Study Objectives:People with obstructive sleep apnea (OSA) remain undiagnosed owing to lack of easy and comfortable screening tools. Through this study, we aimed to compare the diagnostic accuracy ...

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM_2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM_2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .¹.

Background: Exercise limitation is an important issue in patients with chronic obstructive pulmonary disease (COPD), and it often co-exists with obstructive sleep apnoea (overlap syndrome). This study examined the effects of nocturnal continuous positive airway pressure (CPAP) treatment on walking capacity in COPD patients with or without obstructive sleep apnoea. Methods: Forty-four stable moderate-to-severe COPD patients were recruited and completed this study. They all underwent polysomnography, CPAP titration, accommodation, and treatment with adequate pressure. The incremental shuttle walking test was used to measure walking capacity at baseline and after two nights of CPAP treatment. Urinary catecholamine and heart rate variability were measured before and after CPAP treatment. Results: After two nights of CPAP treatment, the apnoea-hypopnoea index and oxygen desaturation index significantly improved in both overlap syndrome and COPD patients, however these changes were significantly greater in the overlap syndrome than in the COPD group. Sleep architecture and autonomic dysfunction significantly improved in the overlap syndrome group but not in the COPD group. CPAP treatment was associated with an increased walking capacity from baseline from 226.4 ± 95.3 m to 288.6 ± 94.6 m (P < 0.05), and decreased urinary catecholamine levels, pre-exercise heart rate, oxygenation, and Borg scale in the overlap syndrome group. An improvement in the apnoea-hypopnoea index was an independent factor associated with the increase in walking distance (r = 0.564). Conclusion: Nocturnal CPAP may improve walking capacity in COPD patients with overlap syndrome. Trial registration: NCT00914264