Clinical Epidemiology of Pediatric COVID-19 and MIS-C

儿科 COVID-19 和 MIS-C 的临床流行病学

基本信息

批准号：
10191775
负责人：
Carlos Rafael Oliveira
金额：
$ 18.85万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10191775
关键词：
18 year old 2019-nCoV Accident and Emergency department Adult Affect Algorithms COVID-19 COVID-19 patient COVID-19 severity COVID-19 surveillance Caring Child Child Care Childhood Clinic Clinical Clinical Data Communicable Diseases Complement Country Critical Illness Cross-Sectional Studies Data Data Analyses Data Science Data Set Detection Deterioration Diagnosis Discipline Disease Educational workshop Electronic Health Record Emerging Communicable Diseases Emotional Enrollment Epidemiology Failure Frequencies Fright Functional disorder Funding Future Goals Health Care Costs Health system Hospitalized Child Hospitals Hour Infection Informatics Infrastructure International Intervention Investigation Laboratories Literature Long-Term Effects Longitudinal Studies Longitudinal cohort Longitudinal cohort study Lung diseases Machine Learning Manuals Medical center Mentored Patient-Oriented Research Career Development Award Mentors Mentorship Modeling Multisystem Inflammatory Syndrome in Children Names Natural Language Processing Outcome Patient Self-Report Patient-Focused Outcomes Patients Pediatric epidemiology Phenotype Physicians Protocols documentation Quality-of-Life Assessment Recovery Registries Reporting Research Research Personnel Risk Risk Factors SARS-CoV-2 infection SARS-CoV-2 positive Science Scientist Severities Shock Site Societies Standardization State Hospitals Structure Symptoms Syndrome System Testing Therapeutic Intervention Training United States Ventilator Virus Work base biomedical informatics body system clinical care clinical epidemiology clinical predictors clinical risk cohort computer program computerized tools data access design economic cost experience follow-up health related quality of life improved longitudinal analysis mathematical model multidisciplinary novel novel coronavirus pandemic disease predictive modeling prevent preventive intervention programs public health relevance recruit severe COVID-19 skills structured data surveillance data systemic inflammatory response tool unstructured data

项目摘要

PROJECT SUMMARY / ABSTRACT Although the novel Coronavirus (SARS-CoV-2) has accounted for significant health and economic costs throughout the world, relatively little is known about its effect on children. The first pediatric case of SARS- CoV-2 in the United States was reported on March 2, 2020, and within just three months, over 64,000 cases were confirmed. Even though children, as a group, have been relatively spared from the effects of the virus, there has been an increasing body of evidence to suggest that some may become critically ill. Since a number of children with SARS-CoV-2 infections manifest with severe systemic inflammation and multi-organ dysfunction, more research on determinants of disease and long-term outcomes of those affected is critical. Dr. Oliveira is a pediatric infectious disease clinician whose long-term goal is to become an independently funded physician-scientist, who integrates the disciplines of clinical epidemiology, data science, and biomedical informatics to detect and respond to emerging infectious diseases. The work described in this proposal builds on the scientific themes he developed throughout his prior training and aims to mechanistically understand the effects of SARS-CoV-2 in children by integrating three different scientific tools: natural language processing, machine learning, and clinical epidemiology. The first consideration for this K23 award period will be to use novel computational tools to build automated surveillance and data-extraction system that can facilitate the identification and tracking of incident cases of SARS-CoV-2 in children (Aim 1). Using this surveillance system, Dr. Oliveira will create a comprehensive registry and conduct a rigorous, model-based investigation to derive a state-of-the-art prediction model of clinical deterioration in children with SARS-CoV-2 (Aim 2). Last, he will recruit a longitudinal cohort of SARS-Cov-2 and determine the frequency of complications and long-term outcomes after recovery (Aim 3). This mentored research experience will furnish Dr. Oliveira with skills and expertise in various aspects of clinical epidemiology, including the establishment of surveillance systems, conducting longitudinal studies, computer programing, and executing sophisticated analyses of the longitudinal data. Workshops, semester- long courses will complement this practical experience, and one-on-one mentorship by a multidisciplinary team of established, independently funded, internationally respected investigators and pioneers in the fields of epidemiology, infectious diseases, biomedical informatics, and mathematical modeling. After this work, Dr. Oliveira will have produced important science that could improve the care of all the children affected by this pandemic. Furthermore, he will have gained a unique set of skills and built the necessary infrastructure that will allow him to establish a research program integrating the disciplines of clinical epidemiology, data science, and informatics to detect, prevent, and respond to future pandemics.

项目概要/摘要尽管新型冠状病毒（SARS-CoV-2）造成了巨大的健康和经济损失在世界各地，人们对其对儿童的影响知之甚少。第一例儿科SARS病例—— 美国于 2020 年 3 月 2 日报告了 CoV-2，短短三个月内就有超过 64,000 例病例被证实。尽管儿童作为一个群体相对免受病毒的影响，越来越多的证据表明，有些人可能会病情危重。由于一个数 SARS-CoV-2 感染儿童表现为严重的全身炎症和多器官功能障碍，对疾病的决定因素和受影响者的长期结果进行更多研究至关重要。 Oliveira 博士是一名儿科传染病临床医生，其长期目标是成为一名独立的受资助的医师科学家，整合了临床流行病学、数据科学和生物医学等学科检测和应对新出现的传染病的信息学。本提案中描述的工作构建他在之前的培训中发展了科学主题，旨在机械地理解通过整合三种不同的科学工具来研究 SARS-CoV-2 对儿童的影响：自然语言处理、机器学习和临床流行病学。 K23 奖励期的首要考虑因素是使用新颖的计算工具来构建自动监视和数据提取系统，以促进识别和追踪儿童 SARS-CoV-2 病例（目标 1）。使用这个监控系统，奥利维拉博士将创建一个全面的注册表，并进行严格的、基于模型的调查，以获得 SARS-CoV-2 儿童临床恶化的最先进预测模型（目标 2）。最后，他将招募 SARS-Cov-2 的纵向队列并确定并发症的频率和长期恢复后的结果（目标 3）。这种受指导的研究经验将为奥利维拉博士提供各个方面的技能和专业知识临床流行病学，包括建立监测系统、进行纵向研究、计算机编程，并对纵向数据进行复杂的分析。研讨会，学期- 长期课程将补充这种实践经验以及多学科团队的一对一指导领域内知名的、独立资助的、受国际尊敬的研究人员和先驱者流行病学、传染病、生物医学信息学和数学建模。完成这项工作后，奥利维拉博士将取得重要的科学成果，可以改善所有儿童的护理受此次疫情影响。此外，他将获得一套独特的技能并建立必要的技能基础设施将使他能够建立一个整合临床学科的研究项目流行病学、数据科学和信息学，用于检测、预防和应对未来的流行病。