A living systematic review protocol for COVID-19 clinical trial registrations
Maguire BJ., Guérin PJ.
<ns4:p>Since the coronavirus disease 2019 (COVID-19) outbreak was identified in December 2019 in Wuhan, China, a strong response from the research community has been observed with the proliferation of independent clinical trials assessing diagnostic methods, therapeutic and prophylactic strategies. While there is no intervention for the prevention or treatment of COVID-19 with proven clinical efficacy to date, tools to distil the current research landscape by intervention, level of evidence and those studies likely powered to address future research questions is essential.</ns4:p><ns4:p> </ns4:p><ns4:p> This living systematic review aims to provide an open, accessible and frequently updated resource summarising the characteristics of COVID-19 clinical trial registrations. Weekly search updates of the WHO International Clinical Trials Registry Platform (ICTRP) and source registries will be conducted. Data extraction by two independent reviewers of trial characteristic variables including categorisation of trial design, geographic location, intervention type and targets, level of evidence and intervention adaptability to low resource settings will be completed. Descriptive and thematic synthesis will be conducted.</ns4:p><ns4:p> A searchable and interactive visualisation of the results database will be created, and made openly available online. Weekly results from the continued search updates will be published and made available on the Infectious Diseases Data Observatory (IDDO) website (<ns4:ext-link xmlns:ns3="http://www.w3.org/1999/xlink" ext-link-type="uri" ns3:href="https://www.iddo.org/research-themes/covid-19">COVID-19 website</ns4:ext-link>).</ns4:p><ns4:p> </ns4:p><ns4:p> This living systematic review will provide a useful resource of COVID-19 clinical trial registrations for researchers in a rapidly evolving context. In the future, this sustained review will allow prioritisation of research targets for individual patient data meta-analysis.</ns4:p>