背景:COVID-19大流行引发了流行病学研究出版物的激增,基础科学,和临床科学。多亏了数字革命,现在可以访问大型数据集,这也可以实现实时流行病跟踪。然而,尽管如此,学术教师和他们的学员一直在努力获取全面的临床数据。为了解决这个问题,我们设计了一个临床数据存储库,以简化研究过程并促进跨学科合作。
目的:这项研究旨在提供一个易于访问的最新数据库,以促进对本地COVID-19临床数据的访问,从而提高效率,精简,并使研究企业民主化。通过提供强大的数据库,鼓励来自不同医学领域的广泛研究人员(教师和受训人员)和临床医生探索和合作新的临床相关研究问题。
方法:一个研究平台,称为耶鲁医学系COVID-19浏览器和存储库(DOM-CovX),是为了打扫房子而建造的,高度颗粒状,被取消身份,并不断更新2020年1月至2023年1月在耶鲁纽黑文卫生系统接受COVID-19治疗的18,000多名患者的数据。提取了几个关键领域的数据,包括人口统计,既往病史,住院期间的实验室值,生命体征,药物,成像,程序,和结果。鉴于几个数据域的时变特性,摘要统计数据的构建是为了限制数据库的计算大小,并提供一个合理的数据文件,更广泛的研究社区可以用于基本的统计分析。该计划还包括一个前端用户界面,DOM-CovXExplorer,用于聚合数据的简单数据可视化。经过审查委员会程序后,研究人员可以获得详细的临床数据集。
结果:截至2023年1月,DOM-CovXExplorer已收到来自耶鲁大学不同科学家群体的38项请求,该资料库已将研究能力扩展到不同利益相关者群体,包括15个不同外科和非外科专业的临床和研究型教师和学员。专门的DOM-CovX团队指导数据库的访问和使用,这加强了部门间的合作,导致发表16篇同行评审论文,在预打印服务器中提供2个项目,在科学会议上发表了8次演讲。目前,DOM-CovXExplorer继续扩展和改进其界面。该存储库包含7个不同临床领域的多达3997个变量,随着对研究人员请求和数据可用性的持续增长。
结论:DOM-CovXDataExplorerandRepository是一种用户友好的工具,用于分析数据并访问持续更新的,标准化,和大型数据库。它的创新方法促进了合作,学术追求的多样性,扩大医学教育。此外,它可以应用于COVID-19以外的其他疾病。
BACKGROUND: The COVID-19 pandemic sparked a surge of research publications spanning epidemiology, basic science, and clinical science. Thanks to the digital revolution, large data sets are now accessible, which also enables real-time epidemic tracking. However, despite this, academic faculty and their trainees have been struggling to access comprehensive clinical data. To tackle this issue, we have devised a clinical data repository that streamlines research processes and promotes interdisciplinary collaboration.
OBJECTIVE: This study aimed to present an easily accessible up-to-date database that promotes access to local COVID-19 clinical data, thereby increasing efficiency, streamlining, and democratizing the research enterprise. By providing a robust database, a broad range of researchers (faculty and trainees) and clinicians from different areas of medicine are encouraged to explore and collaborate on novel clinically relevant research questions.
METHODS: A research platform, called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), was constructed to house cleaned, highly granular, deidentified, and continually updated data from over 18,000 patients hospitalized with COVID-19 from January 2020 to January 2023, across the Yale New Haven Health System. Data across several key domains were extracted including demographics, past medical history, laboratory values during hospitalization, vital signs, medications, imaging, procedures, and outcomes. Given the time-varying nature of several data domains, summary statistics were constructed to limit the computational size of the database and provide a reasonable data file that the broader research community could use for basic statistical analyses. The initiative also included a front-end user interface, the DOM-CovX Explorer, for simple data visualization of aggregate data. The detailed clinical data sets were made available for researchers after a review board process.
RESULTS: As of January 2023, the DOM-CovX Explorer has received 38 requests from different groups of scientists at Yale and the repository has expanded research capability to a diverse group of stakeholders including clinical and research-based faculty and trainees within 15 different surgical and nonsurgical specialties. A dedicated DOM-CovX team guides access and use of the database, which has enhanced interdepartmental collaborations, resulting in the publication of 16 peer-reviewed papers, 2 projects available in preprint servers, and 8 presentations in scientific conferences. Currently, the DOM-CovX Explorer continues to expand and improve its interface. The repository includes up to 3997 variables across 7 different clinical domains, with continued growth in response to researchers\' requests and data availability.
CONCLUSIONS: The DOM-CovX Data Explorer and Repository is a user-friendly tool for analyzing data and accessing a consistently updated, standardized, and large-scale database. Its innovative approach fosters collaboration, diversity of scholarly pursuits, and expands medical education. In addition, it can be applied to other diseases beyond COVID-19.