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A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study

PURPOSE: Snoring patients, as a high-risk group for OSA, are prone to the combination of severe OSA and face serious health threats. The aim of our study was to develop and validate a nomogram to predict the occurrence of severe OSA in snorers, in order to improve the diagnosis rate and treatment ra...

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Autores principales: Teng, Gang, Zhang, Rui, Zhou, Jing, Wang, Yuanyuan, Zhang, Nianzhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120827/
https://www.ncbi.nlm.nih.gov/pubmed/37090896
http://dx.doi.org/10.2147/NSS.S406384
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author Teng, Gang
Zhang, Rui
Zhou, Jing
Wang, Yuanyuan
Zhang, Nianzhi
author_facet Teng, Gang
Zhang, Rui
Zhou, Jing
Wang, Yuanyuan
Zhang, Nianzhi
author_sort Teng, Gang
collection PubMed
description PURPOSE: Snoring patients, as a high-risk group for OSA, are prone to the combination of severe OSA and face serious health threats. The aim of our study was to develop and validate a nomogram to predict the occurrence of severe OSA in snorers, in order to improve the diagnosis rate and treatment rate in this population. PATIENTS AND METHODS: A training cohort of 464 snoring patients treated at our institution from May 2021 to October 2022 was divided into severe OSA and non-severe OSA groups. Univariate and multivariate logistic regression were used to identify potential predictors of severe OSA, and a nomogram model was constructed. An external hospital cohort of 210 patients was utilized as an external validation cohort to test the model. Area under the receiver operating characteristic curve, calibration curve, and decision curve analyses were used to assess the discriminatory power, calibration, and clinical utility of the nomogram, respectively. RESULTS: Multivariate logistic regression demonstrated that body mass index, Epworth Sleepiness Scale total score, smoking history, morning dry mouth, dream recall, and hypertension were independent predictors of severe OSA. The area under the curve (AUC) of the nomogram constructed from the above six factors is 0.820 (95% CI: 0.782–0.857). The Hosmer-Lemeshow test showed that the model had a good fit (P = 0.972). Both the calibration curve and decision curve of the nomogram demonstrated the corresponding dominance. Moreover, external validation further confirmed the reliability of the predicted nomograms (AUC=0.805, 95% CI: 0.748–0.862). CONCLUSION: A nomogram predicting the occurrence of severe OSA in snoring patients was constructed and validated with external data for the first time, and the findings all confirmed the validity of the model. This may help to improve existing clinical decision making, especially at institutions that do not yet have devices for diagnosing OSA.
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spelling pubmed-101208272023-04-22 A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study Teng, Gang Zhang, Rui Zhou, Jing Wang, Yuanyuan Zhang, Nianzhi Nat Sci Sleep Original Research PURPOSE: Snoring patients, as a high-risk group for OSA, are prone to the combination of severe OSA and face serious health threats. The aim of our study was to develop and validate a nomogram to predict the occurrence of severe OSA in snorers, in order to improve the diagnosis rate and treatment rate in this population. PATIENTS AND METHODS: A training cohort of 464 snoring patients treated at our institution from May 2021 to October 2022 was divided into severe OSA and non-severe OSA groups. Univariate and multivariate logistic regression were used to identify potential predictors of severe OSA, and a nomogram model was constructed. An external hospital cohort of 210 patients was utilized as an external validation cohort to test the model. Area under the receiver operating characteristic curve, calibration curve, and decision curve analyses were used to assess the discriminatory power, calibration, and clinical utility of the nomogram, respectively. RESULTS: Multivariate logistic regression demonstrated that body mass index, Epworth Sleepiness Scale total score, smoking history, morning dry mouth, dream recall, and hypertension were independent predictors of severe OSA. The area under the curve (AUC) of the nomogram constructed from the above six factors is 0.820 (95% CI: 0.782–0.857). The Hosmer-Lemeshow test showed that the model had a good fit (P = 0.972). Both the calibration curve and decision curve of the nomogram demonstrated the corresponding dominance. Moreover, external validation further confirmed the reliability of the predicted nomograms (AUC=0.805, 95% CI: 0.748–0.862). CONCLUSION: A nomogram predicting the occurrence of severe OSA in snoring patients was constructed and validated with external data for the first time, and the findings all confirmed the validity of the model. This may help to improve existing clinical decision making, especially at institutions that do not yet have devices for diagnosing OSA. Dove 2023-04-17 /pmc/articles/PMC10120827/ /pubmed/37090896 http://dx.doi.org/10.2147/NSS.S406384 Text en © 2023 Teng et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Teng, Gang
Zhang, Rui
Zhou, Jing
Wang, Yuanyuan
Zhang, Nianzhi
A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study
title A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study
title_full A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study
title_fullStr A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study
title_full_unstemmed A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study
title_short A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study
title_sort prediction nomogram for severe obstructive sleep apnea in snoring patients: a retrospective study
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120827/
https://www.ncbi.nlm.nih.gov/pubmed/37090896
http://dx.doi.org/10.2147/NSS.S406384
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