Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis
Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis
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DOI：
10.1371/journal.pone.0157318
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发表时间：
2016-06-17
期刊：
影响因子：
3.7
通讯作者：
Pepin, Jean-Louis
中科院分区：
文献类型：
--
作者：
Bailly, Sebastien;Destors, Marie;Pepin, Jean-Louis
阻塞性睡眠呼吸暂停的分类是基于睡眠研究标准，可能无法充分捕捉疾病的异质性。改善的表型可以改善预后预测和帮助选择治疗策略。目的：本研究采用聚类分析，探讨阻塞性睡眠呼吸暂停的临床集群。MethodsAn升序层次聚类分析进行基线症状，体检，危险因素暴露和共病从18,263名参与者在OSFP（法国国家登记处的睡眠呼吸暂停）。使用比值比评估与给定聚类相关的标准的概率，通过单变量logistic回归确定。结果如下：确定了六个集群，其中患者在年龄，性别，症状，肥胖，合并症和环境风险因素方面差异很大。集群之间的主要显着差异是最小的症状与睡眠阻塞性睡眠呼吸暂停患者，瘦与肥胖，肥胖患者之间的不同组合的共病和环境的风险factors.ConclusionsOur聚类分析确定了六个不同的集群阻塞性睡眠呼吸暂停。我们的研究结果强调了阻塞性睡眠呼吸暂停患者在临床表现、危险因素和后果方面的高度异质性。这可能有助于研究和临床实践，以验证新的预防方案，诊断和治疗策略的决定。
BackgroundThe classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies. Objectives: This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea.MethodsAn ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression. Results: Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors.ConclusionsOur cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies.