Assessing data quality of rheumatoid and psoriatic arthritis patients in the

Purpose: Rheumatoid and Psoriatic Arthritis (RA and PsA) are autoimmune diseases that cause debilitating joint pain. Disease-modifying antirheumatic drugs (DMARDs) are recommended for the treatment of both conditions. However, real-world evidence studies would help characterize compliance with these recommendations. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standardizes electronic health record (EHR) data, allowing for research that incorporates multiple data sources. We are interested in determining whether OMOP CDM data on RA and PsA are fit-for-use. Methods: We selected diagnosis codes for RA and PsA that were the basis for each phenotype. We used a data quality checklist to evaluate 5 domains systematically: conformance, completeness, concordance, plausibility, and temporality. Results: Most phenotype-defining ICD source codes mapped to SNOMED. Both cohorts had low concept prevalences. Most concept correlations were weak (ρ ≤ 0.5). The relative distribution of DMARD ingredients in both cohorts was consistent with prior studies. The proportion of the RA and PsA cohorts that had data for timing between event calculations ranged from 13% to 85% and 16% to 81%, respectively. Despite variability in concept sequence analysis, symptomatic treatment concepts for RA and PsA were preceded by rheumatoid factor concepts, followed by DMARD therapy and disease diagnosis concepts. Conclusion: We have shown a novel implementation of our data quality framework on autoimmune disease cohorts.