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Yanqing Ye,1,2,* Ze-Lin Yan,3,* Yuanshou Huang,2,* Li Li,3 Shiming Wang,1 Xiaoxing Huang,1 Jingmeng Zhou,1 Liyi Chen,4 Chun-Quan Ou,3 Huaihong Chen1 1Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2Otolaryngology Department, Foshan Nan Hai District People’s Hospital, Foshan, People’s Republic of China; 3State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China; 4Yidu Cloud Technology Ltd, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huaihong Chen, Department of Otorhinolaryngology Head and Neck Surgery, Nan fang Hospital, Southern Medical University, 1838 Guangzhou Avenue, Bai-Yun District, Guangzhou, People’s Republic of China, Email chh19760306@163.com Chun-Quan Ou, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, 1838 Guangzhou Avenue, Bai-Yun District, Guangzhou, People’s Republic of China, Email ouchunquan@hotmail.comPurpose: Obstructive sleep apnea (OSA) is a disease with high morbidity and is associated with adverse health outcomes. Screening potential severe OSA patients will improve the quality of patient management and prognosis, while the accuracy and feasibility of existing screening tools are not so satisfactory. The purpose of this study is to develop and validate a well-feasible clinical predictive model for screening potential severe OSA patients.Patients and Methods: We performed a retrospective cohort study including 1920 adults with overnight polysomnography among which 979 cases were diagnosed with severe OSA. Based on demography, symptoms, and hematological data, a multivariate logistic regression model was constructed and cross-validated and then a nomogram was developed to identify severe OSA. Moreover, we compared the performance of our model with the most commonly used screening tool, Stop-Bang Questionnaire (SBQ), among patients who completed the questionnaires.Results: Severe OSA was associated with male, BMI≥ 28 kg/m2, high blood pressure, choke, sleepiness, apnea, white blood cell count ≥ 9.5× 109/L, hemoglobin ≥ 175g/L, triglycerides ≥ 1.7 mmol/L. The AUC of the final model was 0.76 (95% CI: 0.74– 0.78), with sensitivity and specificity under the optimal threshold selected by maximizing Youden Index of 73% and 66%. Among patients having the information of SBQ, the AUC of our model was statistically significantly greater than that of SBQ (0.78 vs 0.66, P = 0.002).Conclusion: Based on common clinical examination of admission, we develop a novel model and a nomogram for identifying severe OSA from inpatient with suspected OSA, which provides physicians with a visual and easy-to-use tool for screening severe OSA.Plain Language Summary: Question: How to build a more efficient screening model for severe OSA using some common variables for community physicians or non-sleep physicians?Findings: It was found that severe OSA was associated with male, BMI≥ 28 kg/m2, high blood pressure, choke, sleepiness, apnea, white blood cell count ≥ 9.5× 109/L, hemoglobin ≥ 175g/L, and triglycerides ≥ 1.7 mmol/L. The nomogram based on these variables was developed and validated. It seemed that our model outperformed the SBQ.Meaning: A clinically easy-to-use nomogram was provided for the screening of severe OSA in both non-sleep departments and community hospitals.Keywords: severe obstructive sleep apnea, clinical prediction model, nomogram

Zhi-Ying Zhan,1,2,* Qi Tian,3,* Ting-Ting Chen,1,* Yunshao Ye,3 Qiaoxuan Lin,3 Dong Han,1 Chun-Quan Ou1 1State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Health Care Management and Social Medicine, School of Public Health, Fujian Medical University, Fuzhou, People’s Republic of China; 3Department of Information Resources, Guangzhou Health Information Center, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chun-Quan Ou Email ocq@smu.edu.cnPurpose: Chronic obstructive pulmonary disease (COPD) is a major cause of chronic diseases causing considerable social and economic burden globally. Despite substantial evidence on temperature-COPD association, few studies have investigated the acute effect of temperature variability (TV), a potential trigger of exacerbation of COPD disease, and it remains unknown what fraction of the disease burden of COPD is attributable to TV.Patients and Methods: Based on 71,070 COPD hospitalizations during 2013– 2015 in Guangzhou, China, we conducted a time-series analysis using quasi-Poisson regression to assess the association between TV and hospital admission for COPD after adjusting for daily mean temperature. Short-term TV was captured by the standard deviation of hourly or daily temperatures across various exposure days. We also provided the fraction (total number) of COPD attributable to TV. Stratified analyses by admission route, sex, age, occupation, marital status and season were performed to identify vulnerable subpopulations.Results: We found a linear relationship between TV and COPD hospitalization, with a 1°C increase in hourly TV and daily TV associated with 4.3% (95%CI: 2.2– 6.4) and 4.0% (2.3– 5.8) increases in COPD, respectively. The greater relative risks of TV identified males, people aged 0– 64 years, blue collar, and divorced/widowed people as vulnerable population. There were 12.0% (8500 cases) of COPD hospitalization attributable to hourly TV during the study period. Daily TV produced similar estimates of relative effects (relative risk) but grater estimates of absolute effects (attributable fraction) than hourly TV.Conclusion: We concluded that TV was an independent risk factor of COPD morbidity, especially among the susceptible subgroups. These findings would be helpful to guide the development of targeted public intervention.Keywords: epidemiology, COPD hospitalization, distributed lag non-linear model, time-series analysis, China

Yu-He Hu,1,* Zhen-Yu Liang,2,* Li-Mei Xu,1,* Wen-Hui Xu,1 Hao Liao,1 Rui Li,1 Kai Wang,1 Ying Xu,1 Chun-Quan Ou,3 Xin Chen1 1Department of Respiratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China; 2State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; 3State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China *These authors contributed equally to this work Objective: Compared with the 2011 Global Initiative for Chronic Obstructive Lung Disease (GOLD), there have been significant changes in the 2017 GOLD classification. The purpose of this study was to analyze the changes in clinical characteristics of the new A-B-C-D system and to explore its role in comprehensive assessment of COPD.Subjects and methods: A total of 631 stable COPD patients were included in a cross-sectional survey. Data collected included baseline data and pulmonary function testing results, respiratory muscle strength, symptoms and quality of life, exercise capacity, nutritional status, and anxiety and depression as a comprehensive assessment. Based on the 2011 GOLD and 2017 GOLD classifications, patients were divided into Groups A1–D1 and Groups A2–D2, respectively.Results: In the 2011 GOLD, 64 subjects in Group C1 were reclassified into Group A2 (41.6%), while 77 subjects in Group D1 were reclassified into Group B2 (27.1%). The old and new grading systems were somewhat consistent (Cohen’s kappa=0.6963, P