ArticleNature and science of sleep2023
A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea.
Article in Nature and science of sleep, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 1 citations in OpenAlex.
- Explainable Machine-Learning Model for Predicting Severe Obstructive Sleep Apnea in Patients Undergoing Metabolic Bariatric Surgery.Obesity surgery · 2026Article
- Development and Validation of Prediction Models for Severe Obstructive Sleep Apnea Based on Periodic Health Examinations.The clinical respiratory journal · 2026Article
- Advances in Machine Learning Prediction Models for the Screening of Obstructive Sleep Apnea in Adults.Nature and science of sleep · 2025Review
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Obstructive sleep apnea (OSA) is a disease with high morbidity and is associated with adverse health outcomes. Screening potential severe OSA patients will improve the quality of patient management and prognosis, while the accuracy and feasibility of existing screening tools are not so satisfactory. The purpose of this study is to develop and validate a well-feasible clinical predictive model for screening potential severe OSA patients. Patients and Methods: We performed a retrospective cohort study including 1920 adults with overnight polysomnography among which 979 cases were diagnosed with severe OSA. Based on demography, symptoms, and hematological data, a multivariate logistic regression model was constructed and cross-validated and then a nomogram was developed to identify severe OSA. Moreover, we compared the performance of our model with the most commonly used screening tool, Stop-Bang Questionnaire (SBQ), among patients who completed the questionnaires. Results: Severe OSA was associated with male, BMI≥ 28 kg/m Conclusion: Based on common clinical examination of admission, we develop a novel model and a nomogram for identifying severe OSA from inpatient with suspected OSA, which provides physicians with a visual and easy-to-use tool for screening severe OSA.
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Registered trials
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