The de novo FAIRification process of a registry for vascular anomalies

Abstract

Background Patient data registries that are FAIR—Findable, Accessible, Interoperable, and Reusable for humans and computers—facilitate research across multiple resources. This is particularly relevant to rare diseases, where data often are scarce and scattered. Specific research questions can be asked across FAIR rare disease registries and other FAIR resources without physically combining the data. Further, FAIR implies well-defined, transparent access conditions, which supports making sensitive data as open as possible and as closed as necessary. Results We successfully developed and implemented a process of making a rare disease registry for vascular anomalies FAIR from its conception—de novo. Here, we describe the five phases of this process in detail: (i) pre-FAIRification, (ii) facilitating FAIRification, (iii) data collection, (iv) generating FAIR data in real-time, and (v) using FAIR data. This includes the creation of an electronic case report form and a semantic data model of the elements to be collected (in this case: the “Set of Common Data Elements for Rare Disease Registration” released by the European Commission), and the technical implementation of automatic, real-time data FAIRification in an Electronic Data Capture system. Further, we describe how we contribute to the four facets of FAIR, and how our FAIRification process can be reused by other registries. Conclusions In conclusion, a detailed de novo FAIRification process of a registry for vascular anomalies is described. To a large extent, the process may be reused by other rare disease registries, and we envision this work to be a substantial contribution to an ecosystem of FAIR rare disease resources.

Clinical phenotype of adolescent and adult patients with extracranial vascular malformation

“…[31][32][33][34] The reported prevalence of CVM between 0.1-1.5% originating from the aforementioned studies is probably an underestimation in the overall population context. In the absence of cross-sectional studies, the exact prevalence and anatomical predisposition of extracranial CVM remains unclear; we need to await results of large registry studies to estimate a realistic prevalence 35 and to adequately adjust the treatment resources.…”

Towards FAIRification of sensitive and fragmented rare disease patient data: challenges and solutions in European reference network registries

“…When compared to our previous FAIRification experience involving a single FAIR data steward [ 14 ], a team supported FAIRification effort resulted in a more robust approach. First, the diverse backgrounds and the collaboration among team members facilitated experience exchange and FAIRification discussions.…”

“…The unique experiences from the interaction between the FAIR data stewards and the diverse RD registries are summarised below: Clarify FAIRification goals before implementation. Available FAIRification workflows recommend that defining the FAIRification goal(s) is the first key step in FAIRification [ 11 , 14 ]. Nonetheless, some of the RD registries have not completed this step yet.…”

“…Clarify FAIRification goals before implementation. Available FAIRification workflows recommend that defining the FAIRification goal(s) is the first key step in FAIRification [ 11 , 14 ]. Nonetheless, some of the RD registries have not completed this step yet.…”

“…Data can be made FAIR retrospectively, often long after they were collected, which may require extensive efforts to understand the meaning of the data [ 11 – 13 ]. Data can also be made FAIR when they are being collected, where the FAIRification steps are embedded in the data collection tool [ 14 ]. The latter was implemented for a VASCERN ERN registry, where data are made FAIR automatically and in real-time upon collection [ 15 ].…”