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Joint Bone Spine. 2018 Mar;85(2):219-226. doi: 10.1016/j.jbspin.2017.02.015. Epub 2017 Mar 28.

A multi-parameter response prediction model for rituximab in rheumatoid arthritis.

Author information

1
Amsterdam rheumatology and immunology center, location VU university medical center, P.O. box 7057, 1007MB Amsterdam, The Netherlands. Electronic address: td.dejong@vumc.nl.
2
Inserm UMRS_938, DHU i2B, rheumatology department, Saint-Antoine hospital, université Paris 06, AP-HP, 184, rue du Faubourg-Saint-Antoine, 75012 Paris, France.
3
Amsterdam rheumatology and immunology center, location VU university medical center, P.O. box 7057, 1007MB Amsterdam, The Netherlands; Amsterdam rheumatology and immunology center, location Reade, P.O. box 58271, 1040HG Amsterdam, The Netherlands.
4
Department of pathology, VU university medical center, P.O. box 7057, 1007MB Amsterdam, The Netherlands.
5
Department of epidemiology and biostatistics, VU university medical center, P.O. box 7057, 1007MB Amsterdam, The Netherlands.
6
Amsterdam rheumatology and immunology center, location VU university medical center, P.O. box 7057, 1007MB Amsterdam, The Netherlands.
7
Inserm U1184, rheumatology department, center for immunology of viral infections and autoimmune diseases, hôpitaux universitaires Paris-Sud, université Paris-Sud, AP-HP, 78, rue du Général-Leclerc, 94275 Le Kremlin-Bicêtre, France.

Abstract

OBJECTIVES:

To validate the IFN response gene (IRG) set for the prediction of non-response to rituximab in rheumatoid arthritis (RA) and assess the predictive performance upon combination of this gene set with clinical parameters.

METHODS:

In two independent cohorts of 93 (cohort I) and 133 (cohort II) rituximab-starting RA patients, baseline peripheral blood expression of eight IRGs was determined, and averaged into an IFN score. Predictive performance of IFN score and clinical parameters was assessed by logistic regression. A multivariate prediction model was developed using a forward stepwise selection procedure. Patients with a decrease in disease activity score (ΔDAS28)≥1.8 after 6 months of therapy were considered responders.

RESULTS:

The mean IFN score was higher in non-responders compared to responders in both cohorts, but this difference was most pronounced in patients who did not use prednisone, as described before. Univariate analysis in cohort I showed that baseline DAS28, IFN score, DMARD use and negativity for IgM-RF and/or ACPA were associated with rituximab non-response. The multivariate model consisted of DAS28, IFN score and DMARD use, which showed an area under the curve (AUC) of 0.82. In cohort II, this model revealed a comparable AUC in PREDN-negative patients (0.78), but AUC in PREDN-positive patients was significantly lower (0.63), which seemed due to effect modification of the IFN score by prednisone.

CONCLUSIONS:

Combination of predictive parameters provided a promising model for the prediction of non-response to rituximab, with possibilities for optimization via definition of the exact interfering effect of prednisone on IFN score.

TRIAL REGISTRATION (COHORT II, SMART TRIAL):

NCT01126541, registered 18 May 2010.

KEYWORDS:

Prediction; Rheumatoid arthritis; Rituximab; Type I interferon

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