Identifying subtypes of Hypersomnolence Disorder: a clustering analysis.

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.