Phenotypes of patients with mild to moderate obstructive sleep apnoea as confirmed by cluster analysis

Authors

  • SIMON A. JOOSTEN,

    1. Department of Respiratory and Sleep Medicine, Monash Medical Centre, Melbourne
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  • KAIS HAMZA,

    1. School of Mathematical Sciences, Monash University, Melbourne
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  • SCOTT SANDS,

    1. Ritchie Centre for Baby Health Research, Monash Institute of Medical Research, Melbourne, Australia
    2. Brigham and Women's Hospital, Division of Sleep Medicine, Boston, Massachusetts, USA
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  • ANTHONY TURTON,

    1. Department of Respiratory and Sleep Medicine, Monash Medical Centre, Melbourne
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  • PHILIP BERGER,

    1. Ritchie Centre for Baby Health Research, Monash Institute of Medical Research, Melbourne, Australia
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  • GARUN HAMILTON

    Corresponding author
    1. Department of Respiratory and Sleep Medicine, Monash Medical Centre, Melbourne
    2. Ritchie Centre for Baby Health Research, Monash Institute of Medical Research, Melbourne, Australia
      Garun Hamilton, Department of Respiratory and Sleep Medicine, Monash Medical Centre, 246 Clayton Rd, Clayton 3168, Melbourne, Australia. Email: garun.hamilton@southernhealth.org.au
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Garun Hamilton, Department of Respiratory and Sleep Medicine, Monash Medical Centre, 246 Clayton Rd, Clayton 3168, Melbourne, Australia. Email: garun.hamilton@southernhealth.org.au

ABSTRACT

Background and objective:  Patients with OSA manifest different patterns of disease. However, this heterogeneity is more evident in patients with mild-moderate OSA than in those with severe disease and a high total AHI. We hypothesized that mild-moderate OSA can be categorized into discreet disease phenotypes, and the aim of this study was to comprehensively describe the pattern of OSA phenotypes through the use of cluster analysis techniques.

Methods:  The data for 1184 consecutive patients, collected over 24 months, was analysed. Patients with a total AHI of 5–30/h were categorized according to the sleep stage and position in which they were predominantly affected. This categorization was compared with one in which patients were grouped using a K-means clustering technique with log linear modelling and cross-tabulation.

Results:  Patients with mild-moderate OSA can be categorized according to polysomnographic parameters. This clinical categorization was validated by comparison with a categorization in which patients were grouped by unsupervised K-means cluster analysis. The clinical groups identified were: (i) rapid eye movement (REM) predominant OSA, 44.6%; (ii) non-REM predominant OSA, 18.9%; (iii) supine predominant OSA, 61.9%; and (iv) intermittent OSA, 12.4%. Patients categorized as having both REM and supine predominant OSA showed characteristics of both the REM predominant and supine predominant OSA groups.

Conclusions:  Patients with mild-moderate OSA show different polysomnographic phenotypes. This approach to categorization more appropriately reflects disease heterogeneity and the likely multiple pathophysiological processes involved in OSA.

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