ArticleThe clinical respiratory journal2023
Nomogram to diagnosis of obstructive sleep apnoea-hypopnoea syndrome in high-risk Chinese adult patients.
Article in The clinical respiratory journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development and Validation of Prediction Models for Severe Obstructive Sleep Apnea Based on Periodic Health Examinations.The clinical respiratory journal · 2026Article
- Development and validation of a diagnostic model for obstructive sleep apnea.Sleep & breathing = Schlaf & Atmung · 2026Article
- Effects of time-restricted eating on weight loss, sleep, and quality of life in obstructive sleep apnea-hypopnea syndrome (OSAHS) patients with obesity: a randomized controlled trial.Frontiers in nutrition · 2026Article
- Nomogram to diagnosis of obstructive sleep apnoea-hypopnoea syndrome in high-risk Chinese adult patients.The clinical respiratory journal · 2023Article
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8 authors.
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Abstract
introductionMany scales are designed to screen for obstructive sleep apnoea-hypopnoea syndrome (OSAHS); however, there is a lack of an efficiently and easily diagnostic tool, especially for Chinese. Therefore, we conduct a cross-sectional study in China to develop and validate an efficient and simple clinical diagnostic model to help screen patients at risk of OSAHS.
methodsThis study based on 782 high-risk patients (aged >18 years) admitted to the Sleep Medicine department of the Sixth Affiliated Hospital, Sun Yat-sen University from 2015 to 2021. Totally 34 potential predictors were evaluated. We divided all patients into training and validation dataset to develop diagnostic model. The univariable and multivariable logistic regression model were used to build model and nomogram was finally built.
resultsAmong 602 high-risk patients with median age of 46 (37, 56) years, 23.26% were women. After selecting using the univariate logistic model, 15 factors were identified. We further used the stepwise method to build the final model with five factors: age, BMI, total bilirubin levels, high Berlin score, and symptom of morning dry mouth or mouth breathing. The AUC was 0.780 (0.711, 0.848), with sensitivity of 0.848 (0.811, 0.885), specificity of 0.629 (0.509, 0.749), accuracy of 0.816 (0.779, 0.853). The discrimination ability had been verified in the validation dataset. Finally, we established a nomogram model base on the above final model.
conclusionWe developed and validated a predictive model with five easily acquire factors to diagnose OSAHS patient in high-risk population with well discriminant ability. Accordingly, we finally build the nomogram model.
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