COVID-19 Supplement - Center for Innovative TRIals in ChilDrEN and AdulTs (TRIDENT)

项目总结/摘要 当前COVID-19全球大流行的影响可能被低估。对孕妇的具体影响 妇女及其婴儿和儿童的特征更不明确。我们将使用杜克的基础设施/ 范德比尔特试验创新中心通过三个项目来解决这些知识差距。我们将使用 现有的电子数据仓库的临床数据来自大约100 000名婴儿入院超过200 在美国的新生儿重症监护病房，检查12个月龄内的流行病学和结局 在当前的大流行期间，母亲所生的婴儿。我们的合作研究团队，包括 来自杜克临床研究所（DCRI）和Pediatrix Medical 集团，是唯一的定位，以完成这个项目。此外，我们将执行一个直接对家庭 一项观察性研究，以评估暴露于病毒的儿童中SARS-CoV-2感染的自然史 可能是通过卫生保健工作者的家庭接触。全美将有多达1000名儿童参加 States.我们将使用现有的登记/试验来确定家庭，并将评估感染率、病毒感染率、 脱落和免疫反应的儿童暴露于SARS-CoV-2。最后，为了充分实现 作为多项正在进行的COVID-19研究的一部分收集的数据，需要对数据进行整理和协调。 在这里，数据策展被定义为一种元数据管理活动， 结果和暴露。大部分策展将在个体研究水平进行。在数据被 协调，我们将进行详细的质量检查。由此产生的统一数据将在 不同的时间点提供给不同的受众，最终结果是一个可通过计算机获得的去识别数据集。 通过这项研究，我们将填补与COVID-19相关的主要知识空白，并改善公共卫生。

PROJECT SUMMARY/ABSTRACT The impact of the current COVID-19 global pandemic is likely underestimated. The specific impact on pregnant women, their infants and children is even less well characterized. We will use the infrastructure of the Duke / Vanderbilt Trial Innovation Center to address these knowledge gaps with three projects. We will use an existing electronic data warehouse of clinical data from approximately 100,000 infants admitted to over 200 neonatal intensive care units in the US to examine the epidemiology and outcomes through 12 months of age for infants born to mothers during the current pandemic. Our collaborative research team, comprising neonatologists and biostatisticians from the Duke Clinical Research Institute (DCRI) and the Pediatrix Medical Group, is uniquely positioned to complete this project. In addition, we will perform a direct-to-family observational study to evaluate the natural history of SARS-CoV-2 infection in children exposed to the virus presumably via health care worker household contact. Up to 1000 children will be enrolled across the United States. We will identify households using existing registries/trials and will evaluate the rate of infection, viral shedding, and immune response of children exposed to SARS-CoV-2. Finally, to realize the full value of the data collected as part of the multiple ongoing COVID-19 studies, data needs to be curated and harmonized. Here, data curation is defined as a metadata management activity that results in data with well-defined outcomes and exposures. The majority of curation will occur at individual study level. After the data have been harmonized, we will perform detailed quality checks. The resulting harmonized data will made availabel at different timepoints to various audiences with the final result beign a publically available de-identitified dataset. As a result of this research, we will fill major knowledge gaps related to COVID-19 and improve public health.