Sleep & breathing = Schlaf & Atmung · 2026
Development and internal validation of a prediction model for sleep apnea syndrome treated with continuous positive airway pressure based on claims and health checkup data linked to personal health records.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Muraki Tatsuya, Ueda Tsuyoshi, Hasegawa Chihiro, Usui Hiroshi, Koshimizu Hiroshi, Ariyada Kenichi, Kusajima Kunio, Tomita Yasuhiro, Yanagisawa Masashi, Iwagami Masao
Аннотация
PURPOSE: To develop and validate a prediction model for sleep apnea syndrome (SAS) treated with continuous positive airway pressure (CPAP) in the general population.
METHODS: Using claims and health checkup data held by JMDC Inc. linked to personal health records (Pep Up), we developed and internally validated a prediction model for SAS treated with CPAP, defined as a SAS diagnosis and reimbursement records of CPAP. Every 3 months from January 1, 2022 to July 1, 2024 (11 timepoints), we identified eligible individuals with available data both 1 year before and after that timepoint to define the presence/absence of SAS treated with CPAP, along with 279 predictor variables. We developed a LightGBM model for the training and tuning datasets and evaluated its performance on the validation dataset.
RESULTS: Overall, 18,692,873 observations (mean age: 44.8 ± 11.3 years; female, 37.5%) were obtained from 1,858,566 individuals; of these observations, 300,868 observations (1.6%) were found to have SAS treated with CPAP. The area under the receiver operating characteristic curve was 0.898 (95% confidence interval 0.895-0.901). The positive predictive values among observations in the top 1% and 10% of predicted risk were 28.3% and 10.3%, respectively. According to the SHapley Additive exPlanations plots, male sex was the most important predictor, followed by age, body mass index, and waist circumference. Moreover, personal health records significantly improved the predictive performance.
CONCLUSION: We developed a prediction model to identify individuals at high risk of SAS treated with CPAP and encourage them to undergo polysomnography or related tests.
METHODS: Using claims and health checkup data held by JMDC Inc. linked to personal health records (Pep Up), we developed and internally validated a prediction model for SAS treated with CPAP, defined as a SAS diagnosis and reimbursement records of CPAP. Every 3 months from January 1, 2022 to July 1, 2024 (11 timepoints), we identified eligible individuals with available data both 1 year before and after that timepoint to define the presence/absence of SAS treated with CPAP, along with 279 predictor variables. We developed a LightGBM model for the training and tuning datasets and evaluated its performance on the validation dataset.
RESULTS: Overall, 18,692,873 observations (mean age: 44.8 ± 11.3 years; female, 37.5%) were obtained from 1,858,566 individuals; of these observations, 300,868 observations (1.6%) were found to have SAS treated with CPAP. The area under the receiver operating characteristic curve was 0.898 (95% confidence interval 0.895-0.901). The positive predictive values among observations in the top 1% and 10% of predicted risk were 28.3% and 10.3%, respectively. According to the SHapley Additive exPlanations plots, male sex was the most important predictor, followed by age, body mass index, and waist circumference. Moreover, personal health records significantly improved the predictive performance.
CONCLUSION: We developed a prediction model to identify individuals at high risk of SAS treated with CPAP and encourage them to undergo polysomnography or related tests.