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: 10847827
• 负责人: Maria Laura Gennaro
• 金额: $ 151.77万
• 依托单位: RUTGERS BIOMEDICAL AND HEALTH SCIENCES
• 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 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10847827
• 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 Systems; Inpatients; Intervention; Knowledge; Knowledge Management; 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; System; Testing; Time; United States Health Resources and Services Administration; Virus; Youth; case finding; coronavirus disease; data integration; data resource; developmental disease; epidemiologic data; improved; infection risk; mortality; multidimensional data; predictive marker; predictive modeling; prevent; risk prediction; 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大流行体现在具有广泛临床表现的儿童中 从不对称感染到毁灭性的急性呼吸道症状，阑尾炎（通常有破裂）和 儿童多系统炎症综合征（MIS-C），一种严重的炎症状况，表现出几种 暴露于病毒或感染该病毒后几周。这些演示文稿在其临床严重程度上重叠 保持不同的临床特征。公共卫生和临床方法将受益于改进 了解与SARS COV-2相关的疾病频谱以及从整合数据的能力到 实现两个目标：（i）确定预测严重的COVID-19和的临床，社会和生物学变量 MIS-C和（ii）针对那些人群和个人受到最大伤害风险的人。我们建议 COVID-19网络网络扩展了临床和翻译方法以预测严重疾病 在儿童（与预测儿童联系）中，完成了八个合作伙伴，可在> 15上访问数据 百万儿童。我们的网络将系统地整合社会，流行病学，遗传，免疫学和 识别严重疾病的人口和个人级别危险因素的计算方法。我们的 基本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 - 可以整合以预测哪些孩子有最大的风险有严重后果 来自SARS-COV-2感染。为了检验我们的假设，我们将开发连接以预测生病的孩子，一个 利用住院，门诊，社区和流行病学数据资源的网络网络 使用机器学习和基于模型的分析来支持大数据分析。对于R61阶段，我们 将使用来自我们网络网络的数据开发和完善预测模型（AIM 1）。我们还将招募 以前被诊断为Covid-19或MIS-C的参与者（以及已有的适当对照 SARS-COV2的轻度或不对称感染），他们将提供调查数据（包括社会决定者） 以及唾液和血液样本以鉴定与严重疾病相关的持续生物学因素（AIM 2）。我们 是否使用知识管理框架对我们的模型进行迭代评估，以考虑边际价值 随着时间的推移，用于改善模型的预测能力的数据。在R33阶段，我们将验证并进一步完善 预测模型从整个网络网络中招募的其他参与者导入数据， 包括通过实时监视确定的新感染的患有严重的Covid-19或MIS-C的新感染儿童（AIM 3）。我们试图为有用的儿童和青少年开发预测模型，对社区敏感 和环境环境，并通过RFA指定的保证框架来告知。模型 通过我们的全国网络网络开发的生物标志物将产生可普遍的知识 这将提高我们预测哪些儿童对SARS-COV严重并发症的最大风险的能力 - 2感染。这些知识将促进干预措施，以预防和治疗严重的小儿疾病。