COVID-19 Network of Networks Expanding Clinical and Translational approaches to Predict Severe Illness in Children (CONNECT to Predict SIck Children)

SARS-CoV-2大流行表现在儿童身上，临床表现广泛，范围广泛 从无症状感染到毁灭性的急性呼吸道症状、阑尾炎(通常伴有破裂)，以及 儿童多系统炎症综合征(MIS-C)，一种严重的炎症状态，表现为几种 在接触病毒或感染病毒几周后。这些表现在临床严重性上是重叠的，而 保持鲜明的临床特征。公共卫生和临床方法将受益于改进的 了解与SARS CoV-2相关的疾病谱，以及将数据整合到 实现两个目标：(I)确定预测严重新冠肺炎和 MIS-C，以及(Ii)针对那些受病毒危害风险最大的人群和个人。我们建议 新冠肺炎网络扩大临床和翻译方法以预测严重疾病 在儿童中(连接以预测患病儿童)，由八个合作伙伴组成，提供对&gt；15数据的访问 百万儿童。我们的网络将系统地将社会、流行病学、遗传学、免疫学和 确定人群和个人水平的严重疾病风险因素的计算方法。我们的 潜在的假设是多维数据的组合-临床，社会人口学，流行病学， 和生物学--可以结合起来预测哪些儿童有最大的风险产生严重后果 来自SARS-CoV-2感染。为了检验我们的假设，我们将开发预测患病儿童的连接，a 利用住院、门诊、社区和流行病学数据资源的网络 支持使用机器学习和基于模型的分析来分析大数据。对于R61阶段，我们 将使用我们网络网络中的数据开发和改进预测模型(目标1)。我们还将招募 之前被诊断患有新冠肺炎或MISC的参与者(以及患有 轻微或无症状的SARS-CoV2感染)，世卫组织将提供调查数据(包括社会决定因素) 以及唾液和血液样本，以确定与严重疾病有关的持久性生物因素(目标2)。我们 将使用考虑边际价值的知识管理框架反复评估我们的模型 随着时间的推移，提高模型的预测能力的数据。在R33阶段，我们将验证并进一步细化 预测模型结合了来自我们整个网络网络中招募的更多参与者的数据， 包括通过实时监测(AIM)发现患有严重新冠肺炎或MISC的新感染儿童 3)。我们寻求为儿童和青少年开发有用的、对社区敏感的预测模型 和环境背景，并由RFA规定的放心框架提供信息。模特们 通过我们全国网络开发的生物标记物将产生可概括的知识 这将提高我们预测哪些儿童患SARS冠状病毒严重并发症的风险最高的能力。 2感染。这一知识将有助于预防和治疗严重儿科疾病的干预措施。

The SARS-CoV-2 pandemic has manifested in children with a wide spectrum of clinical presentations ranging from asymptomatic infection to devastating acute respiratory symptoms, appendicitis (often with rupture), and Multisystem Inflammatory Syndrome in Children (MIS-C), a serious inflammatory condition presenting several weeks after exposure to or infection with the virus. These presentations overlap in their clinical severity while maintaining distinct clinical profiles. Public health and clinical approaches will benefit from an improved understanding of the spectrum of illness associated with SARS CoV-2 and from the capacity to integrate data to achieve two goals: (i) to identify the clinical, social, and biological variables that predict severe COVID-19 and MIS-C, and (ii) to target those populations and individuals at greatest risk for harm from the virus. We propose the COVID-19 Network of Networks Expanding Clinical and Translational approaches to Predict Severe Illness in Children (CONNECT to Predict SIck Children) comprising eight partners providing access to data on >15 million children. Our network will systematically integrate social, epidemiological, genetic, immunological, and computational approaches to identify both population- and individual-level risk factors for severe illness. Our underlying hypothesis is that a combination of multidimensional data – clinical, sociodemographic, epidemiologic, and biological -- can be integrated to predict which children are at greatest risk to have severe consequences from SARS-CoV-2 infection. To test our hypothesis, we will develop CONNECT to Predict SIck Children, a network of networks that leverages inpatient, outpatient, community, and epidemiological data resources to support the analysis of large data using machine learning and model-based analyses. For the R61 phase, we will develop and refine predictive models using data from our network of networks (Aim 1). We will also recruit participants previously diagnosed with either COVID-19 or MIS-C (along with appropriate controls who have had mild or asymptomatic infections with SARS-CoV2), who will provide survey data (including social determinants) and saliva and blood samples to identify persisting biological factors associated with severe disease (Aim 2). We will iteratively assess our models using a knowledge management framework that considers the marginal value of data for improving models' predictive capacity over time. In the R33 phase, we will validate and further refine predictive models incorporating data from additional participants recruited throughout our network of networks, including newly infected children with severe COVID-19 or MIS-C identified through real-time surveillance (Aim 3). We seek to develop predictive models for children and adolescents that are useful, sensitive to community and environmental contexts, and informed by the REASSURED framework specified by the RFA. The models and biomarkers developed through our nationwide network of networks will produce generalizable knowledge that will improve our ability to predict which children are at greatest risk for severe complications of SARS-CoV- 2 infection. This knowledge will facilitate interventions to prevent and treat severe pediatric illness.