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

COVID-19 网络网络扩展预测儿童严重疾病的临床和转化方法（CONNECT 预测患病儿童）

• 批准号: 10273971
• 负责人: Maria Laura Gennaro
• 金额: $ 84.02万
• 依托单位: RBHS-ROBERT WOOD JOHNSON MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10273971
• 关键词: 2019-nCoV; Acute; Acute Disease; Adolescent; Adult; Affect; Appendicitis; Biochemical; Biological; Biological Factors; Biological Markers; Blood specimen; COVID-19; COVID-19 pandemic; Characteristics; Child; Childhood; Chronic; Clinical; Clinical Data; Communities; Data; Diagnosis; Diagnostic; Disease; Environmental Risk Factor; Epidemiology; Exposure to; Funding; Genetic; Genetic Polymorphism; Goals; Health Information System; Healthcare; Heart Diseases; Immune response; Immunologics; Individual; Infection; Inflammatory; Information Resources Management; Information Systems; Inpatients; Institutes; Intervention; Knowledge; Life; Lung diseases; Machine Learning; Maternal and Child Health; Measurement; Modeling; Morbidity - disease rate; Multisystem Inflammatory Syndrome in Children; Obesity; Outpatients; Pathogenicity; Patient Recruitments; Pediatric Hospitals; Phase; Population; Public Health; RADx; Rare Diseases; Reporting; Research; Respiratory Signs and Symptoms; Rheumatology; Risk; Risk Factors; Rupture; SARS-CoV-2 infection; Series; Severities; Social Sciences; Specific qualifier value; Surveys; Symptoms; Syndrome; Testing; Time; United States Health Resources and Services Administration; Virus; base; case control; case finding; coronavirus disease; data resource; developmental disease; epidemiologic data; improved; infection risk; mortality; multidimensional data; predictive marker; predictive modeling; prevent; saliva sample; severe COVID-19; social; social determinants; sociodemographics; translational approach

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.

SARS-CoV-2大流行在儿童中表现出广泛的临床表现， 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）是一种严重的炎症性疾病， 暴露于病毒或感染病毒后数周。这些表现在临床严重程度上重叠， 保持独特的临床特征。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，并从整合数据的能力， 实现两个目标：（i）确定预测严重COVID-19的临床、社会和生物变量， MIS-C，以及（ii）针对那些受病毒危害风险最大的人群和个人。我们提出 COVID-19网络的网络扩展临床和转化方法来预测严重疾病 由八个伙伴组成，提供关于15岁以上儿童的数据 百万儿童我们的网络将系统地整合社会，流行病学，遗传学，免疫学， 计算方法，以确定人口和个人水平的严重疾病的风险因素。我们 潜在的假设是，多维数据的组合-临床，社会人口统计学，流行病学， 和生物学上的--可以综合起来预测哪些孩子最有可能产生严重的后果 SARS-CoV-2感染。为了验证我们的假设，我们将开发一种预测患病儿童的方法， 利用住院、门诊、社区和流行病学数据资源， 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 我们将使用我们的网络数据开发和完善预测模型（目标1）。我们还将招募 先前诊断为COVID-19或MIS-C的参与者（沿着有适当对照的参与者， 轻度或无症状SARS-CoV 2感染者），提供调查数据（包括社会决定因素） 以及唾液和血液样本，以确定与严重疾病相关的持续生物因素（目标2）。我们 我将使用一个考虑边际价值的知识管理框架来反复评估我们的模型 用于提高模型的预测能力。在R33阶段，我们将验证并进一步完善 预测模型结合了来自我们网络中招募的其他参与者的数据， 包括通过实时监测发现的新感染严重COVID-19或MIS-C的儿童（Aim （3）第三章。我们寻求为儿童和青少年开发有用的预测模型， 和环境背景，并由RFA指定的REASSURED框架提供信息。模型 通过我们的全国性网络开发的生物标志物将产生可推广的知识， 这将提高我们预测哪些儿童最有可能患SARS-CoV严重并发症的能力， 2感染。这些知识将促进预防和治疗严重儿科疾病的干预措施。