Manders Peggy, Peters Tessa M A, Siezen Ariaan E, van Rooij Iris A L M, Snijder Roger, Swinkels Dorine W, Zielhuis Gerhard A
1 Radboud Biobank, Radboud University Medical Center , Nijmegen, the Netherlands .
2 Department of Human Genetics, Radboud University Medical Center , Nijmegen, the Netherlands .
Biopreserv Biobank. 2018 Apr;16(2):138-147. doi: 10.1089/bio.2017.0084. Epub 2018 Feb 13.
Current guidelines for clinical biobanking have a strong focus on obtaining, handling, and storage of biospecimens. However, to allow for research tying biomarker analysis to clinical decision making, there should be more focus on collection of data on donor characteristics. Therefore, our aim was to develop a stepwise procedure to define a framework as a tool to help start the data collection process in clinical biobanking.
The Radboud Biobank (RB) is a central clinical biobanking facility designed in accordance with the standards set by the Parelsnoer Institute, a Dutch national biobank originally initiated with eight different disease cohorts. To organize the information of these cohorts, we used our experience and knowledge in the field of biobanking and translational research to identify research domains and information categories to classify data. We extended this classification system to a stepwise procedure for defining a data collection framework and examined its utility for existing RB biobanks.
Our approach resulted in the definition of a three-step procedure: (1) Identification of research domains and relevant questions within the field that may benefit from biobank samples. (2) Identification of information categories and accompanying subcategories that are relevant for answering questions in identified research domains. (3) Reduction to an efficient framework based on essentiality and quality criteria. We showed the utility of the procedure for three existing RB biobanks.
We developed guidelines for the definition of a framework that supports the standardization of the biobank data collection process. Connecting the biobank database to pertinent information collected from the electronic health record will improve data quality and efficiency for both care and research. This is crucial when using the corresponding biospecimens for scientific research. Further, it also facilitates the combination of different clinical biobanks for a specific disease.
当前临床生物样本库指南非常注重生物样本的获取、处理和存储。然而，为了使生物标志物分析与临床决策相关的研究成为可能，应更加关注捐赠者特征数据的收集。因此，我们的目标是制定一个逐步程序，以定义一个框架，作为帮助启动临床生物样本库数据收集过程的工具。
拉德堡德生物样本库（RB）是一个中央临床生物样本库设施，其设计符合帕雷斯诺尔研究所制定的标准，该研究所是荷兰的一个国家生物样本库，最初由八个不同的疾病队列启动。为了整理这些队列的信息，我们利用我们在生物样本库和转化研究领域的经验和知识，确定研究领域和信息类别以对数据进行分类。我们将这个分类系统扩展为一个定义数据收集框架的逐步程序，并检验了其对现有RB生物样本库的实用性。
我们的方法导致定义了一个三步程序：（1）确定该领域内可能受益于生物样本库样本的研究领域和相关问题。（2）确定与回答已确定研究领域中的问题相关的信息类别及相应子类别。（3）根据必要性和质量标准简化为一个高效的框架。我们展示了该程序对三个现有RB生物样本库的实用性。
我们制定了支持生物样本库数据收集过程标准化的框架定义指南。将生物样本库数据库与从电子健康记录中收集的相关信息相连接，将提高医疗和研究的数据质量和效率。在将相应生物样本用于科学研究时，这至关重要。此外, 它还便于针对特定疾病合并不同的临床生物样本库。
