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A matrix risk model for the prediction of rapid radiographic progression in patients with rheumatoid arthritis receiving different dynamic treatment strategies: post hoc analyses from the BeSt study

Abstract

Objectives To develop a matrix model for the prediction of rapid radiographic progression (RRP) in subpopulations of patients with recent-onset rheumatoid arthritis (RA) receiving different dynamic treatment strategies.

Methods Data from 465 patients with recent-onset RA randomised to receive initial monotherapy or combination therapy were used. Predictors for RRP (increase in Sharp-van der Heijde score ≥5 after 1 year) were identified by multivariate logistic regression analysis. For subpopulations, the estimated risk of RRP per treatment group and the number needed to treat (NNT) were visualised in a matrix.

Results The presence of autoantibodies, baseline C-reactive protein (CRP) level, erosion score and treatment group were significant independent predictors of RRP in the matrix. Combination therapy was associated with a markedly reduced risk of RRP. The positive and negative predictive values of the matrix were 62% and 91%, respectively. The NNT with initial combination therapy to prevent one patient from RRP with monotherapy was in the range 2–3, 3–7 and 7–25 for patients with a high, intermediate and low predicted risk, respectively.

Conclusion The matrix model visualises the risk of RRP for subpopulations of patients with recent-onset RA if treated dynamically with initial monotherapy or combination therapy. Rheumatologists might use the matrix for weighing their initial treatment choice.

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