Translational Deep Phenotyping Of Deaths Related To The Covid-19 Pandemic: Protocol For A Prospective Observational Autopsy Study

BMJ OPEN(2021)

引用 1|浏览4
暂无评分
摘要
Introduction The COVID-19 pandemic is an international emergency with an extreme socioeconomic impact and a high mortality and disease burden. The COVID-19 outbreak is neither fully understood nor fully pictured. Autopsy studies can help understand the pathogenesis of COVID-19 and has already resulted in better treatment of patients. Structured and systematic autopsy of COVID-19-related deaths will enhance the mapping of pathophysiological pathways, not possible in the living. Furthermore, it provides an opportunity to envision factors translationally for the purpose of disease prevention in this and future pandemics. This is the protocol for an autopsy study that offers an umbrella for deep and diverse investigations of COVID-19-related deaths, including a systematic investigation of 'long' COVID-19 by means of extensive and systematic tissue sampling. Methods and analysis A COVID-19-specific autopsy algorithm has been created to cover all cases undergoing clinical or forensic autopsy in Denmark. The algorithm describes advanced tissue sampling and a translational analytical follow-up for deep phenotyping. The translational approach covers registry data, postmortem imaging, gross autopsy findings, microscopic organ changes, postmortem toxicology, postmortem biochemical investigation, microbiological profiling and immunological status at the time of death, and future research projects covering genetics and epigenetics on an organ level. Ethics and dissemination This study has been approved by the Regional Ethics Committee of the Region of Greater Copenhagen (No: H-20078436) and the Danish Data Protection Agency (No: 2002-54-1080). Next of kin gave informed consent to research. The study results will be published in peer-reviewed journals.
更多
查看译文
关键词
COVID-19, forensic pathology, adult pathology, computed tomography, immunology, cardiology
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要