AB0425 Biological Therapies in Rheumatoid Arthritis: Graphical Analysis of DAS28 and CDAI Components Evolution Over Time. (9th June 2015) - Avila, M.G.; Alonso, A.; Lόpez Lasanta, M.; Pluma, A.; Quesada, E.; Marsal, S.
Abstract : Background: Routine use of composite measures to assess rheumatoid arthritis (RA) disease activity has become standard practice in rheumatology. Two of the most commonly used composite scores to measure the efficacy of biologic therapy (BT) in RA are DAS28 and CDAI scores. However, little is known yet about the long-term evolution of each of the score components over time. Objectives: Our aim was to analyze the evolution of the DAS28 and CDAI score components over time in RA patients treated with anti-TNF therapies in comparison to patients treated with anti-IL6 therapy. Methods: A total of 222 RA patients treated with BTs during the period between Dec'99 and March'13 were included. The data of the DAS28 and CDAI score components were collected from baseline and at every 3 months of therapy. Only patients with ≥12 weeks of follow up were included. To graphically analyze the temporal variation of each score component we used radar charts. In this type of multivariate data visualization technique, each component is represented as different axis and, at each time point, the mean relative improvement for each component is connected by a line. First, we graphically analyzed the evolution of each score component for each different anti-TNF therapy (infliximab (INF), etanercept (ETN), adalimumab (ADA)). Second, we analyzed the graphical evolution of all anti-TNFs in comparison to anti-IL6 therapy tocilizumab (TCZ). Results: A total of 347 BTs were analyzed (INF=100, ETN=126, ADA=79, TCZ=42). No significant differences were found between the three anti-TNFs in the DAS28 and CDAI scores at each time point during the first 96 weeks of follow up. However, radar charts showed different evolution patterns of the components of the two scores DAS28 and CDAI (Figure 1 ). Compared to ADA or INF, treatment with ETN shown a more regular pattern of improvement. In ADA and INF the relative weight of each DAS28-CDAI component at each time period was variable, suggesting a different mode of action for monoclonal therapies. These differences were observed independently of the line of treatment where anti-TNF treatment was used. When we compared the radar charts of the combined anti-TNF therapies against the anti-IL6 treatment, we found a markedly different graphical evolution. While the treatment with anti-TNFs showed a predominant improvement in the articular component, the radar chart indicated a higher improvement in the ESR component for anti-IL6 therapy. Conclusions: Using a graphical analysis approach we have identified the presence of differential evolution patterns according to the treatment type. The results of this study are useful to understand the differential mechanisms of action of each treatment, and could help to explain the differences observed in clinical trials, meta-analyses and observational studies. Disclosure of Interest: None declared