Visualization analysis of the characteristics of COVID-19 clinical trials

Research Square(2020)

引用 1|浏览4
暂无评分
摘要
Abstract Background As a highly contagious disease, COVID-19 is raging on and is faced by every human being. Clinical trials are one of the most important means of investigating treatments for COVID-19, and their effective implementations may address the massive spread of the pandemic. As clinical trials continue to be conducted, the inability to view large amounts of data at a glance becomes a problem for many researchers. In order to provide reference and assistance for clinical trial design, this study collected and analyzed the current COVID-19 clinical trial registration data from multiple sources, and subsequently discussed their research status and developmental trend. Method The registered data of COVID-19 clinical trials were gathered from the ChiCTR and ClinicalTrials.gov website, which were transformed by Python and further demonstrated by Apache ECharts. Results As of March 28, 2020, records of 677 eligible registered trials had been retrieved. Overall, there are 407 (60.12%) interventional studies and 270 (39.12%) observational studies; 522 (77.10%) trials were conducted by hospitals; 53.32% of trials would be completed within six months; 523 (77.25%) subjects in trials were confirmed cases. Among interventional studies, 70.27% of the trials were randomized parallel studies; 55 (13.51%) trials considered time condition for clinical recovery as the primary endpoint, and 46 (11.30%) trials through clinical parameters and laboratory index as the primary endpoint. In the selection of intervention measures, chemical or biological agents constituted 43.49%, of which antiviral ones accounted for 14.50%, and antimalarials accounted for 8.85%, and 98 (24.14%) cases of studies involving TCM or integrated medicine. In addition, this study further analyzed antiviral drugs and explored possibilities of using combined drugs. Although a large number of clinical trials are already underway, interim research data will be helpful for future trial design and drug selection. Conclusions By compiling representative information of topical COVID-19 clinical trial registration, this study complements and enhances the effects of future researchers' trial designs.
更多
查看译文
关键词
visualization,trials,clinical
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