What is the clinical evidence from this study?

Study design: Cross-Sectional. Population: Obstructive Sleep Apnea (n=18263). Intervention: Cluster analysis. Primary outcome: Identification of clinical clusters of obstructive sleep apnea.

June 17, 2016Open Access

Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis

Key Result

A cluster analysis of 18,263 patients with obstructive sleep apnea identified six distinct clinical clusters varying considerably in age, symptoms, obesity, co-morbidities, and risk factors.

Study Design

Type

Cross-Sectional (n=18,263)

Multicenter

Yes

Structured PICO

Population

18,263 adults with obstructive sleep apnea syndrome (AHI > 15 events/hour or ODI > 15 events/hour), median age 59, 73.8% male, from the French national registry of sleep apnea (OSFP).

Outcome

Identification of clinical clusters of obstructive sleep apnea based on baseline symptoms, physical examination, risk factor exposure, and co-morbidities

Cluster analysis of over 18,000 patients with obstructive sleep apnea identified six distinct clinical phenotypes, highlighting significant heterogeneity that may guide personalized therapeutic strategies and cardiovascular risk management.

Limitations

The cut-off for AHI was >15 events per hour, which excluded patients with mild OSA (AHI 5-14).
The cluster analysis was based on cross-sectional diagnosis visit data and does not capture how OSA and individual responses evolve over time.
The study was not designed to separate the independent effects of obesity per se from those related to OSA on cardiovascular comorbidities.

Abstract

BACKGROUND: The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies. OBJECTIVES: This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea. METHODS: An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression. RESULTS: Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors. CONCLUSIONS: Our cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies.

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