Abstract
The COVID-19 pandemic has led to tremendous investment in clinical studies to generate much-needed knowledge on the prevention, diagnosis, treatment and long-term effects of the disease. Case report forms, comprised of questions and answers (variables), are commonly used to collect data in clinical trials. Maximizing the value of study data depends on data quality and on the ability to easily pool and share data from several sources. ISARIC, in collaboration with the WHO, has created a case report form that is available for use by the scientific community to collect COVID-19 trial data. One of such research initiatives collecting and analyzing multi-country and multi-cohort COVID-19 study data is the Horizon 2020 project ORCHESTRA. Following the ISO/TS 21564:2019 standard, a mapping between five ORCHESTRA studies' variables and the ISARIC Freestanding Follow-Up Survey elements was created. Measures of correspondence of shared semantic domain of 0 (perfect match), 1 (fully inclusive match), 2 (partial match), 4 (transformation required) or 4* (not present in ORCHESTRA) as compared to the target code system, ORCHESTRA study variables, were assigned to each of the elements in the ISARIC FUP case report form (CRF) which was considered the source code system. Of the ISARIC FUP CRF's variables, around 34% were found to show an exact match with corresponding variables in ORCHESTRA studies and about 33% showed a non-inclusive overlap. Matching variables provided information on patient demographics, COVID-19 testing, hospital admission and symptoms. More in-depth details are covered in ORCHESTRA variables with regards to treatment and comorbidities. ORCHESTRA's Long-Term Sequelae and Fragile population studies' CRFs include 32 and 27 variables respectively which were evaluated as a perfect match to variables in the ISARIC FUP CRF. Our study serves as an example of the kind of maps between case report form variables from different research projects needed to link ongoing COVID-19 research efforts and facilitate collaboration and data sharing. To enable data aggregation across two data systems, the information they contain needs to be connected through a map to determine compatibility and transformation needs. Combining data from various clinical studies can increase the power of analytical insights.