Novel methodology to discern predictors of remission and patterns of disease activity over time using rheumatoid arthritis clinical trials data

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

Abstract

Objectives To identify predictors of remission and disease activity patterns in patients with rheumatoid arthritis (RA) using individual participant data (IPD) from clinical trials.
Methods Phases II and III clinical trials completed between 2002 and 2012 were identified by systematic literature review and contact with UK market authorisation holders. Anonymised baseline and follow-up IPD from non-biological arms were amalgamated. Multiple imputation was used to handle missing outcome and covariate information. Random effects logistic regression was used to identify predictors of remission, measured by the DAS28 score at 6 months. Novel latent class mixed models characterised DAS28 over time.
Results IPD of 3290 participants from 18 trials were included. Of these participants, 92% received methotrexate (MTX). Remission rates were estimated at 8.4% (95%CI: 7.4%-9.5%) overall, 17% (95%CI: 14.8%-19.4%) for MTX-naïve early RA patients, and 3.2% (95%CI: 2.4%-4.3%) for those with prior MTX exposure at entry. In prior MTX-exposed patients, lower baseline DAS28 and MTX-re-initiation were associated with remission. In MTX-naïve patients, being young, white, male, with better functional and mental health, lower baseline DAS28 and receiving concomitant glucocorticoids were associated with remission. Three DAS28 trajectory sub-populations were identified in MTX-naïve and MTX-exposed patients. A number of variables were associated with sub-population membership and DAS28 levels within sub-populations.
Conclusions Predictors of remission differed between MTX-naïve and prior MTX-exposed patients at entry. Latent class mixed models supported differential non-biologic therapy response, with three distinct trajectories observed in both MTX-naïve and MTX-exposed patients. Findings should be useful when designing future RA trials and interpreting results of biomarker studies.

Details

Original languageEnglish
Article numbere000721
JournalRMD Open
Volume4
Issue number2
Early online date25 Oct 2018
Publication statusPublished - 25 Oct 2018