Boesen Kim, Hemkens Lars G, Janiaud Perrine, Hirt Julian
Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA.
medRxiv. 2024 Nov 18:2024.11.18.24317477. doi: 10.1101/2024.11.18.24317477.
Conducting systematic reviews of clinical trials is arduous and resource consuming. One potential solution is to design databases that are continuously and automatically populated with clinical trial data from harmonised and structured datasets. We aimed to map publicly available, continuously updated, topic-specific databases of randomised clinical trials (RCTs). We systematically searched PubMed, Embase, the preprint servers medRxiv, ArXiv, and Open Science Framework, and Google. We described seven features (access model, database architecture, data input sources, retrieval methods, data extraction methods, trial presentation, and export options) and narratively summarised the results. We did not register a protocol for this review. We identified 14 continuously updated clinical trial databases, seven related to COVID-19 (first active in 2020) and seven non-COVID databases (first active in 2009). All databases, except one, were publicly funded and accessible without restrictions. They mainly employed methods similar to those from static article-based systematic reviews and retrieved data from journal publications and trial registries. The COVID-19 databases and some non-COVID databases implemented semi-automated features of data import, which combined automated and manual data curation, whereas the non-COVID databases mainly relied on manual workflows. Most reported information was metadata, such as author names, years of publication, and link to publication or trial registry. Two databases included trial appraisal information (risk of bias assessments). Six databases reported aggregate group level results, but only one database provided individual participant data on request. We identified few continuously updated trial databases, and existing initiatives mainly employ methods known from static article -based reviews. The main limitation to create truly live evidence synthesis is the access and import of machine-readable and harmonised clinical trial data.
对临床试验进行系统评价既艰巨又耗费资源。一种潜在的解决方案是设计数据库,使其不断自动填充来自统一结构化数据集的临床试验数据。我们旨在梳理公开可用、持续更新的特定主题随机临床试验(RCT)数据库。我们系统地检索了PubMed、Embase、预印本服务器medRxiv、ArXiv和开放科学框架以及谷歌。我们描述了七个特征(访问模式、数据库架构、数据输入源、检索方法、数据提取方法、试验呈现和导出选项),并对结果进行了叙述性总结。我们未为此评价注册方案。我们识别出14个持续更新的临床试验数据库,其中7个与COVID-19相关(2020年首次启用),7个非COVID数据库(2009年首次启用)。除一个数据库外,所有数据库均由公共资金资助且无限制访问。它们主要采用与基于静态文章的系统评价类似的方法,从期刊出版物和试验注册库中检索数据。COVID-19数据库和一些非COVID数据库实现了数据导入的半自动功能,将自动和手动数据管理相结合,而非COVID数据库主要依赖手动工作流程。报告的大多数信息是元数据,如作者姓名、发表年份以及与出版物或试验注册库的链接。两个数据库包含试验评估信息(偏倚风险评估)。六个数据库报告了汇总组水平结果,但只有一个数据库可根据请求提供个体参与者数据。我们发现持续更新的试验数据库很少,现有举措主要采用基于静态文章的综述中已知的方法。创建真正实时证据合成的主要限制是机器可读和统一的临床试验数据的获取和导入。