ArticleThe clinical respiratory journal2026
Development and Validation of Prediction Models for Severe Obstructive Sleep Apnea Based on Periodic Health Examinations.
Article in The clinical respiratory journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionObstructive sleep apnea (OSA) is not only associated with reduced work efficiency and an elevated risk of occupational accidents but also with hypertension, diabetes, and other lifestyle-related diseases, making it an important occupational health concern. Conventional questionnaire-based screening may fail to detect OSA because it frequently lacks subjective symptoms. Herein, we aimed to develop and validate a simple objective, questionnaire-independent prediction model for severe OSA using periodic health examination (PHE) data.
methodsFollowing the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), we analyzed the data of 671 patients who underwent overnight polysomnography (PSG) at Tohoku University Hospital. Eight predictors-age group, sex, obesity, hypertension, diabetes mellitus, dyslipidemia, polycythemia, and liver dysfunction-derived from routine PHE items-were included in logistic regression models to predict severe OSA, defined as an apnea-hypopnea index (AHI) ≥ 30 or a 3% oxygen desaturation index (ODI) ≥ 30. Internal validity was assessed using bootstrap samples. External validation was performed using overnight percutaneous oxygen saturation data of 100 university employees.
resultsThe areas under the receiver operating characteristic curve were 0.67 and 0.72 for the AHI- and ODI-based models, respectively. The internal validity was generally acceptable. In external validation, the AHI model had a sensitivity and specificity of 1.00 and 0.95, respectively, while the ODI model exhibited values of 0.50 and 0.97, respectively.
conclusionWe developed and validated two predictive models for severe OSA using the PHE data. These models could be used for screening by occupational physicians and clinicians.
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