Introduction: Obstructive sleep apnea (OSA) is heterogeneous and complex, but its severity is still based on the apnea-hypoapnea index (AHI). The present study explores using cluster analysis (CA), the additional information provided from routine polysomnography (PSG) to optimize OSA categorization.
Methods: Cross-sectional study of OSA subjects diagnosed by PSG in a tertiary hospital sleep unit during 2016-2020. PSG, demographical, clinical variables, and comorbidities were recorded. Phenotypes were constructed from PSG variables using CA. Results are shown as median (interquartile range).
Results: 981 subjects were studied: 41% females, age 56 years (45-66), overall AHI 23events/h (13-42) and body mass index (BMI) 30kg/m (27-34). Three PSG clusters were identified: Cluster 1: "Supine and obstructive apnea predominance" (433 patients, 44%). Cluster 2: "Central, REM and shorter-hypopnea predominance" (374 patients, 38%). Cluster 3: "Severe hypoxemic burden and higher wake after sleep onset" (174 patients, 18%). Based on classical OSA severity classification, subjects are distributed among the PSG clusters as severe OSA patients (AHI≥30events/h): 46% in cluster 1, 17% in cluster 2 and 36% in cluster 3; moderate OSA (15≤AHI<30events/h): 57% in cluster 1, 34% in cluster 2 and 9% in cluster 3; mild OSA (5≤AHI<15events/h): 28% in cluster 1, 68% in cluster 2 and 4% in cluster 3.
Conclusions: The CA identifies three specific PSG phenotypes that do not completely agree with classical OSA severity classification. This emphasized that using a simplistic AHI approach, the OSA severity is assessed by an incorrect or incomplete analysis of the heterogeneity of the disorder.
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