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Sleep Med Rev. 2017 Oct;35:113-123. doi: 10.1016/j.smrv.2016.10.002. Epub 2016 Oct 12.

Phenotypes in obstructive sleep apnea: A definition, examples and evolution of approaches.

Author information

1
Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA. Electronic address: andrey.zinchuk@yale.edu.
2
Cushing/Whitney Medical Library, Yale University School of Medicine, New Haven, CT, USA.
3
Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA; Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT, USA.

Abstract

Obstructive sleep apnea (OSA) is a complex and heterogeneous disorder and the apnea hypopnea index alone can not capture the diverse spectrum of the condition. Enhanced phenotyping can improve prognostication, patient selection for clinical trials, understanding of mechanisms, and personalized treatments. In OSA, multiple condition characteristics have been termed "phenotypes." To help classify patients into relevant prognostic and therapeutic categories, an OSA phenotype can be operationally defined as: "A category of patients with OSA distinguished from others by a single or combination of disease features, in relation to clinically meaningful attributes (symptoms, response to therapy, health outcomes, quality of life)." We review approaches to clinical phenotyping in OSA, citing examples of increasing analytic complexity. Although clinical feature based OSA phenotypes with significant prognostic and treatment implications have been identified (e.g., excessive daytime sleepiness OSA), many current categorizations lack association with meaningful outcomes. Recent work focused on pathophysiologic risk factors for OSA (e.g., arousal threshold, craniofacial morphology, chemoreflex sensitivity) appears to capture heterogeneity in OSA, but requires clinical validation. Lastly, we discuss the use of machine learning as a promising phenotyping strategy that can integrate multiple types of data (genomic, molecular, cellular, clinical) to identify unique, meaningful OSA phenotypes.

KEYWORDS:

Cluster analysis; Obstructive sleep apnea; Personalized medicine; Phenotype; Positional; Rapid eye movement (REM) related

PMID:
27815038
PMCID:
PMC5389934
DOI:
10.1016/j.smrv.2016.10.002
[Indexed for MEDLINE]
Free PMC Article

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