On the Usage of Combined Data Structures to Study COVID-19 in Understudied Populations.
On the Usage of Combined Data Structures to Study COVID-19 in Understudied Populations.
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关于使用组合数据结构研究未受研究人群中的 COVID-19。
DOI：
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发表时间：
2021
影响因子：
13.8
通讯作者：
David J. Schlueter
中科院分区：
文献类型：
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作者：
David J. Schlueter
在Bourgeois et al，1中，作者证明了电子健康记录（EHR）数据结构的实用性，以系统地研究在正在进行的流行病背景下未被充分研究的人群。此外，它们提供了如何使用存储在不同国家不同数据模型中的数据进行此类分析的示例。通过EHR（4CE）COVID-19临床表征联盟，来自6个国家27家医院的数据（来自来自7个国家351家医院的更大的全球数据联盟）被结合起来研究儿科人群中COVID-19相关的临床结果，发现炎症标志物升高，凝血异常，心律失常，病毒性肺炎，和呼吸衰竭。这项工作进一步了解了先前在系统审查中研究的COVID-19在儿童和青年中的表现。2，3持续的COVID-19大流行以前所未有的方式影响了我们的生活和工作。从广泛的居家令和口罩规定到在家工作和指导我们的孩子，COVID-19疫情深刻影响了地球仪的每一个部分。此大流行病的一个值得注意的方面是为应对COVID-19而进行的大量研究。截至2021年4月，PubMed对COVID-19的搜索返回了超过110 000个索引结果，阐明了过去几个月进行的大量COVID-19相关研究。这项研究的大部分是不同实体之间的合作;从向公众发布SARS-CoV-2的初始基因序列到合作开发疫苗，合作有可能及时挽救生命。国家或全球范围的合作研究不仅有助于开发治疗方法，而且有助于了解在其他方面研究不足的人群中疾病的自然过程。例如，在Bourgeois等人的研究中，1关于COVID-19如何影响儿童和青少年的情况还不清楚，因为由于将未成年人纳入临床试验的挑战，很难研究这种疾病。1在儿科COVID-19证据的叙述性综合中，Metha等人2指出，在目前的许多文献中，临床数据很少，这说明了儿科COVID-19患者急需研究的领域。尽管很困难，但研究所有人群的临床病程很重要。用于研究未充分研究的人群的临床结果的一个天然数据源是EHR，其包括作为常规临床护理的一部分收集的结构化和非结构化数据。Bourgeois等人1写道，“站点对包含患者级EHR数据的本地临床数据仓库执行查询。为了构建所需的数据文件，研究中心使用了Informatics for Integrating Biology & the Bedside平台、观察性医学成果伙伴关系（OMOP）通用数据模型（CDM）、Epic Clarity或其他临床数据仓库。1 EHR标准，如OMOP，提供了一个通用的、标准化的医学概念抽象，将特定于站点的医学概念转换为通用词汇，以便研究人员可以利用来自多个贡献源的数据。例如，在OMOP的情况下，特定于站点的源条件概念（例如，疾病和相关健康问题的国际统计分类，第十次修订[ICD 10]，甚至非标准的医院特定代码）被转换为系统化的医学命名法或SNOMED，本体作为通用词汇。然而，在行级别以这样的规模合并数据源存在固有的困难，因为单个国家可能有更多的数据源，而这些数据源可能是由不同的国家或地区组成的。
In Bourgeois et al,1 the authors demonstrate the utility of electronic health record (EHR) data structures to systematically study an otherwise understudied population in the context of an ongoing pandemic. Furthermore, they provide an example of how to perform such analyses using data stored in different data models across different countries. Through the Consortium for Clinical Characterization of COVID-19 by EHR (4CE), data from 27 hospitals in 6 countries (from a larger consortium of global data from 351 hospitals from 7 countries) were combined to study COVID-19– associated clinical outcomes in the pediatric population, uncovering findings of elevated markers of inflammation, evidence of abnormalities in coagulation, cardiac arrhythmias, viral pneumonia, and respiratory failure. This work adds further knowledge to the manifestations of COVID-19 in children and youth that have been previously studied in systematic reviews.2,3 The ongoing COVID-19 pandemic has affected how we live and work in unprecedented ways. From widespread stay-at-home orders and mask mandates to working and instructing our children from home, the COVID-19 outbreak has profoundly affected every part of the globe. One notable facet of this pandemic is the amount of research that has been undertaken to address COVID-19. As of April 2021, a PubMed search for COVID-19 returned more than 110 000 indexed results, elucidating the vast amount of COVID-19–related research undertaken in the past several months. Much of this research is cooperative among disparate entities; from the release of the initial genetic sequence of SARS-CoV-2 to the public to collaborative development of vaccinations, collaboration has the potential to save lives in a timely manner. Collaborative research at a national or global scale can assist not only in the development of therapeutics but also in understanding the natural course of disease in otherwise understudied populations. For example, in Bourgeois et al,1 much is unknown about how COVID-19 affects children and youth because it is difficult to study the disease because of the challenges associated with including minors in clinical trials.1 In a narrative synthesis of pediatric COVID-19 evidence, Metha et al2 note that clinical data are scarce among much of the current literature, which illustrates a muchneeded area of study among pediatric patients with COVID-19. Despite being difficult, it is important to study the clinical course of disease in all populations. One natural data source for studying clinical outcomes among understudied populations is EHRs, which comprise structured and unstructured data collected as part of routine clinical care. Bourgeois et al1 write that “sites executed queries on local clinical data warehouses containing patient-level EHR data. To construct the required data files, sites used the Informatics for Integrating Biology & the Bedside platform, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), Epic Clarity, or other clinical data warehouses.”1 EHR standards, such as OMOP, provide a common, standardized abstraction of medical concepts that translates site-specific medical concepts to a common vocabulary so that researchers may leverage data from multiple contributing sources. For example, in the case of OMOP, site-specific source condition concepts (eg, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD10], or even nonstandard hospital-specific codes) are converted to the systemized nomenclature of medicine, or SNOMED, ontology as a common vocabulary. However, there is an inherent difficulty in the merging of data sources at such a scale at the row level because individual countries may have + Related article
DOI：
10.1056/nejmsr1809937
发表时间：
2019-08-15
期刊：
The New England journal of medicine
影响因子：
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作者：
All of Us Research Program Investigators;Denny JC;Rutter JL;Goldstein DB;Philippakis A;Smoller JW;Jenkins G;Dishman E
通讯作者：
Dishman E