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.

中文翻译：

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将临床化学生物标志物数据与COPD数据库合并-构建蛋白质组学研究的临床基础设施。

背景技术来自生物样品和医学评估的数据在临床决策中起着至关重要的作用。这些数据在临床研究中同样重要，拥有确保其在整个生命周期中都保持质量的基础设施至关重要。我们正在进行为期5年的纵向临床研究，即KOL-Örestad，目的是鉴定血液中新的COPD（慢性阻塞性肺疾病）生物标志物。在这项研究中，临床数据和血液样本分别从私人和公共卫生保健机构收集，并分别存储在我们的研究中心的数据库和生物库中。用质谱分析血液，然后将分析结果与临床数据联系起来。方法我们建立了一个基础架构，使我们能够有效地收集和分析数据。我们选择使用REDCap作为研究的EDC（电子数据捕获）工具，因为它的建立时间短，易于使用且具有灵活性。REDCap使用户可以轻松地基于现有模板设计数据收集模块。另外，它提供了两个功能，允许用户导入批量数据。通过Web API（应用程序编程接口）以及上传CSV文件（逗号分隔值）。结果我们创建了一个软件DART（数据快速转换），将我们的生物标记数据转换为适合REDCap的CSV模板的格式。此外，DART还可以配置为与许多其他数据格式一起使用。我们使用DART将我们的临床化学数据导入REDCap数据库。结论我们已经表明，可以扩展诸如REDCap之类的功能强大且已被国际广泛采用的EDC工具，从而可以有效地用于蛋白质组学研究。在我们的研究中，我们通过使用DART将我们的临床化学数据转换为适合REDCap模板的格式来实现。