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      Rationale and design of the Early Chronic Obstructive Pulmonary Disease (ECOPD) study in Guangdong, China: a prospective observational cohort study

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          Abstract

          Background

          Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and its clinically relevant subtypes are not well understood. Which clinical characteristics are more likely to be present among individuals who develop COPD remains to be studied in depth. Therefore, we designed a prospective observational cohort study, entitled the Early Chronic Obstructive Pulmonary Disease (ECOPD) study, to fill this evidence gap. The ECOPD study has four specific aims: (I) identification of characteristics, parameters, and biomarkers that may predict the development of airflow obstruction and annual decline in lung function with normal spirometry; (II) identification of clinically relevant early COPD subtypes; (III) identification of characteristics, parameters, and biomarkers that may predict disease progression in these early COPD subtypes; (IV) development and validation of machine learning models to predict development of airflow obstruction and disease progression.

          Methods

          We will recruit approximately 2,000 participants aged 40–80 years, including approximately 1,000 with COPD [post-bronchodilator forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) <0.7] and approximately 1,000 without COPD, using a population-based survey for COPD. We will assess all participants using standard respiratory epidemiological questionnaires, pulmonary function tests [pre-bronchodilator and post-bronchodilator spirometry, and impulse oscillometry (IOS)], health outcomes [modified British Medical Research Council (mMRC) dyspnea scale, COPD assessment test (CAT), COPD clinical questionnaire (CCQ)], inspiratory and expiratory chest computed tomography (CT), and biomarker measurements (blood and urine), as well as satellite remote sensing pollutant exposure measures. Subgroup will additionally complete induced sputum, exercise capacity tests [6-minute walk test (6MWT) and cardiopulmonary exercise testing (CPET)] and home monitoring/personal sampling as pollutant exposure measures. Study procedures will be performed at baseline and every 1 year thereafter.

          Discussion

          The ECOPD study will provide insight into many aspects of early COPD and improve our understanding of COPD development, which may facilitate therapeutic interventions with the potential to modify the course of disease.

          Trial Registration

          Chinese Clinical Trial Registry, ChiCTR1900024643. Registered on 19 July, 2019.

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          Most cited references62

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          Standardisation of spirometry.

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            ATS statement: guidelines for the six-minute walk test.

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              3D Slicer as an image computing platform for the Quantitative Imaging Network.

              Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                J Thorac Dis
                J Thorac Dis
                JTD
                Journal of Thoracic Disease
                AME Publishing Company
                2072-1439
                2077-6624
                December 2021
                December 2021
                : 13
                : 12
                : 6924-6935
                Affiliations
                [1 ]deptNational Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health , The First Affiliated Hospital of Guangzhou Medical University , Guangzhou, China;
                [2 ]Guangzhou Laboratory, Bio-Island , Guangzhou, China;
                [3 ]deptShenzhen Institute of Respiratory Disease , Shenzhen People’s Hospital , Shenzhen, China
                Author notes

                Contributions: (I) Conception and design: P Ran, Y Zhou; (II) Administrative support: P Ran, Y Zhou, R Chen; (III) Provision of study materials or patients: P Ran, Y Zhou; (IV) Collection and assembly of data: F Wu, J Peng, Z Deng, X Wen, Y Zheng, H Tian, H Yang, Z Wang, P Huang, N Zhao, R Sun; (V) Data analysis and interpretation: P Ran, Y Zhou, F Wu; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

                [#]

                These authors contributed equally to this work.

                Correspondence to: Pixin Ran. National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Laboratory, Guangzhou 510120, China. Email: pxran@ 123456gzhmu.edu.cn .
                [^]

                ORCID: 0000-0001-6651-634X.

                Article
                jtd-13-12-6924
                10.21037/jtd-21-1379
                8743397
                35070376
                bad039b6-96ef-4b4d-984d-4aeacbc5f33f
                2021 Journal of Thoracic Disease. All rights reserved.

                Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

                History
                : 20 August 2021
                : 17 November 2021
                Categories
                Study Protocol

                chronic obstructive pulmonary disease (copd),early chronic obstructive pulmonary disease,pre-chronic obstructive pulmonary disease,cohort study,subtype

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