Tocilizumab as monotherapy or combination therapy for treating active rheumatoid arthritis : A meta-analysis of efficacy and safety reported in randomized controlled trials

Background: Previous studies in patients with rheumatoid arthritis (RA) have shown that switching to tocilizumab (TCZ) monotherapy (TCZMONO) or combination therapy (TCZCOMBI) with conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs) is efficacious in reducing disease activity in patients with inadequate response to csDMARDs. However, hitherto there is no consensus on whether TCZMONO is as effective as TCZCOMBI. The objective of this study was therefore to evaluate the efficacy and safety of TCZMONO versus add-on TCZCOMBI and both TCZ therapies versus continuing the current csDMARD therapy, by performing a systematic review and meta-analyses. Method: The MEDLINE, EMBASE and CENTRAL databases were searched until February 2016 for relevant randomized controlled trials (RCTs). We performed meta-analyses of Disease Activity Score in 28 joints (DAS28COMBI strategy. However, the risk of SAEs was also significantly higher using this strategy (RR 1.40; 95 % CI 1.03, 1.92, p=0.03). Pooled effect estimates showed statistical superiority of switching to either TCZ strategy compared to continuing csDMARD therapy. Conclusions: In the management of active RA, almost similar efficacy can be expected in patients unable to tolerate csDMARDs, who switch to TCZMONO compared to inadequate responders switching to add-on TCZCOMBI. Although TCZCOMBI is marginally superior to TCZMONO in achieving DAS2

Systems approach for classifying the response to biological therapies in patients with rheumatoid arthritis in clinical practice

Introduction: Biological therapies have greatly improved the treatment efficacy in rheumatoid arthritis (RA). However, in clinical practice a significant proportion of patients experience an inadequate response to treatment. The aim of this study is to classify responding and non-responding rheumatoid arthritis patients treated with biological therapies, based on clinical parameters and symptoms used in Western and Chinese medicine. Methods: Cold and Heat symptoms accessed by a Chinese medicine (CM) questionnaire and Western clinical data were collected as baseline data, before initiating biological therapy. Categorical principal components analysis with forced classification (CATPCA-FC) approach was applied to the baseline data set to classify responders and non-responders. Results: In this study, 61 RA patients were characterized using a CM questionnaire and clinical measurements. The combination of baseline symptoms (‘preference for warm food’, ‘weak tendon severity’) and clinical parameters (positive rheumatoid factor/anti-cyclic citrullinated peptide antibody, C-reactive protein, creatinine) were able to differentiate responders from non-responders to biological therapies with a positive predictive value of 82.35% and a misclassification rate of 24.59%. Adding CM symptom variables in addition to clinical data did not improve the classification of responders, but it did show 8.3% improvement in classifying non-responders. Conclusions: No significant differences were found between the three classification models. Adding CM symptoms to the clinical parameters in the combined model improved the classification of non-responders. Although this improvement is not significant in the current study, we consider it worthwhile to further investigate the potential of adding symptom variables for improving treatment efficacy

Does disease activity add to functional disability in estimation of utility for rheumatoid arthritis patients on biologic treatment?

Objective. Treatment in general is mostly directly aimed at disease activity, and measures such as the DAS28 might therefore present important additional information. Our aim was to develop and validate a model that uses a combination of disease activity (DAS28) and HAQs to estimate EuroQoL 5-dimension scale (EQ5D) utilities. Methods. Longitudinal data from a cohort study in RA patients from the Utrecht Rheumatoid Arthritis Cohort study Group (Stichting Reumaonderzoek Utrecht) who started treatment with a biologic drug were used for mapping and validation. All 702 observations, including DAS28, HAQ and EQ5D assessed at the same time points, were used. The observations were randomly divided into a subset for development of the model (n = 428 observations) and a subset for validation (n = 274). A stepwise multivariable regression analysis was used to test the association of DAS28 (components) and HAQ (domains) with EQ5D. Model performance was assessed using the explained variance (R2) and root mean square errors. Observed and predicted utility scores were compared to check for under- or overestimation of the scores. Finally, the performance of the model was compared with published mapping models. Results. Lower DAS28 score and HAQ items dressing and grooming, arising, eating, walking and activities were associated with higher EQ5D scores. The final model had an explained variance of 0.35 and a lower root mean square error as compared with other models tested. The agreement between predicted and observed scores was fair. Conclusion. HAQ components estimate EQ5D better than total HAQ. Adding DAS28 to HAQ components does not result in better utility estimations

Does disease activity add to functional disability in estimation of utility for rheumatoid arthritis patients on biologic treatment?

Objective. Treatment in general is mostly directly aimed at disease activity, and measures such as the DAS28 might therefore present important additional information. Our aim was to develop and validate a model that uses a combination of disease activity (DAS28) and HAQs to estimate EuroQoL 5-dimension scale (EQ5D) utilities. Methods. Longitudinal data from a cohort study in RA patients from the Utrecht Rheumatoid Arthritis Cohort study Group (Stichting Reumaonderzoek Utrecht) who started treatment with a biologic drug were used for mapping and validation. All 702 observations, including DAS28, HAQ and EQ5D assessed at the same time points, were used. The observations were randomly divided into a subset for development of the model (n = 428 observations) and a subset for validation (n = 274). A stepwise multivariable regression analysis was used to test the association of DAS28 (components) and HAQ (domains) with EQ5D. Model performance was assessed using the explained variance (R2) and root mean square errors. Observed and predicted utility scores were compared to check for under- or overestimation of the scores. Finally, the performance of the model was compared with published mapping models. Results. Lower DAS28 score and HAQ items dressing and grooming, arising, eating, walking and activities were associated with higher EQ5D scores. The final model had an explained variance of 0.35 and a lower root mean square error as compared with other models tested. The agreement between predicted and observed scores was fair. Conclusion. HAQ components estimate EQ5D better than total HAQ. Adding DAS28 to HAQ components does not result in better utility estimations