Clinical response within 12 weeks as a predictor of future low disease activity in patients with early RA: results from the TEAR Trial.

OBJECTIVE: Rapidly predicting future outcomes based on short-term clinical response would be helpful to optimize rheumatoid arthritis (RA) management in early disease. Our aim was to derive and validate a clinical prediction rule to predict low disease activity (LDA) at 1 year among patients participating in the Treatment of Early Aggressive Rheumatoid Arthritis (TEAR) trial escalating RA therapy by adding either etanercept or sulfasalazine + hydroxychloroquine [triple therapy (TT)] after 6 months of methotrexate (MTX) therapy.

METHODS: Eligible subjects included in the derivation cohort (used for model building, n = 186) were participants with moderate or higher disease activity [Disease Activity Score 28-erythrocyte sedimentation rate (DAS-ESR) > 3.2] despite 24 weeks of MTX monotherapy who added either etanercept or sulfasalazine + hydroxychloroquine. Clinical characteristics measured within the next 12 weeks were used to predict LDA 1 year later using multivariable logistic regression. Validation was performed in the cohort of TEAR patients randomized to initially receive either MTX + etanercept or TT.

RESULTS: The derivation cohort yielded 3 prediction models of varying complexity that included age, DAS28 at various timepoints, body mass index, and ESR (area under the receiver-operator characteristic curve up to 0.83). Accuracy of the prediction models ranged between 80% and 95% in both derivation and validation cohorts, depending on the complexity of the model and the cutpoints chosen for response and nonresponse. About 80% of patients could be predicted to be responders or nonresponders at Week 12.

CONCLUSION: Clinical data collected early after starting or escalating disease-modifying antirheumatic drug/biologic treatment could accurately predict LDA at 1 year in patients with early RA. For patients predicted to be nonresponders, treatment could be changed at 12 weeks to optimize outcomes.