Clinical Epidemiology of Pediatric COVID-19 and MIS-C

儿科 COVID-19 和 MIS-C 的临床流行病学

• 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
• 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

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.

项目摘要/摘要 尽管新型冠状病毒(SARS-CoV-2)造成了巨大的健康和经济成本 在世界各地，人们对它对儿童的影响知之甚少。首例儿科SARS病例-- 美国于2020年3月2日报告了CoV-2病毒，在短短三个月内，超过6.4万例病例 都得到了证实。尽管儿童作为一个群体相对没有受到病毒的影响， 越来越多的证据表明，其中一些人可能会病危。因为有一个数字 儿童SARS-CoV-2感染表现为严重的全身炎症和多器官损害 对疾病决定因素和受影响者的长期结果进行更多的研究是至关重要的。 奥利维拉医生是一名儿科传染病临床医生，他的长期目标是成为一名独立的 获得资助的内科医生-科学家，他整合了临床流行病学、数据科学和生物医学的学科 检测和应对新出现的传染病的信息学。本建议书中描述的工作构建 关于他在之前的训练中形成的科学主题，目的是机械地理解 通过整合三种不同的科学工具对儿童SARS-CoV-2的影响：自然语言处理， 机器学习和临床流行病学。K23授权期的首要考虑因素将是使用 构建自动监控和数据提取系统的新型计算工具，可以促进 识别和跟踪儿童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登记，并进行严格的、基于模型的调查，以得出 SARS-CoV-2儿童临床恶化的最新预测模型(目标2)。最后，他会 招募SARS-CoV-2纵向队列，并确定并发症和长期疗效的频率 康复后的结果(目标3)。 这种有指导的研究经验将为Oliveira博士提供在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，并对纵向数据执行复杂的分析。工作坊，学期- 长时间的课程将补充这种实践经验，以及多学科团队的一对一指导。 由独立资助的、在国际上受尊敬的研究人员和先驱者组成 流行病学、传染病、生物医学信息学和数学建模。 在这项工作之后，奥利维拉博士将产生重要的科学成果，可以改善对所有儿童的照顾 受这场大流行的影响。此外，他将获得一套独特的技能，并建立必要的 基础设施，使他能够建立一个整合临床学科的研究计划 流行病学、数据科学和信息学，以检测、预防和应对未来的流行病。