Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration.
Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration.
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DOI：
10.1186/s12911-022-02093-0
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发表时间：
2023-01-16
影响因子：
3.5
通讯作者：
Akbari, Ashley
中科院分区：
文献类型：
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作者：
Abbasizanjani, Hoda;Torabi, Fatemeh;Bedston, Stuart;Bolton, Thomas;Davies, Gareth;Denaxas, Spiros;Griffiths, Rowena;Herbert, Laura;Hollings, Sam;Keene, Spencer;Khunti, Kamlesh;Lowthian, Emily;Lyons, Jane;Mizani, Mehrdad A.;Nolan, John;Sudlow, Cathie;Walker, Venexia;Whiteley, William;Wood, Angela;Akbari, Ashley
关键词：
CVD-COVID-UK联盟的成立是为了通过分析英国四个国家的协调电子健康记录（EHR）来了解COVID-19与心血管疾病之间的关系。除了COVID-19之外，数据协调和通用方法可以在独立的可信研究环境内和跨环境进行分析。在这里，我们描述了使用威尔士大规模EHR开发的可重复的协调方法，以适应英格兰和威尔士快速有效地实施跨国分析，作为CVD-COVID-UK计划的一部分。我们应对当前的挑战，分享经验教训。 服务于多个研究协议的范围和可扩展性，我们使用了威尔士人口SAIL数据库中保存的链接，匿名的个人EHR，人口统计和行政数据。从第一层的原始数据开始，将协调方法实施为四层可重现过程。然后，第二层到第四层中的每一层都由（但不限于）特征化的挑战和吸取的教训构成。我们在第二层中实现了精选数据，然后在第三层中提取表型数据。我们在第四层中捕获任何特定于项目的需求。使用实施的四层协调方法，我们检索了威尔士320万人的大约100个健康相关变量，这些变量与英格兰> 5600万人的相应变量相协调。我们将13个数据源处理到协调方法的第一层：其中5个每天或每周更新，其余的以不同的频率更新，提供足够的数据流更新，以便经常捕获最新的人口统计学，管理和临床信息。我们实施了一种高效、透明、可扩展和可复制的协调方法，使多国合作研究成为可能。由于目前重点关注COVID-19及其与心血管结局的关系，协调后的数据支持了英国各地的广泛研究活动。在线版本包含补充材料，可通过10.1186/s12911-022-02093-0获得
The CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Beyond COVID-19, data harmonisation and common approaches enable analysis within and across independent Trusted Research Environments. Here we describe the reproducible harmonisation method developed using large-scale EHRs in Wales to accommodate the fast and efficient implementation of cross-nation analysis in England and Wales as part of the CVD-COVID-UK programme. We characterise current challenges and share lessons learnt. Serving the scope and scalability of multiple study protocols, we used linked, anonymised individual-level EHR, demographic and administrative data held within the SAIL Databank for the population of Wales. The harmonisation method was implemented as a four-layer reproducible process, starting from raw data in the first layer. Then each of the layers two to four is framed by, but not limited to, the characterised challenges and lessons learnt. We achieved curated data as part of our second layer, followed by extracting phenotyped data in the third layer. We captured any project-specific requirements in the fourth layer. Using the implemented four-layer harmonisation method, we retrieved approximately 100 health-related variables for the 3.2 million individuals in Wales, which are harmonised with corresponding variables for > 56 million individuals in England. We processed 13 data sources into the first layer of our harmonisation method: five of these are updated daily or weekly, and the rest at various frequencies providing sufficient data flow updates for frequent capturing of up-to-date demographic, administrative and clinical information. We implemented an efficient, transparent, scalable, and reproducible harmonisation method that enables multi-nation collaborative research. With a current focus on COVID-19 and its relationship with cardiovascular outcomes, the harmonised data has supported a wide range of research activities across the UK. The online version contains supplementary material available at 10.1186/s12911-022-02093-0
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影响因子：
16.6
作者：
Froelicher D;Troncoso-Pastoriza JR;Raisaro JL;Cuendet MA;Sousa JS;Cho H;Berger B;Fellay J;Hubaux JP
通讯作者：
Hubaux JP
影响因子：
13.8
作者：
Bourgeois FT;Gutiérrez-Sacristán A;Keller MS;Liu M;Hong C;Bonzel CL;Tan ALM;Aronow BJ;Boeker M;Booth J;Cruz Rojo J;Devkota B;García Barrio N;Gehlenborg N;Geva A;Hanauer DA;Hutch MR;Issitt RW;Klann JG;Luo Y;Mandl KD;Mao C;Moal B;Moshal KL;Murphy SN;Neuraz A;Ngiam KY;Omenn GS;Patel LP;Jiménez MP;Sebire NJ;Balazote PS;Serret-Larmande A;South AM;Spiridou A;Taylor DM;Tippmann P;Visweswaran S;Weber GM;Kohane IS;Cai T;Avillach P;Consortium for Clinical Characterization of COVID-19 by EHR (4CE)
通讯作者：
Consortium for Clinical Characterization of COVID-19 by EHR (4CE)
影响因子：
--
作者：
通讯作者：
--
影响因子：
15.2
作者：
Brat, Gabriel A.;Weber, Griffin M.;Kohane, Isaac S.
通讯作者：
Kohane, Isaac S.
影响因子：
2.6
作者：
Coloma, Preciosa M.;Schuemie, Martijn J.;Sturkenboom, Miriam
通讯作者：
Sturkenboom, Miriam