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Original Article
Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea
  1. Andrey V Zinchuk1,
  2. Sangchoon Jeon2,
  3. Brian B Koo3,
  4. Xiting Yan1,
  5. Dawn M Bravata4,
  6. Li Qin5,
  7. Bernardo J Selim6,
  8. Kingman P Strohl7,
  9. Nancy S Redeker2,
  10. John Concato1,8,
  11. Henry K Yaggi1
  1. 1Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
  2. 2Division of Acute Care/Health Systems, Yale School of Nursing, Yale University, New Haven, Connecticut, USA
  3. 3Department of Neurology, Yale University, New Haven, Connecticut, USA
  4. 4Departments of Neurology and Internal Medicine, Richard L. Roudenbush VA Medical Center, Indianapolis, Indiana, USA
  5. 5Center for Outcomes Research and Evaluation, Yale University School of Medicine, New Haven, Connecticut, USA
  6. 6Section of Pulmonary, Critical Care, and Sleep Medicine, Mayo Clinic, Rochester, Minnesota, USA
  7. 7Section of Pulmonary, Critical Care, and Sleep Medicine, Case Western Reserve University, Cleveland, Ohio, USA
  8. 8Clinical Epidemiology Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, USA
  1. Correspondence to Dr Henry K Yaggi, Department of Medicine, Yale University School of Medicine, 300 Cedar Street, New Haven, CT 06443, USA; henry.yaggi{at}yale.edu

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). Cox survival analysis was used to evaluate longitudinal relationships between clusters and the combined outcome of incident transient ischaemic attack, stroke, acute coronary syndrome or death.

Results Seven patient clusters were identified based on distinguishing polysomnographic features: ‘mild’, ‘periodic limb movements of sleep (PLMS)’, ‘NREM and arousal’, ‘REM and hypoxia’, ‘hypopnoea and hypoxia’, ‘arousal and poor sleep’ and ‘combined severe’. In adjusted analyses, the risk (compared with ‘mild’) of the combined outcome (HR (95% CI)) was significantly increased for ‘PLMS’, (2.02 (1.32 to 3.08)), ‘hypopnoea and hypoxia’ (1.74 (1.02 to 2.99)) and ‘combined severe’ (1.69 (1.09 to 2.62)). Conventional apnoea–hypopnoea index (AHI) severity categories of moderate (15≤AHI<30) and severe (AHI ≥30), compared with mild/none category (AHI <15), were not associated with increased risk.

Conclusions Among patients referred for OSA evaluation, routine polysomnographic data can identify physiological phenotypes that capture risk of adverse cardiovascular outcomes otherwise missed by conventional OSA severity classification.

  • obstructive sleep apnea (OSA)
  • cluster analysis
  • phenotype
  • heterogeneity
  • cardiovascular diseases
  • mortality

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Footnotes

  • Part of this work was presented at as a poster discussion presentation: Zinchuk AV, Jeon S, Koo BB, et al. Clinically Relevant Phenotypes of Obstructive Sleep Apnea. Am J Respir Crit Care Med 2016;193:A6380.

  • Contributors All authors contributed substantially to the conception, design, analysis or interpretation of the data in this study. AVZ, SJ and HKY had full access to all the data in the study and take responsibility for the integrity and the accuracy of the data analyses. AVZ, SJ, BBK, NSR, JC and HKY were involved in the interpretation of data. AVZ, BBK and HKY drafted the manuscript and all authors revised it critically for important intellectual content. All authors gave final approval of this version to be submitted.

  • Funding This work was supported by the VA Clinical Science Research and Development (CSR&D) Merit Review Program (CSRDS07), VA Cooperative Studies Program, NRSA Institutional training Grant from the NIH (5T32HL07778), Yale Center for Investigating Sleep Disturbance in Acute and Chronic Conditions (P20NRO14126) and Robert E. Leet and Clara Guthrie Patterson Trust Fellowship Program in Clinical Research, Bank of America, N.A., Trustee. JC is supported by the VA Cooperative Studies Program.

  • Competing interests None declared.

  • Ethics approval West Haven Veterans Affairs Institutional Review Board.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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