A matrix tool developed to predict golimumab (GLM) treatment outcomes may assist clinicians in determining which patients with rheumatoid arthritis (RA) may most benefit from anti-tumor necrosis factor (TNF) therapy, according to research published in Rheumatology.  The tool combines baseline demographic data with RA disease characteristics to aid in identifying good candidates for anti-TNF therapy.

Emphasis is placed on attaining low disease activity (LDA) or remission in RA treatment to prevent morbidity. While prior research studies have analyzed predictors of disease outcomes with anti-TNF therapy, the prognostic ability of any single predictor is less clear. 

“The goal of these analyses was to develop a tool that can be used to assist in decision making to optimize treatment goal attainment in patients with RA who have failed [disease-modifying antirheumatic drug (DMARD)] treatment,” the authors wrote. 


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To identify patients  who are more likely to benefit from GLM treatment, Nathan Bastesaeger, MD, Department of Medical Affairs, MSD Danmark, Ballerup, Denmark, and colleagues analyzed data from the Subcutaneous Golimumab Plus DMARDs for Rheumatoid Arthritis Followed by Intravenous/Subcutaneous GLM Strategy (GO-MORE) trial, an open-label study assessing efficacy of adding GLM to conventional DMARD therapy (ClinicalTrials.gov identifier NCT00975130).

Patients who were >=18 years old, with active RA according to the 1987 revised American College of Rheumatology (ACR) criteria, taking at least on DMARD, biologic-naïve, with a history of response to conventional treatment.  Study participants received monthly subcutaneous autoinjected doses of GLM 50mg for 6 months.  Efficacy and safety of GLM treatment were re-assessed at months 1, 3, and 6.  

Primary outcome measures were defined as the number of study participants who achieved good or moderate European League Against Rheumatism (EULAR) Response 6 months after treatment initiation and the number of participants who achieved Disease Activity Score 28-Erythrocyte Sedimentation Rate (DAS28-ESR) remission at the start of month 11 after treatment initiation.  

Researchers utilized regression models to determine the baseline factors that were most predictive of disease remission (defined by DAS28-ESR <2.6) at month 6 and LDA (defined by DAS28-ESR ⩽3.2) at month 1 in secondary analyses. Continuous predictor variables such as age and total joint count were transformed to 3 or 4 categorical variables in order to convert data into a prediction matrix tool. After multivariable analysis,  predicted LDA and remission rates were used to construct a series of matrix tools.

Data from 3,280 study participants were used to identify factors associated with greater likelihood of LDA or remission, including male sex, younger age, lower health assessment questionnaire (HAQ) scores, ESR or CRP, and tender joint counts, in addition to the absence of comorbidities. 

“In models predicting 1-, 3- and 6-month LDA or remission, area under the receiver operating curve was 0.648–0.809 (R= 0.0397–0.1078). The models also predicted 6-month HAQ and EuroQoL-5-dimension scores,” the authors found.

The models also predicted 6–month HAQ and EuroQoL–5–dimension scores. A series of matrices were then developed to determine the predicted rates of remission and LDA in RA.

Summary and Clinical Applicability

A predictive matrix tool was developed to predict treatment outcomes in patients with RA treated with GLM.  The objective of creating this tool was to provide clinicians with practical guidance to identify which patients would be good candidates for GLM therapy.

“[This research is] important when considering the implications of treatment recommendations and reimbursement criteria on both patient selection criteria and patient outcomes as patients are considered for anti-TNF treatment”, the authors conclude.

Limitations and Disclosures

This study was not designed to evaluate if GLM was superior to other therapies for DMARD-resistant RA.  It also lacked randomization to directly compare patients on anti-TNF pharmacotherapy as compared to placebo or alternative treatment. 

This study was supported by a grant from Schering-Plough/Merck & Co., Inc.

Reference

Vastesaeger N, Kutzbach AG, Amital H, et al. Prediction of remission and low disease activity in disease-modifying anti-rheumatic drug-refractory patients with rheumatoid arthritis treated with golimumab. Rheumatology (Oxford). 2016;  doi:10.1093/rheumatology/kew179 First published online: April 25, 2016