Evidence network of COVID-19 clinical trials of top 15 interventions

In response to the global coronavirus disease 2019 (COVID-19) emergency, clinical trial research assessing the efficacy and safety of clinical candidate interventions to treat COVID-19 are emerging at an unprecedented rate. As of April 21, 2020, well over 500 clinical trials have been registered at the various international and national clinical trial registry sites. 

Most of these trials will offer comparative efficacy data versus standard of care according to local COVID-19 treatment guidelines, but a handful of randomised controlled trials will also provide head-to-head evidence between high profile interventions. The figure shows the network of completed, ongoing, and planned COVID-19 interventional clinical trials of these interventions or intervention groups that are being explored in at least two trials.

Given the accelerated rate at which trial information and findings are emerging, an urgent need exists to track clinical trials, avoid unnecessary duplication of efforts, and understand what trials are being done and where. In response, a COVID-19 clinical trials registry was developed to collate all trials. Data are pulled from the International Clinical Trials Registry Platform, including those from the Chinese Clinical Trial Registry, ClinicalTrials.gov, Clinical Research Information Service – Republic of Korea, EU Clinical Trials Register, ISRCTN, Iranian Registry of Clinical Trials, Japan Primary Registries Network, and German Clinical Trials Register.

Both automated and manual searches are done to ensure minimisation of duplicated entries and for appropriateness to the research questions. Identified studies are then manually reviewed by two separate reviewers before being entered into the registry.

Concurrently, artificial intelligence (AI)-based methods were developed for data searches to identify potential clinical studies not captured in trial registries. These methods provide estimates of the likelihood of importance of a study being included in our database, such that the study can then be reviewed manually for inclusion. Use of AI-based methods saves 50–80% of the time required to manually review all entries without loss of accuracy.

Finally, content aggregator services, such as LitCovid, will be used to ensure our data acquisition strategy is complete. With this three-step process, the probability of missing important publications is greatly diminished and so the resulting data are representative of global COVID-19 research efforts.

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