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Correspondence |

Relationship Between OSA Clinical Phenotypes and CPAP Treatment Outcomes FREE TO VIEW

Frédéric Gagnadoux, MD, PhD; Marc Le Vaillant, PhD; Audrey Paris, MD, PhD; Thierry Pigeanne, MD; Laurence Leclair-Visonneau, MD; Acya Bizieux-Thaminy, MD; Claire Alizon, MD; Marie-Pierre Humeau, MD; Xuan-Lan Nguyen, MD; Béatrice Rouault, MD; Wojciech Trzepizur, MD, PhD; Nicole Meslier, MD
Author and Funding Information

CORRESPONDENCE TO: Frédéric Gagnadoux, MD, PhD, Département de Pneumologie, Université d'Angers, CHU Angers, 4 rue Larrey, 49033 Angers Cedex, France


Copyright 2016, American College of Chest Physicians. All Rights Reserved.


Chest. 2016;149(1):288-290. doi:10.1016/j.chest.2015.09.032
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There is a growing awareness of heterogeneity between patients with OSA in terms of symptoms and comorbidities. The aim of this study was to identify clinically meaningful OSA phenotypes by means of cluster analysis and to evaluate their relationship with relevant CPAP outcomes.

Latent class analysis, was used to identify phenotypes based on 13 clinically relevant variables in 5,983 patients with newly diagnosed moderate-to-severe OSA from the Institut de Recherche en Santé Respiratoire des Pays de la Loire multicenter prospective sleep cohort. Further methodological details are found in e-Appendix 1 and e-Table 1. Five distinct clusters with marked clinical differences were identified (Fig 1, e-Tables 2, 3). Cluster 1 was characterized by a marked female predominance, high rates of insomnia complaints, depressive symptoms, obesity, and associated comorbidities. Patients from clusters 2 and 3 had marked typical nocturnal and diurnal OSA symptoms and frequent depressive symptoms. Cluster 2 differed from cluster 3 by a male predominance and more frequent comorbidities. Patients in cluster 4 had nocturnal OSA symptoms and insomnia complaints but a low prevalence of excessive daytime sleepiness, depressive symptoms, and comorbidities. Cluster 5 included a marked predominance of minimally symptomatic male patients older than 65 years with a high rate of comorbidities.

Figure Jump LinkFigure 1 Prevalence of each variable according to the clusters identified by latent class analysis in 5,983 patients with moderate-to-severe OSA that was newly diagnosed. Each colored line represents a variable with prevalence ranging from 0% (yellow) to 100% (red).Grahic Jump Location

Treatment outcomes were then compared across clusters in the subgroup of patients in whom CPAP had been prescribed for at least 6 months. A strong agreement (kappa 0.92) was observed between OSA clusters identified in the entire baseline population (n = 5,983) and in the CPAP follow-up population (n = 3,090; e-Fig 1). CPAP treatment success was defined as daily CPAP use ≥4 h and either a decrease in Epworth sleepiness score (ESS) ≥4 points in patients with a baseline value ≥11 and/or an increase of at least 7 points in the energy/vitality component score of the Short Form 36 questionnaire. After adjustment for socioeconomic status, baseline apnea-hypopnea index, and ESS, patients from clusters 1, 4, and 5 that we propose to label as “female OSA,” “mildly symptomatic OSA,” and “comorbid OSA,” respectively, had a lower likelihood of CPAP treatment success than patients with “severe OSA syndrome” from cluster 3 (Table 1, e-Table 4).

Table Graphic Jump Location
Table 1 Unadjusted and Adjusted ORs (95% CI) for the Success of CPAP Treatment Associated With OSA Clusters in the Subgroup of Patients in Whom CPAP Had Been Prescribed for at Least 6 Months (n = 3,090)

Model 1 = adjusted for marital, educational, and employment status; model 2 = adjusted for marital, educational, and employment status and apnea-hypopnea index; model 3 = adjusted for marital, educational, and employment status; apnea-hypopnea index; and baseline Epworth sleepiness score.

Our findings suggest that cluster analysis provides an opportunity for broader than usual pretreatment clinical characterization of patients with OSA. The longitudinal association between OSA clusters and CPAP treatment outcomes remained significant after adjusting for criteria commonly used to assess OSA severity and to prescribe CPAP therapy, including socioeconomic status, apnea-hypopnea index, and ESS.,, These findings suggest that the proposed subtype classification provides relevant prognostic information regarding CPAP treatment outcomes not provided by these clinical criteria alone.

Ye L. .Pien G.W. .Ratcliffe S.J. .et al The different clinical faces of obstructive sleep apnoea: a cluster analysis. Eur Respir J. 2014;44:1600-1607 [PubMed]journal. [CrossRef] [PubMed]
 
Sipsma H.L. .Falb K.L. .Willie T. .et al Violence against Congolese refugee women in Rwanda and mental health: a cross-sectional study using latent class analysis. BMJ Open. 2015;5:e006299- [PubMed]journal. [CrossRef] [PubMed]
 
Sawyer A.M. .Gooneratne N.S. .Marcus C.L. .Ofer D. .Richards K.C. .Weaver T.E. . A systematic review of CPAP adherence across age groups: clinical and empiric insights for developing CPAP adherence interventions. Sleep Med Rev. 2011;15:343-356 [PubMed]journal. [CrossRef] [PubMed]
 
Crawford M.R. .Espie C.A. .Bartlett D.J. .Grunstein R.R. . Integrating psychology and medicine in CPAP adherence—new concepts? Sleep Med Rev. 2013;18:123-139 [PubMed]journal. [PubMed]
 
Gagnadoux F. .Le Vaillant M. .Goupil F. .et al Influence of marital status and employment status on long-term adherence with continuous positive airway pressure in sleep apnea patients. PLoS One. 2011;6:e22503- [PubMed]journal. [CrossRef] [PubMed]
 

Figures

Figure Jump LinkFigure 1 Prevalence of each variable according to the clusters identified by latent class analysis in 5,983 patients with moderate-to-severe OSA that was newly diagnosed. Each colored line represents a variable with prevalence ranging from 0% (yellow) to 100% (red).Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 Unadjusted and Adjusted ORs (95% CI) for the Success of CPAP Treatment Associated With OSA Clusters in the Subgroup of Patients in Whom CPAP Had Been Prescribed for at Least 6 Months (n = 3,090)

Model 1 = adjusted for marital, educational, and employment status; model 2 = adjusted for marital, educational, and employment status and apnea-hypopnea index; model 3 = adjusted for marital, educational, and employment status; apnea-hypopnea index; and baseline Epworth sleepiness score.

References

Ye L. .Pien G.W. .Ratcliffe S.J. .et al The different clinical faces of obstructive sleep apnoea: a cluster analysis. Eur Respir J. 2014;44:1600-1607 [PubMed]journal. [CrossRef] [PubMed]
 
Sipsma H.L. .Falb K.L. .Willie T. .et al Violence against Congolese refugee women in Rwanda and mental health: a cross-sectional study using latent class analysis. BMJ Open. 2015;5:e006299- [PubMed]journal. [CrossRef] [PubMed]
 
Sawyer A.M. .Gooneratne N.S. .Marcus C.L. .Ofer D. .Richards K.C. .Weaver T.E. . A systematic review of CPAP adherence across age groups: clinical and empiric insights for developing CPAP adherence interventions. Sleep Med Rev. 2011;15:343-356 [PubMed]journal. [CrossRef] [PubMed]
 
Crawford M.R. .Espie C.A. .Bartlett D.J. .Grunstein R.R. . Integrating psychology and medicine in CPAP adherence—new concepts? Sleep Med Rev. 2013;18:123-139 [PubMed]journal. [PubMed]
 
Gagnadoux F. .Le Vaillant M. .Goupil F. .et al Influence of marital status and employment status on long-term adherence with continuous positive airway pressure in sleep apnea patients. PLoS One. 2011;6:e22503- [PubMed]journal. [CrossRef] [PubMed]
 
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