Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea.
Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea.
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DOI：
10.1136/thoraxjnl-2017-210431
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发表时间：
2018-05
期刊：
影响因子：
10
通讯作者：
Yaggi HK
中科院分区：
文献类型：
--
作者：
Zinchuk AV;Jeon S;Koo BB;Yan X;Bravata DM;Qin L;Selim BJ;Strohl KP;Redeker NS;Concato J;Yaggi HK
阻塞性睡眠呼吸暂停(OSA)是一种异质性疾病，需要更好地了解生理表型及其临床意义。我们的目的是确定常规多导睡眠图数据是否可以用于识别OSA表型(簇)，并评估表型和心血管结果之间的关联。对一个多部位、观察性的美国退伍军人(n=1247)队列进行了横断面和纵向分析。采用基于主成分聚类的方法识别OSA四个病理生理域(睡眠结构障碍、自主神经调节障碍、呼吸障碍和低氧)的多导睡眠图特征。利用这些特征，通过聚类分析(K-Means)确定OSA的表型。COX生存分析用于评估集群之间的纵向关系和发生短暂性脑缺血发作、中风、急性冠脉综合征或死亡的综合结果。根据多导睡眠图的不同特征，确定了7组患者：“轻度”、“睡眠的周期性肢体运动(PLMS)”、“NREM和觉醒”、“REM和低氧”、“低呼吸和低氧”、“觉醒和睡眠不佳”和“重度结合”。在调整后的分析中，合并预后(HR(95%CI))的风险在“PLMS”(2.02(1.32~3.08))、“低呼吸和低氧”(1.74(1.02~2.99))和“综合重度”(1.69(1.09~2.62))中显著增加。传统的呼吸暂停低通气指数(AHI)严重程度分级为中度(15≤)和重度(AHI≥30)，与轻度/无分级(AHI和15)相比，与风险增加无关。在转诊进行OSA评估的患者中，常规的多导睡眠图数据可以识别生理表型，这些表型可以捕捉到常规OSA严重程度分类所遗漏的不良心血管结果的风险。
Obstructive sleep apnoea (OSA) is a heterogeneous disorder, and improved understanding of physiologic phenotypes and their clinical implications is needed. We aimed to determine whether routine polysomnographic data can be used to identify OSA phenotypes (clusters) and to assess the associations between the phenotypes and cardiovascular outcomes. Cross-sectional and longitudinal analyses of a multisite, observational US Veteran (n=1247) cohort were performed. Principal components-based clustering was used to identify polysomnographic features in OSA’s four pathophysiological domains (sleep architecture disturbance, autonomic dysregulation, breathing disturbance and hypoxia). Using these features, OSA phenotypes were identified by cluster analysis (K-means). Cox survival analysis was used to evaluate longitudinal relationships between clusters and the combined outcome of incident transient ischaemic attack, stroke, acute coronary syndrome or death. Seven patient clusters were identified based on distinguishing polysomnographic features: ‘mild’, ‘periodic limb movements of sleep (PLMS)’, ‘NREM and arousal’, ‘REM and hypoxia’, ‘hypopnoea and hypoxia’, ‘arousal and poor sleep’ and ‘combined severe’. In adjusted analyses, the risk (compared with ‘mild’) of the combined outcome (HR (95% CI)) was significantly increased for ‘PLMS’, (2.02 (1.32 to 3.08)), ‘hypopnoea and hypoxia’ (1.74 (1.02 to 2.99)) and ‘combined severe’ (1.69 (1.09 to 2.62)). Conventional apnoea–hypopnoea index (AHI) severity categories of moderate (15≤AHI<30) and severe (AHI ≥30), compared with mild/none category (AHI <15), were not associated with increased risk. Among patients referred for OSA evaluation, routine polysomnographic data can identify physiological phenotypes that capture risk of adverse cardiovascular outcomes otherwise missed by conventional OSA severity classification.
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发表时间：
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影响因子：
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