P074 Crohn’s disease clinical data standards: Adoption and implementation to promote data sharing and reuse.
Owen, J.(1);
(1)CDISC, Standards Development, Austin, United States
Background
Creation and adoption of clinical data standards will transform incompatible and disparate data into universal and illuminating information, facilitating discoveries that could have invaluable impact on Crohn’s Disease clinical research. Implementation of CDISC standards deliver on the promise of FAIR data through consistent organization and analysis that allow all researchers to leverage information from studies globally.
Methods
With support from The Leona M. and Harry B. Helmsley Charitable Trust and following the CDISC consensus-based standards development process, a team of Crohn’s disease and standards development experts was created to develop clinical data standards for Crohn’s disease.The CDISC standards development process consists of five stages:
- Scoping
- Concept Modelling
- Standards Development
- Internal Review
- Public Review
- Publication
Results
Free and publicly available data standards are anticipated to be available in May 2021 in the following areas:
- Questionnaires, Ratings and Scales (including standard symptom measures, patient/investigator reported outcomes, and socio-economic measures)
- Prior and Baseline, and On-Study Treatments (including response to prior treatment)
- Disease Classification (location and phenotypic descriptions of the disease)- Endoscopy Assessments
- Cross Section Imaging Assessments (including CT, MRI and Ultrasound)
- Histopathology of Biopsy Samples
- Biomarkers of Interest for Crohn’s Disease
Conclusion
Widespread promotion of the standards for researchers to adopt and implement is of highest importance. CDISC provides complementary education courses and implementation information to assist in adoption for academic teams new to CDISC standards. Widespread adoption of the standards will bring clarity to Crohn’s Disease data and will enable the accessibility, interoperability, and reusability of data (FAIR).