scholarly journals Recognizable Clinical Subtypes of Obstructive Sleep Apnea After Ischemic Stroke: A Cluster Analysis

2021 ◽  
Vol Volume 13 ◽  
pp. 283-290
Author(s):  
Chung-Yao Chen ◽  
Chia-Ling Chen
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Ischemic Stroke ◽  
Sleep Apnea ◽  
Obstructive Sleep

scholarly journals Clinical phenotypes of obstructive sleep apnea after ischemic stroke: a cluster analysis

2019 ◽  
Vol 60 ◽  
pp. 178-181 ◽  
Author(s):  
Sonja G. Schütz ◽  
Lynda D. Lisabeth ◽  
Fatema Shafie-Khorassani ◽  
Erin Case ◽  
Brisa N. Sanchez ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Obstructive Sleep Apnea ◽  
Ischemic Stroke ◽  
Sleep Apnea ◽  
Clinical Phenotypes ◽  
Obstructive Sleep

Abstract TP221: Using Berlin Questionnaire and Epworth Sleepiness Scale to Identify Stroke Patients at Risk for the Sleep Apnea Syndrome

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Millene Camilo ◽  
Alan Eckeli ◽  
Heidi Sander ◽  
Regina Fernandes ◽  
Joao Leite ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Ischemic Stroke ◽  
Sleep Apnea ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Epworth Sleepiness Scale ◽  
Berlin Questionnaire ◽  
Stroke Patients ◽  
Obstructive Sleep

Background: Sleep-disordered breathing (SDB) is frequent in the acute phase of stroke. Obstructive sleep apnea (OSA) has been found in 62% of stroke patients. The impact of OSA is significant after ischemic stroke, including early neurological deterioration, poor functional outcome and increased long-term mortality. However, performing polysomnography (PSG) for all patients with acute stroke for diagnose OSA is still impracticable. Therefore clinical tools to select patients at higher risk for OSA would be essential. The aim of this study was to determine the validity of the Berlin Questionnaire (BQ) and the Epworth Sleepiness Scale (ESS) to identify stroke patients in whom the PSG would be indicated. Methods: Subjects with ischemic stroke were stratified into high and low risk groups for SDB using a BQ. The ESS ≥ 10 was used to define excessive daytime sleepiness. The BQ and ESS were administered to the relatives of stroke patients at hospital admission. All patients were submitted to a full overnight PSG at the first night after symptoms onset. OSA severity was measured by the apnea-hypopnea index (AHI). Results: We prospectively studied 40 ischemic stroke patients. The mean age was 62 ± 12.1 years and the obstructive sleep apnea (AHI ≥ 15) was present in 67.5%. On stratifying risk of OSA in these patients based on the QB, 77.5% belonged to the high-risk and 50% to the ESS ≥ 10. The sensitivity of QB was 85%, the specificity 35%, the positive predictive value 74% and the negative predictive value 55%. For ESS was respectively 63%, 85%, 89% and 52%. The diagnostic value of the BQ and ESS in combination to predict OSA had a sensitivity of 58%, a specificity of 89%, a positive predictive value of 95% and a negative predictive value of 38%. Conclusions: The QB even applied to the bed-partners of stroke patients is a useful screening tool for OSA.

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.

Fibrinogen Levels and Obstructive Sleep Apnea in Ischemic Stroke

2000 ◽  
Vol 162 (6) ◽  
pp. 2039-2042 ◽  
Author(s):  
THOMAS E. WESSENDORF ◽  
ALFRED F. THILMANN ◽  
YOU-MING WANG ◽  
ANDREAS SCHREIBER ◽  
NIKOLAUS KONIETZKO ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Ischemic Stroke ◽  
Sleep Apnea ◽  
Obstructive Sleep

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
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