Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA.
Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
Sleep Med. 2019 Dec;64:71-76. doi: 10.1016/j.sleep.2019.06.015. Epub 2019 Jul 4.
Patient heterogeneity is problematic for the accurate assessment and effective treatment of Hypersomnolence Disorder. Clustering analysis is a preferred approach for establishing homogenous subclassifications. Thus, this investigation aimed to identify more homogeneous subclassifications of Hypersomnolence Disorder through clustering analysis.
Patients undergoing polysomnography (PSG) and multiple sleep latency test (MSLT) assessment for hypersomnolence were recruited as part of a larger investigation. A sample of patients with Hypersomnolence Disorder was determined based on a post hoc chart review protocol. After removing persons with missing data, 62 participants were included in the analyses. Self-report total sleep time, Epworth Sleepiness Scale (ESS) score, and Sleep Inertia Questionnaire (SIQ) score were chosen as clustering variables to mirror Hypersomnolence Disorder diagnostic traits. A statistically-driven clustering process produced two clusters using Ward's D hierarchical approach. Clusters were compared across characteristics, self-report measures, PSG/MSLT results, and additional objective measures.
The resulting clusters differed across a variety of hypersomnolence-related subjective metrics and objective measurements. A more severe hypersomnolence phenotype was identified in a cluster that also had elevated depressive symptoms. This cluster endorsed significantly greater daytime sleepiness, sleep inertia, and functional impairment, while displaying longer sleep duration and worse vigilance.
These results provide growing support for a nosological reformulation of hypersomnolence associated with psychiatric disorders. Future research is necessary to solidify the conceptualization and characterization of unexplained hypersomnolence presenting with-and-without psychiatric illness.
患者异质性是准确评估和有效治疗嗜睡症的一个问题。聚类分析是建立同质亚分类的首选方法。因此，本研究旨在通过聚类分析确定嗜睡症更同质的亚分类。
正在接受多导睡眠图（PSG）和多次睡眠潜伏期试验（MSLT）评估以确定嗜睡症的患者作为更大规模研究的一部分被招募。根据事后图表审查方案确定了嗜睡症患者的样本。在去除缺失数据的人员后，共有 62 名参与者纳入分析。选择自我报告的总睡眠时间、嗜睡量表（ESS）评分和睡眠惰性问卷（SIQ）评分作为聚类变量，以反映嗜睡症的诊断特征。使用 Ward 的 D 层次聚类方法进行了一项统计学驱动的聚类过程，生成了两个聚类。比较了聚类之间的特征、自我报告测量、PSG/MSLT 结果以及其他客观测量结果。
结果表明，与各种与嗜睡相关的主观指标和客观测量相比，聚类存在差异。在一个与抑郁症状升高相关的聚类中，确定了更严重的嗜睡表型。该聚类表现出明显更高的日间嗜睡、睡眠惰性和功能障碍，同时显示出更长的睡眠时间和更差的警觉性。
这些结果为嗜睡与精神障碍相关的分类学重新制定提供了越来越多的支持。需要进一步的研究来巩固对伴有和不伴有精神疾病的未解释性嗜睡的概念化和特征描述。
