Doubal Fergus N, Ali Myzoon, Batty G David, Charidimou Andreas, Eriksdotter Maria, Hofmann-Apitius Martin, Kim Yun-Hee, Levine Deborah A, Mead Gillian, Mucke Hermann A M, Ritchie Craig W, Roberts Charlotte J, Russ Tom C, Stewart Robert, Whiteley William, Quinn Terence J
Stroke Association Garfield Weston Foundation Clinical Senior Lecturer, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
VISTA and VICCTA Coordinator, Institutes of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
BMC Neurol. 2017 Apr 17;17(1):72. doi: 10.1186/s12883-017-0841-2.
Traditional approaches to clinical research have, as yet, failed to provide effective treatments for vascular dementia (VaD). Novel approaches to collation and synthesis of data may allow for time and cost efficient hypothesis generating and testing. These approaches may have particular utility in helping us understand and treat a complex condition such as VaD.
We present an overview of new uses for existing data to progress VaD research. The overview is the result of consultation with various stakeholders, focused literature review and learning from the group's experience of successful approaches to data repurposing. In particular, we benefitted from the expert discussion and input of delegates at the 9 International Congress on Vascular Dementia (Ljubljana, 16-18 October 2015).
We agreed on key areas that could be of relevance to VaD research: systematic review of existing studies; individual patient level analyses of existing trials and cohorts and linking electronic health record data to other datasets. We illustrated each theme with a case-study of an existing project that has utilised this approach.
There are many opportunities for the VaD research community to make better use of existing data. The volume of potentially available data is increasing and the opportunities for using these resources to progress the VaD research agenda are exciting. Of course, these approaches come with inherent limitations and biases, as bigger datasets are not necessarily better datasets and maintaining rigour and critical analysis will be key to optimising data use.
传统的临床研究方法至今未能为血管性痴呆（VaD）提供有效的治疗方法。新的数据整理和综合方法可能有助于高效地提出和检验假设，且节省时间和成本。这些方法在帮助我们理解和治疗像VaD这样的复杂病症方面可能具有特殊用途。
我们概述了利用现有数据推进VaD研究的新用途。该概述是与各利益相关者协商、重点文献综述以及借鉴该团队数据重新利用成功方法的经验的结果。特别是，我们受益于第九届国际血管性痴呆大会（2015年10月16 - 18日，卢布尔雅那）代表们的专家讨论和意见。
我们确定了可能与VaD研究相关的关键领域：对现有研究进行系统综述；对现有试验和队列进行个体患者层面的分析，以及将电子健康记录数据与其他数据集相链接。我们用一个利用了这种方法的现有项目的案例研究对每个主题进行了说明。
VaD研究群体有很多机会更好地利用现有数据。潜在可用数据的数量在不断增加，利用这些资源推进VaD研究议程的机会令人兴奋。当然，这些方法存在固有的局限性和偏差，因为更大的数据集不一定就是更好的数据集，保持严谨性和批判性分析将是优化数据使用的关键。
