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Background:For treating Rheumatoid Arthritis (RA) it’s clear that Treat to Target has presented the best strategy. The best outcome for patients has been attained by accepting clinical remission as the final objective in treatment. The development of new drugs for the treatment of RA has dramatically modified the approach clinicians have on the disease, however, the treatment of inflammatory conditions represents a challenge due to the multiple factors can affect its response.It is known that non-adherence represents an increase in mortality, morbidity and healthcare costs.1,2.Emphasis should be made on one of the most simple and effective variables that impact the effectiveness of treatment: adherence to treatment itself.Objectives:This work aimed to evaluate the impact of methotrexate adherence on treatment outcome in patients with RA.Methods:An observational, cross sectional study was performed using medical records from the Rheumatology Clinic at University Hospital from UANL Jose Eleuterio Gonzalez, Monterrey, México; the data was collected from 03/16/2018 to 01/29/2020. RA patients visits included fulfilled the 2010 ACR/EULAR classification, had their disease activity measured by Disease Activity Score 28 ESR (DAS28 ESR) and were treated with methotrexate as their primary disease modifying antirheumatic-drug (DMRAD). Patient disease activity was classified in 4 groups according to the DAS28 ESR score as 3.2-5.1 moderate activity, >5.1 high activity.Adherence to medication was evaluated utilizing a self-reported assessment that was applied per patient’s visit. Patients were classified into 4 groups according to the percentage of the prescribed doses of Methotrexate that they abided to. Statistical analysis was performed using IBM SPSS 21 statistical package. Descriptive analyses were performed with frequencies (%) and the correlation between the calculated disease activity versus patient adherence to treatment was calculated using Pearson correlation coefficient.P-values Results:A total of 795 patients visits were included. They were 92.33% female, mean (SD) age was 52.44 (±12.99) (Table 1). The most frequent routes for methotrexate were Oral 603 (75.84%), Subcutaneous 113 (14.21%) and Intramuscular 79 (9.93%). A significant correlationr(795) =0.-183, (pTable 1.Baseline CharacteristicsN= 795Age, mean SD52.44 (±12.99)Sex, n (%) Female734 Male61Methotrexate (mg/week)Average Dose19.94Median Dose20Intramuscular79 (9.93%)Subcutaneous113 (14.21%)Oral603 (75.84%)Disease Activity accordingto DAS28 ESR, n (%)Remission343 (42.3)Low activity101 (12.5)Moderate activity292 (36.0)High activity59 (7.3)Adherence classification according to prescribed doses taken, n (%)Absent (20 (2.5%)Poor (25-49%)19 (2.3%)Regular (50-74%)55 (6.8%)Adequate (>75%)701 (86.5%)Conclusion:Adherence to treatment with methotrexate therapy has a direct impact on RA treatment outcome. The evaluation of adherence to the prescribed treatment should be addressed before modifications in DMRAD therapy in patients with Rheumatoid Arthritis. Before considering treatment failure in a patient treated with methotrexate, adherence to treatment must be evaluated. According to our results, self-reported adherence appears to be a cost and time effective method to care for patients.References:[1]Monchablon C, et. al. Assessment adherence to disease-modifying anti-rheumatic drugs in rheumatoid arthritis. Clinical Rheumatology. 2019;39(1):207-216.[2]Scott DL, et al. (2010) Rheumatoid arthritis. Lancet 376:1094–1108.Disclosure of Interests:None declared

Background:Tight control of rheumatoid arthritis (RA) is essential and we need a validated, objective and reproducible disease measure to achieve it. There is not gold standard in RA. There is a rising interest in evaluating disease activity on the patient’s point of view using patient-reported outcomes like RAPID3. Furthermore, some new biomarkers have appeared, like serum calprotectin, with promising results on its analyzing.Objectives:To evaluate correlation between disease activity measures usually used in patients with RA (DAS28 ESR/CRP, SDAI and CDAI) and other alternative tools (RAPID3 and calprotectin). To analyze correlation between RAPID3 and serum calprotectin levels.Methods:A cross-sectional study was performed. RA-patients (n=114) according to the ACR/EULAR 2010 classification criteria were consecutively enrolled from Rheumatology department at Hospital de Figueres during the period from February to June 2019. Disease activity, biomarkers and the assessment of the patient’s health status with RAPID3 data were collected. Modified RAPID3 (mRAPID3) was calculated by subtracting the questions about the mood included in the questionnaire (k,l,m questions) to the results of the RAPID3 as if it were a response of the HAQ test. Coefficient Spearman’s correlation (r) was used to assess the relationship between the variables, and coefficient of determination (r-squared=r2and adjusted r2) was used to show the strength of correlation.Results:114 patients were included: 71% women, mean (SD) age 60(11) years, median disease duration 13(8) years. 80% had positive RF and 70% positive ACCP antibodies. 52% had erosions. 89% patients had been receiving treatment with csDMARDs, 38% with bDMARDs o dsDMARS and 66% with glucocorticoids. Disease activity measures’ median values were DAS28ESR 3.07, DAS28CRP 2.76, SDAI 9.62 and CDAI 8.99 and showed low activity. The mean values of RAPID3 y mRAPID3 showed moderate activity (8.95 and 8.68 respectively). Median serum calprotectin level was 1.48μg/ml.All correlations between variables were statistically significant and directly proportional although with different values (table).Spearman’s correlation coefficient between mRAPID3, serum calprotectin, disease activity scores and laboratory parametersmRAPID3CalprotectinCRPESRDAS28ESRDAS28PCRSDAICDAImRAPID31Calprotectin0.231CRP0.330.591ESR0.230.390.541DAS28-ESR0.620.320.410.621DAS28-CRP0.690.320.420.280.831SDAI0.740.310.350.270.820.911CDAI0.730.250.240.210.800.890.991DAS28: Disease Activity Score, SDAI: Simplified Disease Activity Index, CDAI: Clinical Disease Activity Index, RAPID3m: Routine Assessment of Patient Index Data 3 modificative with k,l,m questions. CRP: C-reactive protein. ESR: erythrocyte sedimentation rateCoefficient of determination found a weak association between RAPID3 and mRAPID3 with DAS28ESR (r2=0,38) and moderate with DAS28CRP, SDAI and CDAI (r2=0,47, 0,55 y 0,53). Determination serum calprotectin levels together with RAPID3 or mRAPID3 increased strength of correlation between DAS28ESR, DAS28CRP, SDAI and CDAI with RAPID3 (adjusted r2=0,40, 0,49, 0,56, 0,52) and with mRAPID3 (adjusted r2=0,41, 0,50, 0,56, 0,53). Correlation between RAPID3 and serum calprotectin levels was very weak (r2=0,05).Conclusion:Correlation between disease activity measures and mRAPID3 was strong, but it was weak with serum calprotectin levels. Correlation strength between RAPID3 and DAS28ESR was low and it was moderate with other composite indices, it maintained with RAPID3m and improved by adding serum calprotectin levels although modestly. There was a very weak correlation between RAPID3 and serum calprotectin levels suggesting that these two variables give us different information about the disease activity.Acknowledgments:Mrs. Dolors Ragolta. Mr. Carlos Sanchez Piedra and Mr. Fernando Sanchez AlonsoDisclosure of Interests:None declared

Background Biologics have greatly improved the management of rheumatoid arthritis (RA), demonstrating efficacy and safety in alleviating symptoms, inhibiting bone erosion, and preventing loss of function. 1 The current study hypothesis was that despite significant improvement in patient (pt) outcomes since the introduction of biologics, significant unmet need and burden of illness remains. Objectives The main objective was to test our primary hypothesis. Secondary objectives were to explore patterns of physician use of disease activity measures and compare degree of reported unmet need as ascertained by subjective physician assessment and objective disease activity measures. Methods Descriptive analyses of the following outcomes were performed using ADELPHI RA Disease Specific Program (DSP) data for US and EU5 pts who had been receiving biologic therapy ≥3 months: remission based on DAS28 ESR ( Results After ≥3 months on biologics (n=1299), 56% of pts were in remission based on physician judgement, while 31% were in remission based on DAS28 ESR ( P Conclusions There are significant unmet needs among RA pts on biologic treatment, regardless of line of therapy, suggesting the need for biologics with novel mechanisms of action. Remission based on physician judgement appeared to be over-reported as compared to remission indicated by disease activity measure DAS28. Clinical disease activity measures were not used consistently by physicians. Future study is warranted to explore whether under-utilization of disease activity measures is associated with the discrepancy between physician subjective judgement and clinical measurement. References Smolen JS, et al. Ann Rheum Dis. 2014;73(3):492–509. Disclosure of Interest S. Peterson Shareholder of: Janssen, Employee of: Janssen, E. Sullivan Employee of: Adelphi Real World, D. Kielar Shareholder of: Janssen, Employee of: Janssen, N. Li Employee of: Janssen

Background Patient global assessment (PtGA) is included in all the current remission definitions for RA despite the fact that PtGA is not exclusively related to disease activity. Objectives In this study in order to objectively identify whether patient9s assessment is better than physician9s (Ph) or not, we compared disease activities assessed with ultrasound (US) of Boolean remission patients with PtGA and with PhGA. Methods RA patients in clinical remission (DAS28 ESR Results A total of 88 out of 389 RA patients (22.6%) in DAS28 remission were enrolled (F/M=55/33, mean age 53.4±12.0, disease duration 10.5±6.3 years, bDMARDs 44.3%, RF/Anti-CCP positivity 72%). Of 88 patients, 33 (37.5%) fulfilled the Boolean remission criteria. When PtGA was substituted with PhGA, Boolean remission rate increased to 61.4% (n=54) ( P grade 1 were taken into account, % of patients without PD, GS or both signals were also similar Table 1). US disease activity parameters of Boolean PtGA and Boolean PhGA remission and their counterpart nonremission patients were compared as well, Boolean remission either with PtGA or PhGA had significantly lower PD scores compared to Boolean nonremission groups with PtGA or PhGA (1 [0–4] vs 3 [1–7], P =0.016 and 1.5 [0–4] vs 4 [0.75–7], P =0.027, respectively). Conclusions RA patients in Boolean remission with PhGA do not have higher disease activity that is verified by US, compared to patients in Boolean remission with PtGA. Furthermore Boolean remission with PhGA differentiates patients with US evinced inflammation, especially PD scores, as good as PtGA. These data suggest that PtGA might be substituted with PhGA in Boolean criteria with higher remission achievement rates. Prospective follow up and recruitment of more patients will clarify the consistency of these results over time and impact of this substitution on functionality. Disclosure of Interest None declared

Background Disease activity in rheumatoid arthritis (RA) has traditionally been measured using the 28-joint count disease activity score (DAS28) using ESR. Use of DAS28 with CRP in place of ESR is increasing. Evidence suggests these scores are not truly interchangeable and agreement varies dependant on disease activity. This study uses the BSRBR-RA to investigate agreement between these scores, proposing an optimised method to calculate the estimated DAS28-ESR using CRP (mDAS28-CRP) that offers higher interscore agreement than currently. Objectives To investigate and optimise agreement between the DAS28-ESR and DAS28-CRP scores using the BSRBR-RA. Methods Patients with concurrent measures of ESR and CRP were identified from the BSRBR-RA, enabling paired calculation of DAS28-ESR and DAS28-CRP using existing formulae. A non-linear regression model enabled prediction of ESR from CRP. Resulting values were used in the existing DAS28-ESR formula to calculate estimated disease activity (mDAS28-CRP). A Bayesian approach was used to acknowledge the uncertainty associated with the predictions and correctly propagate it through to mDAS28-CRP. Inference, including constructing credible intervals for predicted ESR and mDAS28-CRP, was performed using Markov-Chain-Monte-Carlo. Bland-Altman analysis and agreement matrices were used to compare agreement levels between mDAS28-CRP, DAS28-CRP and DAS28-ESR. Results 5457 patients (mean age 56 yrs, 76% female) with 31,084 data entries were identified where paired DAS28-ESR/DAS28-CRP scores could be calculated. Mean DAS28-ESR was 0.3 points greater than DAS28-CRP (4.4 (SD 1.7), 4.1 (SD 1.6) respectively) compared with mean mDAS28-CRP of 4.6 (SD 1.6; Fig 1). mDAS28-CRP had superior agreement with DAS28-ESR across remission, low and moderate disease activity, with only a 6.8% reduction in agreement at high disease activity when compared with the DAS28-CRP (Table 1). Conclusions The mDAS28-CRP (online calculator pending) allows calculation of a disease activity score, using the same component parts of the DAS28-CRP, and has higher agreement levels with the original validated DAS28-ESR, than the current DAS28-CRP in this cohort. Disease activity scores are fundamental in clinical trials and practice, forming a central role in guiding availability of therapeutic agents. It is therefore essential that scores used are both valid and, if used interchangeably, have high agreement levels. Disclosure of Interest None declared

Background Disease activity in rheumatoid arthritis (RA) has traditionally been measured using the 28-joint count disease activity score (DAS28) using ESR. Use of DAS28 using C-reactive protein (CRP) in place of ESR is increasing. This study investigates the level of agreement between the DAS28-ESR and DAS28-CRP scores across different disease activity thresholds and identifies how patient characteristics may influence agreement. Objectives To identify the interscore agreement between the DAS28-ESR and DAS28-CRP scores and identify if gender or body mass index (BMI) influence the level of agreement. Methods Patients with concurrent measures of ESR and CRP were identified from the BSRBR-RA, enabling paired calculation of DAS28-ESR and DAS28-CRP. Paired scores were stratified by patients9 baseline BMI and gender. Agreement between the scores was compared using Bland-Altman statistics and agreement matrices. Results 5457 patients (mean age 56 yrs, 76% female) with 31,084 data entries were identified where paired DAS28-ESR/DAS28-CRP scores could be calculated. Mean DAS28-ESR was 0.3 points (95% CI -0.8 - 1.4) greater than DAS28-CRP (4.4 (SD 1.7) and 4.1 (SD 1.6) respectively). Men had a lower mean difference between the two scores compared with women (DAS28-ESR > DAS28-CRP by 0.2 points (95% CI -1.0 – 1.3) vs. 0.4 points (95% CI -0.7 – 1.4) respectively). The results stratified by BMI were similar to the overall mean difference. Agreement between the two scores according to disease activity thresholds are shown in Table 1. Conclusions Overall, the DAS28-ESR classifies fewer patients in remission (15.6% vs. 19.5%) giving a score, on average 0.3 points greater than the DAS28-CRP, with women having a greater difference between the two scores than men. When categorising scores by disease activity thresholds, the DAS28-ESR/DAS28-CRP have lowest agreement at LDA. 54.4% of DAS28-ESR scores were classified as MDA when the paired DAS28-CRP was LDA, which could influence results in clinical trial reporting. Conversely, 20% of patients were classified as being in MDA by DAS28-CRP when the paired DAS28-ESR demonstrated HDA. This is of importance given NICE biologics guidelines, and shows that up to 20% of patients may not satisfy the criteria for biologic therapy if DAS28-CRP were used instead of DAS28-ESR. These results highlight the impact of using the DAS28-ESR or DAS28-CRP interchangeably, and the importance of using a consistent version of the DAS28. Disclosure of Interest None declared

Background The ultimate goal of RA treatment is to achieve disease remission. Studies have shown that objective, measure-based treatment decision-making leads to improved patient outcomes. In addition, the National Quality Forum (NQF) recently endorsed three measures for RA, including assessment of disease activity using a standardized tool. However, in clinical practice physicians may still rely on subjective assessment of disease activity. Objectives To compare physician-reported and DAS28-based clinical assessments of RA disease remission. Methods Data were drawn from the Adelphi RA-DSP, a cross-sectional survey of United States rheumatologists and their RA patients between January and March 2014. Clinical remission status (DAS28 ESR Results Included in the analysis were 531 RA patients (75% female, mean age 56.4 years, 7.7 years since RA diagnosis, 53% treated with biologic) in the care of 78 rheumatologists (31% female, 37% practice only in office). 56% of physicians reported using only subjective criteria to assess remission; 30% of patients were evaluated using a standardized disease activity measure during last visit. While physicians reported 50% of patients were in remission, 32% were in remission by DAS28 criteria. Remission status was over-reported by physicians in 25% and underreported in 7% of patients. Regression analyses indicated that physician over-reporting was significantly higher among patients treated with biologics (Relative Risk Ratio (RRR)=2.01, P=0.003), those in the care of physicians with high RA workload (RRR=2.51, P=0.011), and patients considered by their physicians to have “satisfactory” RA control (RRR=6.09, P Physician under-reporting was significantly higher among obese patients (RRR=2.95, P=0.002), patients with average general health level (RRR=2.03, P=0.029), and those with more than 5 tender or swollen joints (RRR=2.72, P=0.019), but significantly lower among patients with medium to high current pain level (RRR=0.18, P=0.004) or those who saw physicians with high RA workload (RRR=0.43, P=0.029). Conclusions This study points out the need to increase the use of standardized measures of RA disease activity, and ensure consistency of use across clinical care. Addressing these gaps in care may help optimize treatment decisions, reduce variability in delivery of patient care and ultimately improve RA patient outcomes. Disclosure of Interest W. Wei Shareholder of: Sanofi, Employee of: Sanofi, C. Chen: None declared, E. Sullivan: None declared, S. Blackburn: None declared, J. Curtis Grant/research support from: Roche/Genentech, UCB, Janssen, CORRONA, Amgen, Pfizer, BMS, Crescendo, AbbVie, Consultant for: Roche/Genentech, UCB, Janssen, CORRONA, Amgen, Pfizer, BMS, Crescendo, AbbVie

Objectives To compare the disease activity score (DAS)28- ESR versus DAS28 -CRP, and to determine the factors that might influence their difference. Methods Patients with RA were included in a cross-sectional study. We have collected the demographic characteristics and the characteristics of the RA: duration of evolution, morning stiffness in minutes, functional impact of the disease assessed by the HAQ (Health Assessment Questionnaire), and current corticosteroid dose. The disease activity was assessed by the DAS28-ESR and DAS28-CRP. A simple correlation between DAS28-ESR and DAS28-CRP was performed. We considered a difference between DAS28-ESR and DAS28-CRP as significant when it is greater than or equal to 0.6 [1]. Factors influencing this difference were tested by univariate then multivariate logistic regression. Results 103 patients were included with a female predominance (87.4%). Mean age was 49.7±11.4 years. Median disease duration was 8 years [3-14]. There was a strong positive correlation between the two indexes of 0.91 (p Conclusions Our study shows a strong positive correlation between the DAS28-ESR and DAS28 -CRP. But DAS VS 28 would be higher than the DAS28 CRP in patients with high pain intensity and significant functional impairment. References Are the C-reactive protein values and erythrocyte sedimentation rate equivalent when estimating the 28-joint disease activity score in rheumatoid arthritis? Clin Exp Rheumatol. 2008 Sep-Oct;26(5):769-75. Disclosure of Interest None declared DOI 10.1136/annrheumdis-2014-eular.5807

Background The CAMERA II study (Computer Assisted Management in Early RA) demonstrated that the addition of prednisone versus placebo to a MTX-based tight-control strategy increased effectiveness of therapy and reduced need for biological treatment. Objectives The present study evaluated changes in biomarker levels over time with MTX+placebo and MTX+prednisone treatment, using the Multi-Biomarker Disease Activity (MBDA) blood test. Methods Clinical and biomarker assessments were performed at monthly visits to 1 year for 92 patients who had MBDA test results available at baseline and ≥ 1 subsequent visit. Average number of visits with non-missing disease activity measures was 3.7 per patient. Concentrations of 12 serum biomarkers were combined to produce a score between 1 and 100 using the MBDA algorithm, which generates a validated measure of disease activity. Biomarker levels were also assessed individually but only to 5 months, to avoid subsequent protocol-mandated exposures to non-MTX DMARDs for insufficient responders. Correlation between DAS28-ESR change and MBDA change was assessed using Spearman’s correlation. Changes from baseline and comparisons of change in disease activity scores over time for MTX+placebo versus MTX+prednisone were analyzed by t-tests. Biomarker concentrations were analyzed as fractions relative to baseline using Mann Whitney U tests. Results Changes from baseline to 1 year in DAS28-ESR and MBDA scores showed a significant correlation in the MTX+placebo arm (r = 0.57, p Conclusions Responses assessed with the biomarker-based MBDA test and DAS28-ESR were greater and more rapid for therapy with MTX+prednisone than MTX+placebo, even though individual biomarkers differed in their response profiles. The MBDA test may be useful in combination with clinical assessment to evaluate early response to therapy with MTX or MTX+prednisone. Disclosure of Interest M. Jurgens: None Declared, J. Bijlsma: None Declared, J. Jacobs: None Declared, M. Bakker: None Declared, F. Lafeber: None Declared, P. Welsing: None Declared, G. Cavet Consultant for: Crescendo Bioscience, Inc., D. Chernoff Employee of: Crescendo Bioscience, Inc., E. Sasso Employee of: Crescendo Bioscience, Inc., W. Li Employee of: Crescendo Bioscience, Inc., D. Haney Employee of: Crescendo Bioscience, Inc.

Background An essential element in the treat to target strategy is modification of treatment in the absence of achieving a target response by 12 weeks. A change in the Disease Activity Score (DAS28 ESR) of 1.2 and Simplified Disease Activity Index (SDAI) of 50% (SDAI50 based on a ACR20) are possible response measures (RM). Objectives To characterize patients (pts) failing to respond to either RM and determine a relationship between the two. Methods The study was carried out on the Canadian Early Arthritis Cohort (CATCH) - a multicentre early arthritis RA cohort comprising 1953 pts with disease of Results The cohort comprised 74.9% females, mean age: 52.6 years, mean disease duration: 5.7 months. Baseline (BSL) disease characteristics included a mean tender joint count: 9.0, swollen joint count: 8.2, DAS28: 5.2, HAQ: 1.1, SDAI: 29.7, ESR: 27.2, and CRP: 14.4. Of 419 pts evaluated, 198 pts failed to achieve a DAS28 1.2 and 206 pts failed to achieve a SDAI50 by 12 weeks. A strong relationship was observed between DAS28 1.2 and SDAI50 non-responders (Kappa 0.66) and Spearman Correlation (0.66) (p Conclusions SDAI50 and DAS28 1.2 RM are well correlated although at high disease states the likelihood of achieving an SDAI50 is lower than achieving a DAS28 1.2, while the reverse is true at lower disease states. Disclosure of Interest M. Omair: None Declared, P. Akhavan: None Declared, E. Keystone: None Declared, J. Xiong: None Declared, G. Boire: None Declared, J. Pope: None Declared, J. Thorne: None Declared, C. Hitchon: None Declared, B. Haraoui: None Declared, D. Tin: None Declared, D. Weber: None Declared, V. Bykerk Grant/research support from: The CATCH study was designed and implemented by the investigators and financially supported initially by Amgen Canada Inc. and Pfizer Canada Inc. via an unrestricted research grant since inception of CATCH. As of 2011, further support was provided by Hoffman-LaRoche Limited, United Chemicals of Belgium (UCB Canada Inc.)., Bristol-Meyers Squibb Canada Co., Abbvie Inc., and Janssen Biotech Inc. (a wholly owned subsidiary of Johnson & Johnson Inc.).