Polysomnography (PSG) is treated as the gold standard for diagnosing obstructive sleep apnea (OSA). However, it is labor-intensive, time-consuming, and expensive. This study evaluates validity of overnight pulse oximetry as a diagnostic tool for moderate to severe OSA patients. A total of 699 patients with possible OSA were recruited for overnight oximetry and PSG examination at the Sleep Center of a University Hospital from Jan. 2004 to Dec. 2005. By excluding 23 patients with poor oximetry recording, poor EEG signals, or respiratory artifacts resulting in a total recording time less than 3 hours; 12 patients with total sleeping time (TST) less than 1 hour, possibly because of insomnia; and 48 patients whose ages less than 20 or more than 85 years old, data of 616 patients were used for further study. By further considering 76 patients with TST < 4 h, a group of 540 patients with TST ≥ 4 h was used to study the effect of insufficient sleeping time. Alice 4 PSG recorder (Respironics Inc., USA) was used to monitor patients with suspected OSA and to record their PSG data. After statistical analysis and feature selection, models built based on support vector machine (SVM) were then used to diagnose moderate and moderate to severe OSA patients with a threshold of AHI = 30 and AHI = 15, respectively. The SVM models designed based on the oxyhemoglobin desaturation index (ODI) derived from oximetry measurements provided an accuracy of 90.42-90.55%, a sensitivity of 89.36-89.87%, a specificity of 91.08-93.05%, and an area under ROC curve (AUC) of 0.953-0.957 for the diagnosis of severe OSA patients; as well as achieved an accuracy of 87.33-87.77%, a sensitivity of 87.71-88.53%, a specificity of 86.38-86.56%, and an AUC of 0.921-0.924 for the diagnosis of moderate to severe OSA patients. The predictive outcome of ODI to diagnose severe OSA patients is better than to diagnose moderate to severe OSA patients. Overnight pulse oximetry provides satisfactory diagnostic performance in detecting severe OSA patients. Home-styled oximetry may be a tool for severe OSA diagnosis.

Chronotypes are associated with shift work tolerance and sleep in shift workers, and sleep mediates the impact of shift work on mental health. However, the role of chronotype in the association between shift work and mental health has not been clarified. In this study, we aimed to examine the association between chronotype and burnout in shift workers, using the validated Munich ChronoType Questionnaire for shift workers (MCTQ^shift). A total of 288 shift workers with irregular shift frequencies were recruited and completed the Chinese-version MCTQ^shift and the Morningness–Eveningness Questionnaire (MEQ). Chronotypes were assessed by the calculation of corrected mid-sleep time (MSF_SC) from mid-sleep time on free days (MSF) based on their exact shift schedules. Another 26 evening-shift nurses were monitored with actigraphy for at least two consecutive evening shifts and the following two free days. Burnout was evaluated using the Copenhagen Burnout Inventory. We found that MSF^E_SC, MSF^E and mid-sleep time on workdays (MSW^E) had normal distributions and correlated significantly with MEQ scores (r = − 0.47, −0.45 and −0.47, respectively; all p < 0.001). MSW was more closely correlated with actigraphy-derived mid-sleep time on the free day before workdays than that on workdays (r = 0.61 and 0.48, respectively, p < 0.05). Sleep duration was significantly longer on workdays among evening-shift workers who slept late on workdays than those who slept early (β = 0.59, p < 0.001). After demographic and work characteristics were adjusted for in linear regression models, late chronotype and high social jetlag were associated with burnout scores in evening-shift workers. In conclusion, the Chinese-version MCTQ^shift is a valid tool for chronotype assessment. Interventions to improve sleep in shift workers should be tailored to chronotype due to variations in sleep behavior. Late chronotype may be an inherent feature of mental health problems, because the association with burnout was significant in both day workers in previous studies and shift workers.

Objective: Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets. Methods: We conducted our research using a data set consisting of 1656 patients, representing a diverse demographic, from the sleep center of China Medical University Hospital. To detect apnea ECG segments and extract apnea features, we utilized the EfficientNet and some of its layers, respectively. Furthermore, we compared various training and data preprocessing techniques to enhance the model’s prediction, such as setting class and sample weights or employing overlapping and regular slicing. Finally, we tested our approach against other literature on the Apnea-ECG database. Results: Our research found that the EfficientNet model achieved the best apnea segment detection using overlapping slicing and sample-weight settings, with an AUC of 0.917 and an accuracy of 0.855. For patient screening with AHI > 30, we combined the trained model with XGBoost, leading to an AUC of 0.975 and an accuracy of 0.928. Additional tests using PhysioNet data showed that our model is comparable in performance to existing models regarding its ability to screen OSA levels. Conclusions: Our suggested architecture, coupled with training and preprocessing techniques, showed admirable performance with a diverse demographic dataset, bringing us closer to practical implementation in OSA diagnosis. Trial registration The data for this study were collected retrospectively from the China Medical University Hospital in Taiwan with approval from the institutional review board CMUH109-REC3-018.

Introduction: In the prospective, observational, 16-week REACT study conducted between October 21, 2008 and May 12, 2011, we compared the real-world effectiveness of anti-inflammatory reliever and maintenance therapy with budesonide/formoterol (Symbicort^® Turbuhaler) and maintenance therapy with fixed-dose fluticasone/salmeterol (Seretide^®) plus as-needed, short-acting β₂ agonists (SABAs) in Taiwanese patients with inadequate asthma control. Methods: Asthma control was assessed using the five-item Asthma Control Questionnaire (ACQ-5) and standardized pulmonary function testing. Assessments were performed at baseline and at weeks 4– 5 and 12– 16. Overall, we enrolled 842 patients at 11 clinics, 723 of whom were included in analyses (budesonide/formoterol, 563.3± 1.3 μg/d, n=551; fluticasone/salmeterol, 1013.8± 1.4 μg/d, n=172). Results: At baseline, 72.5% and 27.5% of all patients had “partly” and “uncontrolled” asthma, respectively. Mean±SD ACQ-5 scores were 1.54± 1.06 and 1.46± 1.28 in the budesonide/formoterol and fluticasone/salmeterol groups, respectively. ACQ-5 scores significantly improved from baseline (ie, decreased) in both groups at weeks 4 and 16. ACQ-5 difference scores were significantly lower in the budesonide/formoterol group (− 0.91± 1.11) than the fluticasone/salmeterol group (− 0.69± 1.27) at the end of the study (p=0.027). Peak expiratory flow rate significantly improved from baseline in the budesonide/formoterol but not the fluticasone/salmeterol group at the end of the study. Severe exacerbation rates and medical resource utilization were comparable between the budesonide/formoterol and fluticasone/salmeterol groups. Conclusion: Collectively, results indicate the real-world effectiveness of budesonide/formoterol anti-inflammatory reliever and maintenance therapy is better than fixed-dose fluticasone/salmeterol plus as-needed SABA. Trial Registration: ClinicalTrials.gov registration number: NCT00784953.