Predictors at diagnosis for start of biologic disease-modifying antirheumatic drugs in patients with early rheumatoid arthritis: a cohort study

Abstract

Objectives: To investigate the relation between patient characteristics at rheumatoid arthritis (RA) diagnosis and subsequent initiation of treatment with biologic disease-modifying antirheumatic drugs (bDMARDs) or targeted synthetic DMARDs (tsDMARDs).

Design: A retrospective cohort study.

Setting and participants: Consecutive patients (N=330) with early RA (symptom duration <12 months) diagnosed at Skåne University Hospital, Malmö/Lund, Sweden, from 2012 to 2016, were included. Data on demographics, education, comorbidities and treatment were obtained from national registers.

Outcome: The relation between patient characteristics at diagnosis and time to first bDMARD/tsDMARD initiation was analysed using Cox regression models. As a secondary outcome, the relation between characteristics at diagnosis and b/tsDMARD initiation within 3 years was analysed using logistic regression.

Results: A total of 330 patients (mean age 59.2 years; SD 16.4) were included. During follow-up, 41% received a bDMARD (never preceded by a tsDMARD). Higher age at diagnosis was associated with a lower probability of starting bDMARD treatment (multivariable-adjusted HR 0.66 per SD; 95% CI 0.56 to 0.78). Anticitrullinated protein antibody (ACPA) positivity and higher tender joint count at diagnosis were also associated with subsequent bDMARD treatment initiation in multivariable analysis. A higher level of formal education and absence of comorbidities predicted start of a bDMARD in crude, but not in age-adjusted, analyses.

Conclusions: Older patients with RA were less likely to start bDMARDs, whereas ACPA-positive patients, and those with extensive joint involvement at diagnosis, were more likely to receive early bDMARD treatment. The impact of age on the subsequent start of bDMARD therapy was not explained by level of education or comorbidities, suggesting that other aspects of age influence treatment decisions in early RA.

Keywords: Aging; EPIDEMIOLOGY; Factor Analysis, Statistical; RHEUMATOLOGY.