RT Journal Article
SR Electronic
T1 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
JF Annals of the Rheumatic Diseases
JO Ann Rheum Dis
FD BMJ Publishing Group Ltd and European League Against Rheumatism
SP 1333
OP 1337
DO 10.1136/ard.2009.121160
VO 69
IS 7
A1 Visser, K
A1 Goekoop-Ruiterman, Y P M
A1 de Vries-Bouwstra, J K
A1 Ronday, H K
A1 Seys, P E H
A1 Kerstens, P J S M
A1 Huizinga, T W J
A1 Dijkmans, B A C
A1 Allaart, C F
YR 2010
UL http://ard.bmj.com/content/69/7/1333.abstract
AB 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.