DAS28, CDAI and SDAI cut-offs do not translate the same information: results from the Rheumatic Diseases Portuguese Register Reuma.pt.

OBJECTIVES: . The 28-joint DAS (DAS28), clinical disease activity index (CDAI) and simplified disease activity index (SDAI) are indices frequently used to assess disease activity in RA patients. Cut-off values were defined to classify the states of RA disease activity: remission, low, moderate and high. The aim of this work was to assess disease activity states classified by DAS28, CDAI and SDAI and to analyse their agreement in the Rheumatic Diseases Portuguese Register Reuma.pt. METHODS: . A total of 2795 patients and 14 440 visits were selected from Reuma.pt for analysis. Pearson's correlation coefficients (PCCs) were calculated for the three indices. McNemar's chi-squared tests, PCCs and kappa statistics were performed to analyse and compare the distribution of visits among all disease activity states and indices. RESULTS: A strong correlation was found between the three indices throughout the 14 440 visits: r = 0.874 for DAS28/CDAI, r = 0.877 for DAS28/SDAI and r = 0.984 for CDAI/SDAI (all PCCs with P < 0.0001). However, when categorization in the different disease activity states was analysed, McNemar's chi-squared tests and PCCs revealed significant disagreement between the cut-offs of the three indices. CONCLUSION: DAS28, CDAI and SDAI cut-offs do not translate into the same clinical information in Reuma.pt. Although this might be expected for the original DAS28 cut-offs, when compared with CDAI and SDAI significant disagreement was also found for the DAS28 modified cut-offs. For visits where patients are in CDAI or SDAI remission, we also find disagreement between these two indices, which may contradict previous conclusions that acute phase reactants add little to composite disease activity indices for RA.

Comparative effectiveness and predictors of response to tumour necrosis factor inhibitor therapies in rheumatoid arthritis.

OBJECTIVES: Adalimumab, etanercept and infliximab are effective TNF inhibitors (TNFis) in the treatment of RA, but no randomized clinical trials have compared the three agents. Prior observational data are not consistent. We compared their effectiveness over 1 year in a prospective cohort. METHODS: Analyses were performed on subjects' first episode of TNFi use in the Rheumatic Diseases Portuguese Register, Reuma.pt. The primary outcome was the proportion of patients with European League Against Rheumatism good response sustained at two consecutive observations separated by 3 months during the first year of TNFi use. Comparisons were performed using conventional adjusted logistic regression, as well as matching subjects across the three agents using a propensity score. In addition, baseline predictors of treatment response to TNFi were identified. RESULTS: The study cohort included 617 RA patients, 250 starting etanercept, 206 infliximab and 161 adalimumab. Good response was achieved by 59.6% for adalimumab, 59.2% for etanercept and 51.9% for infliximab (P = 0.21). The modelled probability of good response did not significantly differ across agents (etanercept vs adalimumab OR = 0.97, 95% CI 0.55, 1.71; etanercept vs infliximab OR = 1.25, 95% CI 0.74, 2.12; infliximab vs adalimumab OR = 0.80, 95% CI 0.47, 1.36). Matched propensity score analyses also showed no significant treatment response differences. Greater educational attainment was a predictor of better response, while smoking, presence of ACPA, glucocorticoid use and worse physician assessment of disease activity at baseline each predicted a reduced likelihood of treatment response. CONCLUSION: Over 1 year, we found no difference in effectiveness between adalimumab, etanercept and infliximab.

TRAF1/C5 but not PTPRC variants are potential predictors of rheumatoid arthritis response to anti-tumor necrosis factor therapy.

BACKGROUND: The aim of our work was to replicate, in a Southern European population, the association reported in Northern populations between PTPRC locus and response to anti-tumor necrosis factor (anti-TNF) treatment in rheumatoid arthritis (RA). We also looked at associations between five RA risk alleles and treatment response. METHODS: We evaluated associations between anti-TNF treatment responses assessed by DAS28 change and by EULAR response at six months in 383 Portuguese patients. Univariate and multivariate linear and logistic regression analyses were performed. In a second step to confirm our findings, we pooled our population with 265 Spanish patients. RESULTS: No association was found between PTPRC rs10919563 allele and anti-TNF treatment response, neither in Portuguese modeling for several clinical variables nor in the overall population combining Portuguese and Spanish patients. The minor allele for RA susceptibility, rs3761847 SNP in TRAF1/C5 region, was associated with a poor response in linear and logistic univariate and multivariate regression analyses. No association was observed with the other allellic variants. Results were confirmed in the pooled analysis. CONCLUSION: This study did not replicate the association between PTPRC and the response to anti-TNF treatment in our Southern European population. We found that TRAF1/C5 risk RA variants potentially influence anti-TNF treatment response.