Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea
BackgroundOSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.936946/full |
| _version_ | 1798038237901488128 |
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| author | Xiangru Yan Liying Wang Chunguang Liang Huiying Zhang Ying Zhao Hui Zhang Haitao Yu Jinna Di |
| author_facet | Xiangru Yan Liying Wang Chunguang Liang Huiying Zhang Ying Zhao Hui Zhang Haitao Yu Jinna Di |
| author_sort | Xiangru Yan |
| collection | DOAJ |
| description | BackgroundOSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated in time. To enable timely screening of patients with moderate-to-severe OSA, we selected easily accessible variables to establish a risk prediction model.MethodWe collected 492 patients who had polysomnography (PSG), and divided them into the disease-free mild OSA group (control group), and the moderate-to-severe OSA group according to the PSG results. Variables entering the model were identified by random forest plots, univariate analysis, multicollinearity test, and binary logistic regression method. Nomogram were created based on the binary logistic results, and the area under the ROC curve was used to evaluate the discriminative properties of the nomogram model. Bootstrap method was used to internally validate the nomogram model, and calibration curves were plotted after 1,000 replicate sampling of the original data, and the accuracy of the model was evaluated using the Hosmer-Lemeshow goodness-of-fit test. Finally, we performed decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire (SBQ), and NoSAS score to assess clinical utility.ResultsThere are 6 variables entering the final prediction model, namely BMI, Hypertension, Morning dry mouth, Suffocating awake at night, Witnessed apnea, and ESS total score. The AUC of this prediction model was 0.976 (95% CI: 0.962–0.990). Hosmer-Lemeshow goodness-of-fit test χ2 = 3.3222 (P = 0.1899 > 0.05), and the calibration curve was in general agreement with the ideal curve. The model has good consistency in predicting the actual occurrence of moderate-to-severe risk, and has good prediction accuracy. The DCA shows that the net benefit of the nomogram model is higher than that of SBQ and NoSAS, with has good clinical utility.ConclusionThe prediction model obtained in this study has good predictive power for moderate-to-severe OSA and is superior to other prediction models and questionnaires. It can be applied to the community population for screening and to the clinic for prioritization of treatment. |
| first_indexed | 2024-04-11T21:37:29Z |
| format | Article |
| id | doaj.art-73566d19c36e4d898a3cafd2594e1e29 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-11T21:37:29Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-73566d19c36e4d898a3cafd2594e1e292022-12-22T04:01:41ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-08-011610.3389/fnins.2022.936946936946Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apneaXiangru Yan0Liying Wang1Chunguang Liang2Huiying Zhang3Ying Zhao4Hui Zhang5Haitao Yu6Jinna Di7Department of Nursing, Jinzhou Medical University, Jinzhou, ChinaDepartment of Nursing, Jinzhou Medical University, Jinzhou, ChinaDepartment of Nursing, Jinzhou Medical University, Jinzhou, ChinaSleep Monitoring Center, The First Hospital of Jinzhou Medical University, Jinzhou, ChinaDepartment of Nursing, Jinzhou Medical University, Jinzhou, ChinaDepartment of Nursing, Jinzhou Medical University, Jinzhou, ChinaDepartment of Nursing, Jinzhou Medical University, Jinzhou, ChinaRespiratory Medicine, The Third Hospital of Jinzhou Medical University, Jinzhou, ChinaBackgroundOSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated in time. To enable timely screening of patients with moderate-to-severe OSA, we selected easily accessible variables to establish a risk prediction model.MethodWe collected 492 patients who had polysomnography (PSG), and divided them into the disease-free mild OSA group (control group), and the moderate-to-severe OSA group according to the PSG results. Variables entering the model were identified by random forest plots, univariate analysis, multicollinearity test, and binary logistic regression method. Nomogram were created based on the binary logistic results, and the area under the ROC curve was used to evaluate the discriminative properties of the nomogram model. Bootstrap method was used to internally validate the nomogram model, and calibration curves were plotted after 1,000 replicate sampling of the original data, and the accuracy of the model was evaluated using the Hosmer-Lemeshow goodness-of-fit test. Finally, we performed decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire (SBQ), and NoSAS score to assess clinical utility.ResultsThere are 6 variables entering the final prediction model, namely BMI, Hypertension, Morning dry mouth, Suffocating awake at night, Witnessed apnea, and ESS total score. The AUC of this prediction model was 0.976 (95% CI: 0.962–0.990). Hosmer-Lemeshow goodness-of-fit test χ2 = 3.3222 (P = 0.1899 > 0.05), and the calibration curve was in general agreement with the ideal curve. The model has good consistency in predicting the actual occurrence of moderate-to-severe risk, and has good prediction accuracy. The DCA shows that the net benefit of the nomogram model is higher than that of SBQ and NoSAS, with has good clinical utility.ConclusionThe prediction model obtained in this study has good predictive power for moderate-to-severe OSA and is superior to other prediction models and questionnaires. It can be applied to the community population for screening and to the clinic for prioritization of treatment.https://www.frontiersin.org/articles/10.3389/fnins.2022.936946/fullmoderate-to-severe OSAprediction modelnomogramSBQNoSAS |
| spellingShingle | Xiangru Yan Liying Wang Chunguang Liang Huiying Zhang Ying Zhao Hui Zhang Haitao Yu Jinna Di Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea Frontiers in Neuroscience moderate-to-severe OSA prediction model nomogram SBQ NoSAS |
| title | Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea |
| title_full | Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea |
| title_fullStr | Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea |
| title_full_unstemmed | Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea |
| title_short | Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea |
| title_sort | development and assessment of a risk prediction model for moderate to severe obstructive sleep apnea |
| topic | moderate-to-severe OSA prediction model nomogram SBQ NoSAS |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.936946/full |
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