Positive anti-citrullinated protein antibody status and small joint arthritis are consistent predictors of chronic disease in patients with very early arthritis: results from the NOR-VEAC cohort

Introduction The current 1987 American College of Rheumatology (ACR) classification criteria for rheumatoid arthritis (RA) have proven less useful in early arthritis. The objective of this study was to identify and compare predictors of three relevant outcomes of chronic arthritis in a cohort of very early arthritis patients. Methods The Norwegian Very Early Arthritis Cohort (NOR-VEAC) includes adult patients with at least one swollen joint of ≤16 weeks' duration. Patients are followed for 2 years with comprehensive clinical and laboratory examinations. Logistic regression analyses were performed to determine independent predictors of three outcomes: persistent synovitis, prescription of disease-modifying anti-rheumatic drugs (DMARDs), and established clinical RA diagnosis within one year. Results Of 384 patients eligible for one year follow-up (56.3% females, mean (SD) age 45.8 (14.7) years, median (IQR) duration of arthritis 31 (10-62) days), 14.4% were anti-CCP2 positive, and 11.2% were IgM RF positive. 98 patients (25.5%) had persistent synovitis, 106 (27.6%) had received DMARD treatment during follow-up, while 68 (17.7%) were diagnosed with RA. Consistent independent predictors across all three outcomes were positive anti-citrullinated protein antibody (ACPA) status (odds ratio (OR) 3.2, 5.6 and 19.3), respectively, and small joint arthritis (proximal interphalangeal joint (PIP), metacarpo-phalangeal joint (MCP), and/or metatarso-phalangeal joint (MTP) joint swelling) (OR 1.9, 3.5, and 3.5, respectively). Conclusions Positive ACPA status and small joint arthritis were consistent predictors of three relevant outcomes of chronic arthritis in very early arthritis patients. This consistency supports DMARD prescription as a valid surrogate endpoint for chronic arthritis. Importantly, this surrogate is used in ongoing efforts to develop new diagnostic criteria for early RA

Using observational study data as an external control group for a clinical trial: an empirical comparison of methods to account for longitudinal missing data

Background Observational data are increasingly being used to conduct external comparisons to clinical trials. In this study, we empirically examined whether different methodological approaches to longitudinal missing data affected study conclusions in this setting. Methods We used data from one clinical trial and one prospective observational study, both Norwegian multicenter studies including patients with recently diagnosed rheumatoid arthritis and implementing similar treatment strategies, but with different stringency. A binary disease remission status was defined at 6, 12, and 24 months in both studies. After identifying patterns of longitudinal missing outcome data, we evaluated the following five approaches to handle missingness: analyses of patients with complete follow-up data, multiple imputation (MI), inverse probability of censoring weighting (IPCW), and two combinations of MI and IPCW. Results We found a complex non-monotone missing data pattern in the observational study (N = 328), while missing data in the trial (N = 188) was monotone due to drop-out. In the observational study, only 39.0% of patients had complete outcome data, compared to 89.9% in the trial. All approaches to missing data indicated favorable outcomes of the treatment strategy in the trial and resulted in similar study conclusions. Variations in results across approaches were mainly due to variations in estimated outcomes for the observational data. Conclusions Five different approaches to handle longitudinal missing data resulted in similar conclusions in our example. However, the extent and complexity of missing observational data affected estimated comparative outcomes across approaches, highlighting the need for careful consideration of methods to account for missingness in this setting. Based on this empirical examination, we recommend using a prespecified advanced missing data approach to account for longitudinal missing data, and to conduct alternative approaches in sensitivity analyses