Gender and ethnic differences in prevalence of self-reported insomnia among patients with obstructive sleep apnea

Subramanian, Shyam; Guntupalli, Bharat; P, Murugan T; Bopparaju, Swetha; Chanamolu, Sreenivas; Casturi, Lata; Surani, Salim

doi:10.1007/s11325-010-0426-4

Cited by 67 publications

(39 citation statements)

References 13 publications

(14 reference statements)

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“…Among individuals with a presenting complaint related to sleep apnea (eg, snoring, excessive daytime sleepiness, nocturnal breathing issues), the co-occurrence of insomnia varies between 6% and 84%. 25–30,33,35,36,40,42,43 By contrast, in those seeking evaluation for insomnia, rates of co-occurring sleep apnea ranged from 7% to 69%. 31,32,34,37–39,41 Of note, these reports may underestimate the true rates of comorbidity.…”

Section: Clinical Features and Associationsmentioning

confidence: 99%

Insomnia and Obstructive Sleep Apnea

Ong

Crawford

2013

Sleep Medicine Clinics

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Section: Clinical Features and Associationsmentioning

confidence: 99%

Insomnia and Obstructive Sleep Apnea

Ong

Crawford

2013

Sleep Medicine Clinics

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“…They suggest for example, that in OSA patients with insomnia, combination therapy (e.g., cognitive behavioral therapy and CPAP) or non-PAP therapies rather than CPAP could be explored. Supporting this notion, are studies of insomnia timing subtypes in OSA, with both sleep initiation and late night insomnia patients being less sleepy, less adherent and responsive to CPAP therapy, in contrast to patients with middle of the night insomnia [107, 108]. In addition, these studies provide a basis for more rational patient selection in clinical trial design.…”

Section: Potential Clinically Relevant Osa Phenotypesmentioning

confidence: 99%

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

Zinchuk

Gentry

Concato

et al. 2017

Sleep Medicine Reviews

233

153

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Summmary 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.

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“…Obstructive sleep apnea syndrome (OSA), along with insomnia, is the most common sleep disorder in adults [6]. The prevalence of OSA in Korean male and female adults (aged 40-69 years) is estimated as 27% and 16%, respectively [7].…”

Section: Introductionmentioning

confidence: 99%