PURPOSE: The identification of subgroups of obstructive sleep apnea (OSA) is critical to understand disease outcome and treatment response and ultimately develop optimal care strategies customized for each subgroup. In this sense, we aimed to perform a cluster analysis to identify subgroups of individuals with OSA based on clinical parameters in the Epidemiological Sleep Study of São Paulo city (EPISONO). We aimed to analyze whether or not subgroups remain after 8 years, since there is not any evidence showing if these subtypes of clinical presentation of OSA in the same population can change overtime. METHODS: We used data derived from EPISONO cohort, which was followed over 8 years after baseline evaluation. All individuals underwent polysomnography, answered questionnaires, and had their blood collected for biochemical examinations. OSA was defined according to AHI ≥ 15 events/h. Cluster analysis was performed using latent class analysis (LCA). RESULTS: Of the 1042 individuals in the EPISONO cohort, 68% agreed to participate in the follow-up study (n = 712), and 704 were included in the analysis. We were able to replicate the OSA 3-cluster solution observed in previous studies: disturbed sleep, minimally symptomatic and excessively sleepy in both baseline (36%, 45% and 19%, respectively) and follow-up studies (42%, 43%, and 15%, respectively). The optimal cluster solution for our sample based on Bayesian information criterion (BIC) was 2 cluster for baseline (disturbed sleep and excessively sleepy) and 3 clusters for follow-up (disturbed sleep, minimally symptomatic, and excessively sleepy). A total of 45% of the participants migrated clusters between the two evaluations (and the factor associated with this was a greater delta-AHI (B = - 0.033, df = 1, p = 0.003). CONCLUSIONS: The results replicate and confirm previously identified clinical clusters in OSA which remain in the longitudinal analysis, with some percentage of migration between clusters.
PURPOSE: The identification of subgroups of obstructive sleep apnea (OSA) is critical to understand disease outcome and treatment response and ultimately develop optimal care strategies customized for each subgroup. In this sense, we aimed to perform a cluster analysis to identify subgroups of individuals with OSA based on clinical parameters in the Epidemiological Sleep Study of São Paulo city (EPISONO). We aimed to analyze whether or not subgroups remain after 8 years, since there is not any evidence showing if these subtypes of clinical presentation of OSA in the same population can change overtime. METHODS: We used data derived from EPISONO cohort, which was followed over 8 years after baseline evaluation. All individuals underwent polysomnography, answered questionnaires, and had their blood collected for biochemical examinations. OSA was defined according to AHI ≥ 15 events/h. Cluster analysis was performed using latent class analysis (LCA). RESULTS: Of the 1042 individuals in the EPISONO cohort, 68% agreed to participate in the follow-up study (n = 712), and 704 were included in the analysis. We were able to replicate the OSA 3-cluster solution observed in previous studies: disturbed sleep, minimally symptomatic and excessively sleepy in both baseline (36%, 45% and 19%, respectively) and follow-up studies (42%, 43%, and 15%, respectively). The optimal cluster solution for our sample based on Bayesian information criterion (BIC) was 2 cluster for baseline (disturbed sleep and excessively sleepy) and 3 clusters for follow-up (disturbed sleep, minimally symptomatic, and excessively sleepy). A total of 45% of the participants migrated clusters between the two evaluations (and the factor associated with this was a greater delta-AHI (B = - 0.033, df = 1, p = 0.003). CONCLUSIONS: The results replicate and confirm previously identified clinical clusters in OSA which remain in the longitudinal analysis, with some percentage of migration between clusters.
Authors: Rogerio Santos-Silva; Sergio Tufik; Silvia G Conway; Jose Augusto Taddei; Lia Rita A Bittencourt Journal: Sleep Med Date: 2009-02-18 Impact factor: 3.492
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Authors: Sergio Tufik; Rogerio Santos-Silva; Jose Augusto Taddei; Lia Rita Azeredo Bittencourt Journal: Sleep Med Date: 2010-04-01 Impact factor: 3.492
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