A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study
Gang Teng,1 Rui Zhang,1 Jing Zhou,1 Yuanyuan Wang,2 Nianzhi Zhang2 1Graduate School, Anhui University of Chinese Medicine, Hefei, Anhui, 230012, People’s Republic of China; 2Department of Respiratory Medicine, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, 23003...
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Dove Medical Press
2023-04-01
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| Series: | Nature and Science of Sleep |
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| Online Access: | https://www.dovepress.com/a-prediction-nomogram-for-severe-obstructive-sleep-apnea-in-snoring-pa-peer-reviewed-fulltext-article-NSS |
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| author | Teng G Zhang R Zhou J Wang Y Zhang N |
| author_facet | Teng G Zhang R Zhou J Wang Y Zhang N |
| author_sort | Teng G |
| collection | DOAJ |
| description | Gang Teng,1 Rui Zhang,1 Jing Zhou,1 Yuanyuan Wang,2 Nianzhi Zhang2 1Graduate School, Anhui University of Chinese Medicine, Hefei, Anhui, 230012, People’s Republic of China; 2Department of Respiratory Medicine, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, 230031, People’s Republic of ChinaCorrespondence: Nianzhi Zhang, Department of Respiratory Medicine, The First Affiliated Hospital of Anhui University of Chinese Medicine, No. 117 Meishan Road, Hefei, Anhui, 230031, People’s Republic of China, Tel/Fax +86-551-62850057, Email zhangnz@ahtcm.edu.cnPurpose: 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.Keywords: obstructive sleep apnea, snoring, risk factor, prediction model |
| first_indexed | 2024-04-09T17:23:02Z |
| format | Article |
| id | doaj.art-b0367238a75449e09b37712cd17a5212 |
| institution | Directory Open Access Journal |
| issn | 1179-1608 |
| language | English |
| last_indexed | 2024-04-09T17:23:02Z |
| publishDate | 2023-04-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Nature and Science of Sleep |
| spelling | doaj.art-b0367238a75449e09b37712cd17a52122023-04-18T19:32:08ZengDove Medical PressNature and Science of Sleep1179-16082023-04-01Volume 1523124383065A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective StudyTeng GZhang RZhou JWang YZhang NGang Teng,1 Rui Zhang,1 Jing Zhou,1 Yuanyuan Wang,2 Nianzhi Zhang2 1Graduate School, Anhui University of Chinese Medicine, Hefei, Anhui, 230012, People’s Republic of China; 2Department of Respiratory Medicine, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, 230031, People’s Republic of ChinaCorrespondence: Nianzhi Zhang, Department of Respiratory Medicine, The First Affiliated Hospital of Anhui University of Chinese Medicine, No. 117 Meishan Road, Hefei, Anhui, 230031, People’s Republic of China, Tel/Fax +86-551-62850057, Email zhangnz@ahtcm.edu.cnPurpose: 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.Keywords: obstructive sleep apnea, snoring, risk factor, prediction modelhttps://www.dovepress.com/a-prediction-nomogram-for-severe-obstructive-sleep-apnea-in-snoring-pa-peer-reviewed-fulltext-article-NSSobstructive sleep apneasnoringrisk factorprediction model |
| spellingShingle | Teng G Zhang R Zhou J Wang Y Zhang N A Prediction Nomogram for Severe Obstructive Sleep Apnea in Snoring Patients: A Retrospective Study Nature and Science of Sleep obstructive sleep apnea snoring risk factor prediction model |
| 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 | obstructive sleep apnea snoring risk factor prediction model |
| url | https://www.dovepress.com/a-prediction-nomogram-for-severe-obstructive-sleep-apnea-in-snoring-pa-peer-reviewed-fulltext-article-NSS |
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