Can rheumatoid arthritis (RA) registries provide contextual safety data for modern RA clinical trials? The case for mortality and cardiovascular disease

Background We implemented a novel method for providing contextual adverse event rates for a randomised controlled trial (RCT) programme through coordinated analyses of five RA registries, focusing here on cardiovascular disease (CVD) and mortality. Methods Each participating registry (Consortium of Rheumatology Researchers of North America (CORRONA) (USA), Swedish Rheumatology Quality of Care Register (SRR) (Sweden), Norfolk Arthritis Register (NOAR) (UK), CORRONA International (East Europe, Latin America, India) and Institute of Rheumatology, Rheumatoid Arthritis (IORRA) (Japan)) defined a main cohort from January 2000 onwards. To address comparability and potential bias, we harmonised event definitions and defined several subcohorts for sensitivity analyses based on disease activity, treatment, calendar time, duration of follow-up and RCT exclusions. Rates were standardised for age, sex and, in one sensitivity analysis, also HAQ. Results The combined registry cohorts included 57 251 patients with RA (234 089 person-years)—24.5% men, mean (SD) baseline age 58.2 (13.8) and RA duration 8.2 (11.7) years. Standardised registry mortality rates (per 100 person-years) varied from 0.42 (CORRONA) to 0.80 (NOAR), with 0.60 for RCT patients. Myocardial infarction and major adverse cardiovascular events (MACE) rates ranged from 0.09 and 0.31 (IORRA) to 0.39 and 0.77 (SRR), with RCT rates intermediate (0.18 and 0.42), respectively. Additional subcohort analyses showed small and mostly consistent changes across registries, retaining reasonable consistency in rates across the Western registries. Additional standardisation for HAQ returned higher mortality and MACE registry rates. Conclusions This coordinated approach to contextualising RA RCT safety data demonstrated reasonable differences and consistency in rates for mortality and CVD across registries, and comparable RCT rates, and may serve as a model method to supplement clinical trial analyses for drug development programmes.