Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies
(2017) In Proteome Science 15(1).- Abstract
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... (More)
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.
(Less)
- author
- organization
- publishing date
- 2017-04-21
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Biobanking, Bioinformatics, Biomarkers, Clinical study, COPD, EDC, Proteomics
- in
- Proteome Science
- volume
- 15
- issue
- 1
- article number
- 8
- publisher
- BioMed Central (BMC)
- external identifiers
-
- pmid:28439209
- wos:000399772000001
- scopus:85018496883
- ISSN
- 1477-5956
- DOI
- 10.1186/s12953-017-0116-2
- language
- English
- LU publication?
- yes
- id
- d67f4cb9-b260-4e1f-9aa8-ccf2cdfe6770
- date added to LUP
- 2017-05-19 07:40:11
- date last changed
- 2024-03-17 14:22:45
@article{d67f4cb9-b260-4e1f-9aa8-ccf2cdfe6770,
abstract = {{<p>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.</p>}},
author = {{Eriksson, Jonatan and Andersson, Simone and Appelqvist, Roger and Wieslander, Elisabet and Truedsson, Mikael and Bugge, May and Malm, Johan and Dahlbäck, Magnus and Andersson, Bo and Fehniger, Thomas E. and Marko-Varga, György}},
issn = {{1477-5956}},
keywords = {{Biobanking; Bioinformatics; Biomarkers; Clinical study; COPD; EDC; Proteomics}},
language = {{eng}},
month = {{04}},
number = {{1}},
publisher = {{BioMed Central (BMC)}},
series = {{Proteome Science}},
title = {{Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies}},
url = {{http://dx.doi.org/10.1186/s12953-017-0116-2}},
doi = {{10.1186/s12953-017-0116-2}},
volume = {{15}},
year = {{2017}},
}