scholarly journals Cognitive profiles in obstructive sleep apnea: a cluster analysis in sleep clinic and community samples

2020 ◽  
Vol 16 (9) ◽  
pp. 1493-1505 ◽  
Author(s):  
Michelle Olaithe ◽  
Maria Pushpanathan ◽  
David Hillman ◽  
Peter R. Eastwood ◽  
Michael Hunter ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Cognitive Profiles ◽  
Obstructive Sleep

A clinic-based cluster analysis in patients with moderate-severe obstructive sleep apnea (OSA) in Chile

2020 ◽  
Vol 73 ◽  
pp. 16-22
Author(s):  
Gonzalo Labarca ◽  
Jorge Dreyse ◽  
Constanza Salas ◽  
Alexia Schmidt ◽  
Francisca Rivera ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Severe Obstructive Sleep Apnea ◽  
Obstructive Sleep

scholarly journals 0577 Cluster Analysis for the Association Between Obstructive Sleep Apnea Phenotypes: A Population-Based Longitudinal Study

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A221-A221
Author(s):  
P F Tempaku ◽  
L O Silva ◽  
T M Guimaraes ◽  
T A Vidigal ◽  
V D’Almeida ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Latent Class ◽  
Cluster Solution ◽  
Optimal Care ◽  
Sleep Study ◽  
Disturbed Sleep ◽  
Obstructive Sleep

Abstract Introduction The identification of subgroups of obstructive sleep apnea (OSA) is critical to understand disease causality 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. Furthermore, we aimed to analyze whether subgroups remain after 8 years. Methods We used data derived from the Sao Paulo Epidemiologic Sleep Study (EPISONO) cohort, which was followed over 8 years. All individuals underwent polysomnography, answered questionnaires and had their blood collected for biochemical exams. OSA was defined according to an AHI equal or greater than 15 events per hour. Cluster analysis was performed using latent class analysis (LCA). Results Of the 1,042 individuals in the EPISONO baseline cohort, 68.3% accepted to participate in the follow-up study (n=712). We were able to replicate the OSA 3-cluster solution observed in previous studies: disturbed sleep, minimally symptomatic and excessively sleepy in both baseline (35.5%, 45.4% and 19.1%, respectively) and follow-up studies (41.9%, 43.4% and 14.8%, respectively). 44.8% 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). The optimal cluster solution for our sample based on Bayesian information criterion (BIC) was 2 clusters for baseline (disturbed sleep and excessively sleepy) and 3 clusters for follow-up (disturbed sleep, minimally symptomatic and excessively sleepy). Conclusion The results found replicate and confirm previously identified clinical clusters in OSA even in a longitudinal analysis. Support This work was supported by grants from AFIP, FAPESP and CAPES.

Abstract TP215: Clinical Phenotypes of Obstructive Sleep Apnea in Stroke Patients: A Cluster Analysis

Stroke ◽  
2019 ◽  
Vol 50 (Suppl_1) ◽  
Author(s):  
Sonja G Schütz ◽  
Fatema Shafie-Khorassani ◽  
Erin Case ◽  
Brisa N Sanchez ◽  
Lynda D Lisabeth ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Stroke Patients ◽  
Clinical Phenotypes ◽  
Obstructive Sleep

scholarly journals Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis

PLoS ONE ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. e0157318 ◽  
Author(s):  
Sébastien Bailly ◽  
Marie Destors ◽  
Yves Grillet ◽  
Philippe Richard ◽  
Bruno Stach ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Obstructive Sleep

scholarly journals Recognizable clinical subtypes of obstructive sleep apnea across international sleep centers: a cluster analysis

SLEEP ◽  
2018 ◽  
Vol 41 (3) ◽  
Author(s):  
Brendan T Keenan ◽  
Jinyoung Kim ◽  
Bhajan Singh ◽  
Lia Bittencourt ◽  
Ning-Hung Chen ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Obstructive Sleep ◽  
Sleep Centers

Hypoxemic features of obstructive sleep apnea and the risk of mortality: a cluster analysis

2020 ◽  
Author(s):  
Gonzalo Labarca ◽  
Jorge Jorquera ◽  
Jorge Dreyse ◽  
Constanza Salas ◽  
Francisca Letelier
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Risk Of Mortality ◽  
Obstructive Sleep
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