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An Effective Model for Screening Obstructive Sleep Apnea: A Large-Scale Diagnostic Study

BACKGROUND: Obstructive sleep apnea (OSA) causes high morbidity and mortality and is independently associated with an increased likelihood of multiple complications. The diagnosis of OSA is presently time-consuming, labor-intensive and inaccessible. AIM: This study sought to develop a simple and eff...

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Autores principales: Zou, Jianyin, Guan, Jian, Yi, Hongliang, Meng, Lili, Xiong, Yuanping, Tang, Xulan, Su, Kaiming, Yin, Shankai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3846620/
https://www.ncbi.nlm.nih.gov/pubmed/24312494
http://dx.doi.org/10.1371/journal.pone.0080704
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author Zou, Jianyin
Guan, Jian
Yi, Hongliang
Meng, Lili
Xiong, Yuanping
Tang, Xulan
Su, Kaiming
Yin, Shankai
author_facet Zou, Jianyin
Guan, Jian
Yi, Hongliang
Meng, Lili
Xiong, Yuanping
Tang, Xulan
Su, Kaiming
Yin, Shankai
author_sort Zou, Jianyin
collection PubMed
description BACKGROUND: Obstructive sleep apnea (OSA) causes high morbidity and mortality and is independently associated with an increased likelihood of multiple complications. The diagnosis of OSA is presently time-consuming, labor-intensive and inaccessible. AIM: This study sought to develop a simple and efficient model for identifying OSA in Chinese adult population. METHODS: In this study, the efficiency of Epworth Sleepiness Scale (ESS) and a new established prediction model for screening OSA were evaluated in the test cohort (2,032 participants) and confirmed in an independent validation cohort (784 participants). RESULTS: In the test cohort, a high specificity (82.77%, 95% confidence interval [CI], 77.36–87.35) and a moderate sensitivity (61.65%, 95% CI, 59.35–63.91) were obtained at the threshold of nine for the ESS alone. Notably, sex-stratified analysis revealed different optimum cut-off points: nine for males and six for females. The new generated screening model, including age, waist circumference, ESS score, and minimum oxygen saturation (SaO(2)) as independent variables, revealed a higher sensitivity (89.13%, 95% CI, 87.60–90.53) and specificity (90.34%, 95% CI, 85.85–93.77) at the best cut-off point. Through receiver operating characteristics curve analysis, the area under the receiver operating characteristics curve of the model was found significantly larger than that of the ESS alone (0.955 vs. 0.774, P<0.0001). All these results were confirmed in the validation cohort. CONCLUSIONS: A practical screening model comprising minimum SaO(2) and other parameters could efficiently identify undiagnosed OSA from the high-risk patients. Additionally, a sex-specific difference should be considered if the ESS alone is used.
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spelling pubmed-38466202013-12-05 An Effective Model for Screening Obstructive Sleep Apnea: A Large-Scale Diagnostic Study Zou, Jianyin Guan, Jian Yi, Hongliang Meng, Lili Xiong, Yuanping Tang, Xulan Su, Kaiming Yin, Shankai PLoS One Research Article BACKGROUND: Obstructive sleep apnea (OSA) causes high morbidity and mortality and is independently associated with an increased likelihood of multiple complications. The diagnosis of OSA is presently time-consuming, labor-intensive and inaccessible. AIM: This study sought to develop a simple and efficient model for identifying OSA in Chinese adult population. METHODS: In this study, the efficiency of Epworth Sleepiness Scale (ESS) and a new established prediction model for screening OSA were evaluated in the test cohort (2,032 participants) and confirmed in an independent validation cohort (784 participants). RESULTS: In the test cohort, a high specificity (82.77%, 95% confidence interval [CI], 77.36–87.35) and a moderate sensitivity (61.65%, 95% CI, 59.35–63.91) were obtained at the threshold of nine for the ESS alone. Notably, sex-stratified analysis revealed different optimum cut-off points: nine for males and six for females. The new generated screening model, including age, waist circumference, ESS score, and minimum oxygen saturation (SaO(2)) as independent variables, revealed a higher sensitivity (89.13%, 95% CI, 87.60–90.53) and specificity (90.34%, 95% CI, 85.85–93.77) at the best cut-off point. Through receiver operating characteristics curve analysis, the area under the receiver operating characteristics curve of the model was found significantly larger than that of the ESS alone (0.955 vs. 0.774, P<0.0001). All these results were confirmed in the validation cohort. CONCLUSIONS: A practical screening model comprising minimum SaO(2) and other parameters could efficiently identify undiagnosed OSA from the high-risk patients. Additionally, a sex-specific difference should be considered if the ESS alone is used. Public Library of Science 2013-12-02 /pmc/articles/PMC3846620/ /pubmed/24312494 http://dx.doi.org/10.1371/journal.pone.0080704 Text en © 2013 Zou et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zou, Jianyin
Guan, Jian
Yi, Hongliang
Meng, Lili
Xiong, Yuanping
Tang, Xulan
Su, Kaiming
Yin, Shankai
An Effective Model for Screening Obstructive Sleep Apnea: A Large-Scale Diagnostic Study
title An Effective Model for Screening Obstructive Sleep Apnea: A Large-Scale Diagnostic Study
title_full An Effective Model for Screening Obstructive Sleep Apnea: A Large-Scale Diagnostic Study
title_fullStr An Effective Model for Screening Obstructive Sleep Apnea: A Large-Scale Diagnostic Study
title_full_unstemmed An Effective Model for Screening Obstructive Sleep Apnea: A Large-Scale Diagnostic Study
title_short An Effective Model for Screening Obstructive Sleep Apnea: A Large-Scale Diagnostic Study
title_sort effective model for screening obstructive sleep apnea: a large-scale diagnostic study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3846620/
https://www.ncbi.nlm.nih.gov/pubmed/24312494
http://dx.doi.org/10.1371/journal.pone.0080704
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