Subjects. From October 1, 2017 to October 28, 2018, 26 stable COPD patients (21 males and 5 females, aged 45–77 years) with high risk of acute exacerbation and 13 normal controls (control group) were enrolled from the Department of Respiratory and Critical Care Medicine of the First Hospital of China Medical University. Inclusion criteria: 1) COPD was diagnosed following with GOLD 2018, and COPD patients who had history of hospitalization for exacerbation or ≥ 2 moderate exacerbation of COPD per year were regarded as with high risk of acute exacerbation. 2) OSA was diagnosed in accordance with the updated 2014 American Academy of Sleep Medicine (AASM) criteria28. Exclusion criteria: 1) no more than 40 years old. 2) the subjects with other diseases that affected the airflow and the quality of sleep, such as narcolepsy, mental illness, severe myocardial infarction, arrhythmia, severe liver and kidney insufficiency. 3) the subjects who could not complete the questionnaires because of cognitive impairment. 4) the subjects who were taking drugs that affected sleep quality. Based on the results of overnight polysomnography, the COPD patients were divided into COPD group (n = 12) and OS group (n = 14). This study was approved by the Ethics Committee of the First Hospital of China Medical University, and all subjects signed informed consents. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Questionnaires. CAT score (GlaxoSmithKline, Brent ford, UK)29 were recorded to evaluate the daily symptoms of the subjects. The severity of dyspnea was quantified by the mMRC dyspnea scale30. The PSQI31 was used to subjectively evaluate sleep quality which had 19 items and was divided into 7 subparts with a range of 0–21 and a score above 5 indicated poor sleep. The ESS32 was employed to classify subjective daytime sleepiness which contained eight items ranging from 0–24 and a high score indicated excessive daytime sleepiness.
Pulmonary function test. When they came into stable stage, all COPD patients had pulmonary function testing (Master Screen, Germany) based on American Thoracic Society standards33. Before the test, the safety and accuracy of implementation was evaluated. The subjects were required to meet inclusion criteria and take no bronchodilators within 2 weeks. Pre-bronchodilator spirometry was performed according to American Thoracic Society Standards and repeated 15 minutes after inhalation of 400 µg salbutamol via large-volume spacer. At least three measurements were taken and the best value was selected for analysis. FEV1%, FVC%, FEV1/FVC%, FEF25-75%, PEF and PEF% were recorded. Airflow limitation was diagnosed when the ratio of FEV1 to FVC was less than 70% predicted after bronchodilator inhalation.
Polysomnography. All subjects underwent a full overnight PSG (Respironics, Alice 5, US) to assess objective sleep quality just after pulmonary function testing in the same day by monitoring and recording all night electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), chin and tibial electromyography (EMG), respiration, ribcage and abdominal movements, snoring, body position and oxygen saturation by finger pulse oximetry. Apnea-hypopnea index (AHI) was calculated as the sum of apneas and hypopneas during the sleep period divided by total sleep time. Apnea was defined as a cessation of air flow for more than 10 seconds and hypopnea as a reduction of air flow > 50% for > 10 seconds plus oxygen desaturation of > 3% or arousal. Arousals meant an abrupt shift of EEG frequency lasting at least 3 seconds, with at least 10 seconds of preceding stable sleep. TST, sleep latency, WASO, sleep architecture, AHI, the nadir of nocturnal oxygen saturation (MmSaO2), the mean nocturnal oxygen saturation (MSaO2), SIT90 were recorded and compared. The data analyzers were blinded to the patient’s clinical condition.
Statistical Analysis. The data were statistically analyzed by SPSS (Statistical Product and Service Solutions 20.0 version, Armonk, NY, USA) software. Descriptive and inferential statistics were used to characterize baseline measurements. Normally distributed data were expressed as mean ± standard deviation (SD), median and interquartile range, or number. Comparisons of mean levels of quantitative variables between groups were assessed by using the one-way ANOVA or in-dependent t-tests or Mann-Whitney U test. Proportions were compared between groups by using Fisher exact chi-square testing. Covariance analysis or multiple linear regression correction was used to correct confounding factors such as age. Univariate associations between sleep parameters and clinical variables were performed with Pearson (variables with equidistant and normal distribution), Spearman (variables that do not conform to normal distribution) or Kendall's tau-b correlation coefficients (the classified variables). Stepwise multiple regression was used to identify which variables could predict the sleep quality. P < 0.05 were considered statistically significant.