从标准的夜间PSG评分和对PSG心电通道的新分析中识别预测房颤发生的标志物。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(老年男性睡眠障碍研究的结果；N~3000名参与者)，提供了一个战略机会，以评估PSG标志物在8年随访期内预测临床判定为房颤风险增加的个人的能力。尽管我们之前的横断面研究表明，与SDB(主要是中枢性呼吸暂停)相关的房颤的几率增加了2-4倍，SDB与氧化应激和全身炎症的关系，但先前的报告没有阐明PSG标记物(包括心电衍生标记物)作为房颤发生的预测因素的作用，也没有描述潜在的相关病理生理学特征。这项拟议的研究旨在：1)确定基于PSG的SDB表型预测房颤的发生，包括中枢性呼吸暂停和周期性呼吸对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调节作用；2)识别房颤异位和自主神经失衡的PSG导出型心电标记物，并评估其作为房颤事件预测因子的价值；3)开发和测试包含显著PSG和心电标记物房颤预测因子的房颤临床预测规则。鉴于我们在流行病学、睡眠医学、生物统计学、电生理学、动力系统和信号处理方面的专业知识，我们的团队处于有利地位，可以承担这项研究目标。 公共卫生相关性：我们预计，一种创新的、易于应用的临床工具，利用从PSG常规收集的数据，将为未来的房颤筛查和治疗方法提供信息，因此将具有很高的临床影响。这些信息是在房颤及其并发症的高危人群中收集的，对于开发降低房颤相关发病率和死亡率的新策略至关重要。

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.