Polysomnography Reveals 7 Types of Obstructive Sleep Apnea

Polysomnography Reveals 7 Types of Obstructive Sleep Apnea

NEW YORK (Reuters Health) – Polysomnography can identify 7 obstructive sleep apnea (OSA) phenotypes with differing cardiovascular implications, researchers report.

Most epidemiological studies of OSA and cardiovascular disease outcomes rely primarily on the apnea-hypopnea index (AHI), which represents a small fraction of the physiological data generated by polysomnography.

Dr. Henry K. Yaggi from Yale University School of Medicine, New Haven, Connecticut and colleagues used cluster analysis to investigate whether polysomnographic data could sort patients into different phenotypes according to their risk of adverse cardiovascular outcomes: acute coronary syndrome (ACS), transient ischemic attack (TIA), stroke, and death.

They used polysomnographic data in four pathophysiological domains – sleep architecture disturbance, autonomic dysregulation, breathing disturbance, and hypoxia – from 1,247 veterans who underwent OSA evaluation.

Patient-level cluster analysis resulted in seven OSA subgroups: A-mild; B-periodic limb movements of sleep (PLMS); C-non-REM and poor sleep; D-REM and hypoxia; E-hypopnea and hypoxia; F-arousal and poor sleep; and G-combined severe.

Clusters A and B comprised the conventional OSA severity categories of benign and mild, clusters C and D moderate OSA severity, and clusters E through G the severe conventional OSA category, according to the September 21 Thorax online report.

After adjustment for other factors, categories B, E and G were significantly associated with the combined primary outcome (ACS, TIA, stroke, or death from any cause).

In contrast, conventional, AHI-based OSA severity categories were not associated with the primary outcome.

“In summary, our study adds new evidence that polysomnographic heterogeneity exists within traditional OSA severity categories and that readily available data with unsupervised learning methods can be used to classify patients into subgroups reflecting different pathophysiological processes,” the researchers conclude. “Some of these subgroups capture the risk of adverse cardiovascular events or death otherwise missed by conventional AHI categorization, suggesting that additional measures – such as PLMS or respiratory events with hypoxia or arousals – may be important for risk stratification and treatment selection.”

“Future studies are needed to evaluate whether unsupervised learning methods can be applied in other populations and multidimensional datasets to better understand pathophysiology, improve prognostication and identify targeted treatments for patients with OSA,” they add.

Dr. Yaggi did not respond to a request for comment.

SOURCE: http://bit.ly/2yZy6e2

Thorax 2017.

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