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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2023
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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. |
format | Online Article Text |
id | pubmed-10120827 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Dove |
record_format | MEDLINE/PubMed |
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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