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.

项目总结/摘要 尽管新型冠状病毒（SARS-CoV-2）造成了重大的健康和经济损失， 在世界各地，人们对它对儿童的影响知之甚少。首例儿童SARS病例- 2020年3月2日，美国报告了CoV-2，在短短三个月内， 得到了证实尽管儿童作为一个群体，相对来说没有受到病毒的影响， 越来越多的证据显示，有些人可能会患上重病。由于一些 的SARS-CoV-2感染儿童表现为严重的全身炎症和多器官 为了减少功能障碍，对疾病的决定因素和受影响者的长期结果进行更多的研究至关重要。 博士Oliveira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 受资助的医生-科学家，他整合了临床流行病学，数据科学和生物医学等学科 信息学用于检测和应对新出现的传染病。本提案中描述的工作 他在之前的培训中开发的科学主题，旨在机械地理解 SARS-CoV-2在儿童中的影响，通过整合三种不同的科学工具：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 新的计算工具，以建立自动化的监督和数据提取系统，可以促进 识别和追踪儿童中的SARS-CoV-2事件病例（目标1）。利用这个监视系统， 博士Oliveira将创建一个全面的注册表，并进行严格的，基于模型的调查， SARS-CoV-2儿童临床恶化的最新预测模型（目的2）。最后，他会 招募一个SARS-Cov-2纵向队列，并确定并发症和长期 恢复后的结果（目标3）。 这种指导的研究经验将为Oliveira博士提供各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， 计算机编程和执行纵向数据的复杂分析。讲习班，学期- 长期课程将补充这种实践经验，并由多学科团队进行一对一的指导 由独立资助的国际知名研究人员和先驱组成， 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，可以改善所有儿童的护理 受到这场大流行病的影响。此外，他将获得一套独特的技能，并建立必要的 基础设施，使他能够建立一个研究计划，整合学科的临床 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。