Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea.

ABSTRACT:

Background

Obstructive sleep apnoea (OSA) is a heterogeneous disorder, and improved understanding of physiologic phenotypes and their clinical implications is needed. We aimed to determine whether routine polysomnographic data can be used to identify OSA phenotypes (clusters) and to assess the associations between the phenotypes and cardiovascular outcomes.

Methods

Cross-sectional and longitudinal analyses of a multisite, observational US Veteran (n=1247) cohort were performed. Principal components-based clustering was used to identify polysomnographic features in OSA's four pathophysiological domains (sleep architecture disturbance, autonomic dysregulation, breathing disturbance and hypoxia). Using these features, OSA phenotypes were identified by cluster analysis (K-means)

SUBMITTER: Zinchuk AV 

PROVIDER: S-EPMC6693344 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature