Early warning and stratification of the elderly cardiopulmonary dysfunction-related diseases: multicentre prospective study protocol.
Authors
Affiliations (12)
Affiliations (12)
- Department of Radiology, The Second Affiliated Hospital of the Naval Medical University of the Chinese People's Liberation Army, Shanghai, Shanghai, China.
- Fuwai Central China Cardiovascular Hospital, Zhengzhou, China.
- Guangdong Provincial People's Hospital, Guangdong, China.
- Department of Geriatric Medicine, Xiangya Hospital Central South University, Changsha, Hunan, China.
- Xiangya Hospital Central South University, Changsha, Hunan, China.
- Shengjing Hospital of China Medical University, Shenyang, China.
- Department of Respiration, Shanghai Rui Jin Hospital; Shanghai Jiao Tong University School of Medicine; Shanghai, China, Shanghai, Shanghai, China.
- Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, China.
- Xiangya Hospital Central South University, Changsha, China.
- Qingdao Central Hospital, Qingdao, China.
- Hangzhou Smart Intelligent Tech Co Ltd, Hangzhou, China.
- Department of Radiology, The Second Affiliated Hospital of the Naval Medical University of the Chinese People's Liberation Army, Shanghai, Shanghai, China [email protected].
Abstract
In China, there is a lack of standardised clinical imaging databases for multidimensional evaluation of cardiopulmonary diseases. To address this gap, this study protocol launched a project to build a clinical imaging technology integration and a multicentre database for early warning and stratification of cardiopulmonary dysfunction in the elderly. This study employs a cross-sectional design, enrolling over 6000 elderly participants from five regions across China to evaluate cardiopulmonary function and related diseases. Based on clinical criteria, participants are categorized into three groups: a healthy cardiopulmonary function group, a functional decrease group and an established cardiopulmonary diseases group. All subjects will undergo comprehensive assessments including chest CT scans, echocardiography, and laboratory examinations. Additionally, at least 50 subjects will undergo cardiopulmonary exercise testing (CPET). By leveraging artificial intelligence technology, multimodal data will be integrated to establish reference ranges for cardiopulmonary function in the elderly population, as well as to develop early-warning models and severity grading standard models. The study has been approved by the local ethics committee of Shanghai Changzheng Hospital (approval number: 2022SL069A). All the participants will sign the informed consent. The results will be disseminated through peer-reviewed publications and conferences.