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

项目摘要/摘要 当前新冠肺炎全球大流行的影响可能被低估了。对怀孕的具体影响 妇女、她们的婴儿和孩子的特点就更差了。我们将使用公爵的基础设施/ 范德比尔特试验创新中心通过三个项目解决这些知识差距。我们将使用 现有的电子数据仓库存储了大约100,000名入院的200多名婴儿的临床数据 美国新生儿重症监护病房检查12个月大的流行病学和结局 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 杜克大学临床研究所(DCRI)和儿科医学中心的新生儿病学家和生物统计学家 集团是完成这一项目的得天独厚的公司。此外，我们将执行直接到家庭的 评价接触病毒儿童SARS-CoV-2感染自然病史的观察性研究 大概是通过医护人员的家庭接触。全美将招收多达1000名儿童 各州。我们将使用现有的登记/试验来确定家庭，并将评估病毒感染率 暴露于SARS-CoV-2的儿童的脱落和免疫反应。最后，要充分实现 收集的数据作为正在进行的多项新冠肺炎研究的一部分，需要对数据进行整理和统一。 这里，数据管理被定义为产生定义良好的数据的元数据管理活动 结果和曝光率。大部分策展将在个人研究层面上进行。在数据被删除之后 协调，我们将进行详细的质量检查。产生的统一数据将在以下位置提供 针对不同受众的不同时间点，最终结果来自公开可用的非身份数据集。 通过这项研究，我们将填补与新冠肺炎相关的重大知识空白，提高公众健康水平。

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.