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

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Standard

Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies. / Eriksson, Jonatan; Andersson, Simone; Appelqvist, Roger; Wieslander, Elisabet; Truedsson, Mikael; Bugge, May; Malm, Johan; Dahlbäck, Magnus; Andersson, Bo; Fehniger, Thomas E.; Marko-Varga, György.

I: Proteome Science, Vol. 15, Nr. 1, 8, 21.04.2017.

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Harvard

APA

CBE

MLA

Vancouver

Author

RIS

TY - JOUR

T1 - Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies

AU - Eriksson, Jonatan

AU - Andersson, Simone

AU - Appelqvist, Roger

AU - Wieslander, Elisabet

AU - Truedsson, Mikael

AU - Bugge, May

AU - Malm, Johan

AU - Dahlbäck, Magnus

AU - Andersson, Bo

AU - Fehniger, Thomas E.

AU - Marko-Varga, György

PY - 2017/4/21

Y1 - 2017/4/21

N2 - Background: Data 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. Method: We 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). Results: We 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. Conclusion: We 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.

AB - Background: Data 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. Method: We 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). Results: We 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. Conclusion: We 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.

KW - Biobanking

KW - Bioinformatics

KW - Biomarkers

KW - Clinical study

KW - COPD

KW - EDC

KW - Proteomics

UR - http://www.scopus.com/inward/record.url?scp=85018496883&partnerID=8YFLogxK

U2 - 10.1186/s12953-017-0116-2

DO - 10.1186/s12953-017-0116-2

M3 - Article

VL - 15

JO - Proteome Science

T2 - Proteome Science

JF - Proteome Science

SN - 1477-5956

IS - 1

M1 - 8

ER -