Screening and early warning system for chronic obstructive pulmonary disease with obstructive sleep apnoea based on the medical Internet of Things in three levels of healthcare: protocol for a prospective, multicentre, observational cohort study.
Zihan Pan: Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China. ORCID
Sha Liao: Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Wanlu Sun: Department of Pulmonary and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Beijing, China.
Haoyi Zhou: School of Software, Beihang University, Beijing, China.
Shuo Lin: Air Liquide Healthcare (Beijing), Beijing, China.
Dian Chen: Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Simin Jiang: Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Huanyu Long: Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Jing Fan: Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Furong Deng: Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China.
Wenlou Zhang: Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China.
Baiqi Chen: Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China.
Junyi Wang: Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Yongwei Huang: Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Jianxin Li: School of Computer Science and Engineering, Beihang University, Beijing, China chenyahong@vip.sina.com lijx@buaa.edu.cn.
Yahong Chen: Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China chenyahong@vip.sina.com lijx@buaa.edu.cn.
INTRODUCTION: Chronic obstructive pulmonary disease (COPD) and obstructive sleep apnoea (OSA) are prevalent respiratory diseases in China and impose significant burdens on the healthcare system. Moreover, the co-occurrence of COPD and OSA exacerbates clinical outcomes significantly. However, comprehensive epidemiological investigations in China remain scarce, and the defining characteristics of the population affected by COPD and OSA, alongside their intrinsic relationship, remain ambiguous. METHODS AND ANALYSIS: We present a protocol for a prospective, multicentre, observational cohort study based on a digital health management platform across three different healthcare tiers in five sites among Chinese patients with COPD. The study aims to establish predicative models to identify OSA among patients with COPD and to predict the prognosis of overlap syndrome (OS) and acute exacerbations of COPD through the Internet of Things (IoT). Moreover, it aims to evaluate the feasibility, effectiveness and cost-effectiveness of IoT in managing chronic diseases within clinical settings. Participants will undergo baseline assessment, physical examination and nocturnal oxygen saturation measuring. Specific questionnaires screening for OSA will also be administered. Diagnostic lung function tests and polysomnography will be performed to confirm COPD and OSA, respectively. All patients will undergo scheduled follow-ups for 12 months to record the changes in symptoms, lung functions and quality of life. Primary outcomes include the prevalence and characteristics of OS, while secondary outcomes encompass OS prognosis and the feasibility of the management model in clinical contexts. A total of 682 patients with COPD will be recruited over 12-24 months. ETHICS AND DISSEMINATION: The study has been approved by Peking University Third Hospital, and all study participants will provide written informed consent. Study results will be published in an appropriate journal and presented at national and international conferences, as well as relevant social media and various stakeholder engagement activities. TRIAL REGISTRATION NUMBER: NCT04833725.
