A Web-based Platform to Share Harmonized Results from COVID-19 Clinical Studies

COVID-19 has caused a worldwide pandemic, accompanied by a high number of deaths and hospitalizations. Multiple preventative vaccines and variety of COVID-19 treatments have been developed and explored. This large volume of scientific work led to an extensive number of COVID-19 publications, which resulted in the necessity to standardize, store, share, and investigate research results in a harmonized manner. Attempts to standardize and share COVID-19 research data have been lacking. The purpose of the ReMeDy platform is to provide an intelligent informatics solution of integrating diverse COVID-19 trial outcomes and omics data across COVID-19 research studies. To test the platform, we utilized 48 COVID-19 observational retrospective studies. The robustness of the platform was validated through the ability to efficiently organize the diverse data elements. Next steps include expanding our database through the inclusion of all published COVID-19 studies. ReMeDy is located at https://remedy.mssm.edu/.

Characteristics of Electronic Informed Consent Platforms for Consenting Patients to Research Studies: A Scoping Review.

Informed consent process assures that research study participants are properly informed about the study prior to their consent. Due to the increasing significance of electronic informed consent (eIC) platforms, particularly during the COVID-19 pandemic, we conducted a scoping review of eIC systems to address the following characteristics: 1) technological features of current eIC platforms, 2) eIC platforms usability and efficacy, and 3) areas for future eIC research. We performed a literature search using publically available PubMed repository, where we included studies discussing an eIC platform or multimedia educational module given to patients prior to signing a consent form. In addition, we tracked first author, year of publication, sample size, study location, eIC procedure, methodology, and eIC's comparison to paper consent. Our results showed that with a few noted exceptions, electronic consent improves patient usability, satisfaction, knowledge, and trust scores when compared to traditional paper consent.

NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov.

Core outcome sets (COS) are necessary to ensure the systematic collection, metadata analysis and sharing the information across studies. However, development of an area-specific clinical research is costly and time consuming. ClinicalTrials.gov, as a public repository, provides access to a vast collection of clinical trials and their characteristics such as primary outcomes. With the growing number of COVID-19 clinical trials, identifying COSs from outcomes of such trials is crucial. This paper introduces a semi-automatic pipeline that can efficiently identify, aggregate and rank the COS from the primary outcomes of COVID-19 clinical trials. Using Natural language processing (NLP) techniques, our proposed pipeline successfully downloads and processes 5090 trials from all over the world and identifies COVID-19-specific outcomes that appeared in more than 1% of the trials. The top-of-the-list outcomes identified by the pipeline are mortality due to COVID-19, COVID-19 infection rate and COVID-19 symptoms.

Identifying Core Outcome Sets in COVID-19 Clinical Trials Using ClinicalTrials.gov.

Introduction of core outcome sets (COS) facilitates evidence synthesis, transparency in outcome reporting, and standardization in clinical research. However, development of COS may be a time consuming and expensive process. Publicly available repositories, such as ClinicalTrials.gov (CTG), provide access to a vast collection of clinical trial characteristics including primary and secondary outcomes, which can be analyzed using a comprehensive set of tools. With growing number of COVID-19 clinical trials, COS development may provide crucial means to standardize, aggregate, share, and analyze diverse research results in a harmonized way. This study was aimed at initial assessment of utility of CTG analytics for identifying COVID-19 COS. At the time of this study, January, 2021, we analyzed 120 ongoing NIH-funded COVID-19 clinical trials initiated in 2020 to inform COVID-19 COS development by evaluating and ranking clinical trial outcomes based on their structured representation in CTG. Using this approach, COS comprised of 25 major clinical outcomes has been identified with mortality, mental health status, and COVID-19 antibodies at the top of the list. We concluded that CTG analytics can be instrumental for COVID-19 COS development and that further analysis is warranted including broader number of international trials combined with more granular approach and ontology-driven pipelines for outcome extraction and curation.