Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies

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Research

Elisabet Wieslander¹  Thomas E. Fehniger²  Roger Appelqvist²  Jonatan Eriksson²  Magnus Dahlbäck²  György Marko-Varga³  Johan Malm⁴  Bo Andersson⁵  Simone Andersson⁶  May Bugge⁷  Mikael Truedsson⁷ 

[1] Centre of Excellence in Biological and Medical Mass Spectrometry, Biomedical Centre D13, Lund University, 221 84, Lund, Sweden;Centre of Excellence in Biological and Medical Mass Spectrometry, Biomedical Centre D13, Lund University, 221 84, Lund, Sweden;Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden;Centre of Excellence in Biological and Medical Mass Spectrometry, Biomedical Centre D13, Lund University, 221 84, Lund, Sweden;Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden;First Department of Surgery, Tokyo Medical University, 6-7-1 Nishishinjiku Shinjiku-ku, 160-0023, Tokyo, Japan;Centre of Excellence in Biological and Medical Mass Spectrometry, Biomedical Centre D13, Lund University, 221 84, Lund, Sweden;Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, 205 02, Malmö, Sweden;Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden;Encap Security, Øvre Slottsgate 7, 0157, Oslo, Norway;Örestadskliniken, 217 67, Eddagatan 4, 217 67, Malmö, Sweden;

关键词: Proteomics;    COPD;    Clinical study;    Biomarkers;    Proteomics;    Biobanking;    Bioinformatics;    EDC;   

DOI  :  10.1186/s12953-017-0116-2

 received in 2016-09-06, accepted in 2017-04-14,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundData from biological samples and medical evaluations plays an essential part in clinical decision making. This data is equally important in clinical studies and it is critical to have an infrastructure that ensures that its quality is preserved throughout its entire lifetime. We are running a 5-year longitudinal clinical study, KOL-Örestad, with the objective to identify new COPD (Chronic Obstructive Pulmonary Disease) biomarkers in blood. In the study, clinical data and blood samples are collected from both private and public health-care institutions and stored at our research center in databases and biobanks, respectively. The blood is analyzed by Mass Spectrometry and the results from this analysis then linked to the clinical data.MethodWe built an infrastructure that allows us to efficiently collect and analyze the data. We chose to use REDCap as the EDC (Electronic Data Capture) tool for the study due to its short setup-time, ease of use, and flexibility. REDCap allows users to easily design data collection modules based on existing templates. In addition, it provides two functions that allow users to import batches of data; through a web API (Application Programming Interface) as well as by uploading CSV-files (Comma Separated Values).ResultsWe created a software, DART (Data Rapid Translation), that translates our biomarker data into a format that fits REDCap's CSV-templates. In addition, DART is configurable to work with many other data formats as well. We use DART to import our clinical chemistry data to the REDCap database.ConclusionWe have shown that a powerful and internationally adopted EDC tool such as REDCap can be extended so that it can be used efficiently in proteomic studies. In our study, we accomplish this by using DART to translate our clinical chemistry data to a format that fits the templates of REDCap.

【 授权许可】

CC BY   
© The Author(s). 2017