The clinical respiratory journal · 2026
Development and Validation of Prediction Models for Severe Obstructive Sleep Apnea Based on Periodic Health Examinations.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Kanno Kyoka, Ogawa Hiromasa, Irokawa Toshiya, Ohkouchi Shinya, Tabata Masao, Ohko Natsuko, Kurosawa Hajime
Аннотация
INTRODUCTION: Obstructive 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.
METHODS: Following 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.
RESULTS: The 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.
CONCLUSION: We 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.
METHODS: Following 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.
RESULTS: The 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.
CONCLUSION: We 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.