Sleep medicine · 2019
Identifying subtypes of Hypersomnolence Disorder: a clustering analysis.
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Cook J D, Rumble M E, Plante D T
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.