Department of Respiratory Medicine, Tokyo Medical University, 6-7-1Nishi-shinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
Japan Somnology Center, Institute of Neuropsychiatry, Tokyo, Japan.
Sleep Breath. 2019 Dec;23(4):1087-1094. doi: 10.1007/s11325-019-01785-8. Epub 2019 Jan 29.
The upper airway (UA) anatomical collapsibility, UA muscle responsiveness, breathing control, and/or arousability are important contributing factors for obstructive sleep apnea (OSA). Differences in clinical manifestations of OSA are believed to reflect interactions among these factors. We aimed to classify OSA patients into subgroups based on polysomnographic (PSG) variables using cluster analysis and assess each subgroup's characteristics.
Men with moderate or severe OSA and without any concomitant heart or psychosomatic disease were recruited. A hierarchical cluster analysis was performed using variables including fraction of apnea, respiratory event duration, minimum oxygen saturation, arousal rate before termination, and frequency of respiratory events in the supine position. The impact of sleep stages or body position on PSG variables was also evaluated in each cluster.
A total of 210 men (mean age, 50.0 years, mean body mass index, 27.4 kg/m) were studied. The three subgroups that emerged from the analysis were defined as follows: cluster 1 (high fraction of apnea and severe desaturation (20%)), cluster 2 (high fraction of apnea and long event duration (31%)), and cluster 3 (low fraction of apnea (49%)). There were differences in the body mass index and apnea type between the three clusters. Sleep stages and/or body position affected PSG variables in each cluster.
Patients with OSA could be divided into three distinct subgroups based on PSG variables. This clustering may be used for assessing the pathophysiology of OSA to tailor individual treatment other than continuous positive airway pressure therapy.
上气道(UA)解剖结构塌陷性、UA 肌肉反应性、呼吸控制和/或觉醒能力是阻塞性睡眠呼吸暂停(OSA)的重要致病因素。OSA 的临床表现差异被认为反映了这些因素之间的相互作用。我们旨在使用聚类分析根据多导睡眠图(PSG)变量将 OSA 患者分为亚组,并评估每个亚组的特征。
招募了患有中度或重度 OSA 且无任何伴随心脏或身心疾病的男性。使用包括呼吸暂停分数、呼吸事件持续时间、最低氧饱和度、终止前觉醒率和仰卧位呼吸事件频率在内的变量进行层次聚类分析。还评估了每个聚类中睡眠阶段或体位对 PSG 变量的影响。
共纳入 210 名男性(平均年龄 50.0 岁,平均体重指数 27.4 kg/m)。分析得出的三个亚组分别定义为:亚组 1(呼吸暂停分数高且严重低氧饱和度(20%))、亚组 2(呼吸暂停分数高且事件持续时间长(31%))和亚组 3(呼吸暂停分数低(49%))。三个亚组之间在体重指数和呼吸暂停类型上存在差异。PSG 变量在每个聚类中受到睡眠阶段和/或体位的影响。
可以根据 PSG 变量将 OSA 患者分为三个不同的亚组。这种聚类可用于评估 OSA 的病理生理学,以调整连续气道正压通气治疗以外的个体化治疗。