Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial-data management experience from the DESiGN Trial team.
Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial-data management experience from the DESiGN Trial team.
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DOI：
10.1186/s13063-021-05141-8
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发表时间：
2021-03-08
期刊：
影响因子：
2.5
通讯作者：
DESIGN Trial team
中科院分区：
文献类型：
--
作者：
Relph S;Elstad M;Coker B;Vieira MC;Moitt N;Gutierrez WM;Khalil A;Sandall J;Copas A;Lawlor DA;Pasupathy D;DESIGN Trial team
在临床试验中使用电子病历评估结果是一种方法策略，旨在推动更快、更经济高效地获取结果。本手稿的目的是概述使用电子病历数据的孕产妇和围产期临床试验的数据收集和管理注意事项，以DESiGN试验为例进行案例研究。DESiGN试验是一项整群随机对照试验，旨在评估复杂干预与标准护理对识别小于胎龄胎儿的影响。在两个时间点，从13个研究群组的22个不同电子记录系统中保存的四种类型的电子病历中收集关于孕产妇/围产期特征和结局的数据，包括接受新生儿护理的婴儿、胎儿超声参数和用于健康经济评估的医院活动详情。使用定制的Microsoft Excel宏在现场对数据进行加密，并安全地传输到中央数据存储。进行了数据质量检查。制定了原始数据的数据协调规则，并制作了数据词典，沿着了数据集数据链接的规则和假设。该词典包括数据协调和质量检查的基本原理和假设的描述。收集了165，397名女性178，350次怀孕中的182，052名婴儿的数据。各研究中心的数据可用性和完整性各不相同;在首次数据下载时，计算主要结局的关键8个变量中的每一个在中位数3（范围1-4）的聚类中完全缺失。在向研究中心澄清说明后，通过第二次数据下载改善了这一点（在第二个时间点，8个关键变量中的每一个在中位数1（范围0-1）聚类中完全缺失）。常见的数据管理挑战是协调来自多个来源的单个变量和对自由文本数据进行分类，为此试验开发了解决方案。使用电子病历评估结果的临床试验可能具有时间和成本效益，但仍需要适当的时间和资源来最大限度地提高数据质量。在英国，妊娠和围产期研究的一个困难是用于收集产科病房患者数据的各种不同系统。在这篇手稿中，我们描述了我们如何管理这一点，并提供了一个详细的数据字典，涵盖了变量名称和值的协调，这将有助于其他研究人员使用这些数据。主要登记研究和试验识别号：ISRCTN 67698474。注册于2016年2月11日。在线版本包含补充材料，可通过10.1186/s13063-021-05141-8获得。
The use of electronic patient records for assessing outcomes in clinical trials is a methodological strategy intended to drive faster and more cost-efficient acquisition of results. The aim of this manuscript was to outline the data collection and management considerations of a maternity and perinatal clinical trial using data from electronic patient records, exemplifying the DESiGN Trial as a case study. The DESiGN Trial is a cluster randomised control trial assessing the effect of a complex intervention versus standard care for identifying small for gestational age foetuses. Data on maternal/perinatal characteristics and outcomes including infants admitted to neonatal care, parameters from foetal ultrasound and details of hospital activity for health-economic evaluation were collected at two time points from four types of electronic patient records held in 22 different electronic record systems at the 13 research clusters. Data were pseudonymised on site using a bespoke Microsoft Excel macro and securely transferred to the central data store. Data quality checks were undertaken. Rules for data harmonisation of the raw data were developed and a data dictionary produced, along with rules and assumptions for data linkage of the datasets. The dictionary included descriptions of the rationale and assumptions for data harmonisation and quality checks. Data were collected on 182,052 babies from 178,350 pregnancies in 165,397 unique women. Data availability and completeness varied across research sites; each of eight variables which were key to calculation of the primary outcome were completely missing in median 3 (range 1–4) clusters at the time of the first data download. This improved by the second data download following clarification of instructions to the research sites (each of the eight key variables were completely missing in median 1 (range 0–1) cluster at the second time point). Common data management challenges were harmonising a single variable from multiple sources and categorising free-text data, solutions were developed for this trial. Conduct of clinical trials which use electronic patient records for the assessment of outcomes can be time and cost-effective but still requires appropriate time and resources to maximise data quality. A difficulty for pregnancy and perinatal research in the UK is the wide variety of different systems used to collect patient data across maternity units. In this manuscript, we describe how we managed this and provide a detailed data dictionary covering the harmonisation of variable names and values that will be helpful for other researchers working with these data. Primary registry and trial identifying number: ISRCTN 67698474. Registered on 02/11/16. The online version contains supplementary material available at 10.1186/s13063-021-05141-8.
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影响因子：
168.9
作者：
Norman, Jane E.;Heazell, Alexander E. P.;Whyte, Sonia
通讯作者：
Whyte, Sonia
影响因子：
2.5
作者：
Chaillet N;Bujold E;Masse B;Grobman WA;Rozenberg P;Pasquier JC;Shorten A;Johri M;Beaudoin F;Abenhaim H;Demers S;Fraser W;Dugas M;Blouin S;Dubé E;Gauthier R;PRISMA Trial Research Group
通讯作者：
PRISMA Trial Research Group
影响因子：
3.1
作者：
Elden, Helen;Hagberg, Henrik;Wennerholm, Ulla-Britt
通讯作者：
Wennerholm, Ulla-Britt
影响因子：
2.5
作者：
Groenen, Carola J. M.;Faber, Marjan J.;van Duijnhoven, Noortje T. L.
通讯作者：
van Duijnhoven, Noortje T. L.
影响因子：
3.1
作者：
Lenguerrand, Erik;Winter, Catherine;Draycott, Tim
通讯作者：
Draycott, Tim