POS1179 SYSTEMATIC LITERATURE REVIEW ON THE SCREENING AND PROPHYLAXIS OF CHRONIC AND OPPORTUNISTIC INFECTIONS

Background

Opportunistic and chronic infections can arise in the context of treatment used for Autoimmune Rheumatic Diseases (ARDs). Although it is recognized that screening procedures and prophylactic measures must be followed, clinical practice is largely heterogeneous, with relevant recommendations not currently developed or disparately located across the literature.

Objectives

To conduct a systematic literature review (SLR) focusing on the screening and prophylaxis of opportunistic and chronic infections in ARDs. This is preparatory work done by members of the respective EULAR task force (TF).

Methods

Following the EULAR standardised operating procedures, we conducted an SLR with the following 5 search domains; 1) Infection: infectious agents identifed by a scoping review and expert opinion (TF members), 2) Rheumatic Diseases: all ARDs, 3) Immunosuppression: all immunosuppressives/immunomodulators used in rheumatology, 4) Screening: general and specific (e.g mantoux test) terms, 5) Prophylaxis: general and specific (e.g trimethoprim) terms. Articles were retrieved having the terms from domains 1 AND 2 AND 3, plus terms from domains 4 OR 5. Databases searched: Pubmed, Embase, Cochrane. Exclusion criteria: post-operative infections, pediatric ARDs, not ARDs (e.g septic arthritis), not concerning screening or prophylaxis, Covid-19 studies, articles concerning vaccinations and non-Εnglish literature. Quality of studies included was assessed as follows: Newcastle Ottawa scale for non-randomized controlled trials (RCTs), RoB-Cochrane tool for RCTs, AMSTAR2 for SLRs.

Results

5641 studies were initially retrieved (Figure 1). After title and abstract screening and removal of duplicates, 568 full-text articles were assessed for eligibility. Finally, 293 articles were included in the SLR. Most studies were of medium quality. Reasons for exclusion are shown in Figure 1. Results categorized as per type of microbe, are as follows: For Tuberculosis; evidence suggests that tuberculin skin test (TST) is affected by treatment with glucocorticoids and conventional synthetic DMARDs (csDMARDs) and its performance is inferior to interferon gamma release assay (IGRA). Agreement between TST and IGRA is moderate to low. Conversion of TST/IGRA occurs in about 10-15% of patients treated with biologic DMARDs (bDMARDs). Various prophylactic schemes have been used for latent TB, including isoniazide for 9 months, rifampicin for 4 months, isoniazide/rifampicin for 3-4 months. For hepatitis B (HBV): there is evidence that risk of reactivation is increased in patients positive for hepatitis B surface antigen. These patients should be referred for HBV treatment. Patients who are positive for anti-HBcore antibodies, are at low risk for reactivation when treated with glucocorticoids, cDMARDs and bDMARDs but should be monitored periodically with liver function tests and HBV-viral load. Patients treated with rituximab display higher risk for HBV reactivation especially when anti-HBs titers are low. Risk for reactivation in hepatitis C RNA positive patients, treated with bDMARDs is low. However, all patients should be referred for antiviral treatment and monitored periodically. For pneumocystis jirovecii: prophylaxis with trimethoprim/sulfamethoxazole (alternatively with atovaquone or pentamidine) should be considered in patients treated with prednisolone: 15-30mg/day for more than 4 weeks. Few data exist for screening and prophylaxis from viruses like EBV, CMV and Varicella Zoster Virus. Expert opinion supports the screening of rare bugs like histoplasma and trypanosoma in patients considered to be at high risk (e.g living in endemic areas).

Figure 1.

SLR flowchart

Conclusion

The risk of chronic and opportunistic infections should be considered in all patients prior to treatment with immunosuppressives/immunomodulators. Different screening and prophylaxis approaches are described in the literature, partly determined by individual patient and disease characteristics. Collaboration between different disciplines is important.

Acknowledgements

We would like to thank all members of the EULAR Task Force for the screening and prophylaxis of chronic and opportunistic infections in Autoimmune Rheumatic Diseases.

Disclosure of Interests

None declared

POS1229 THE IMPACT OF COVID-19 ON MEDICATION NON-ADHERENCE IN A RHEUMATOID AND PSORIATIC ARTHRITIS UK COHORT

Background

In March 2020, as part of the UK’s COVID-19 prevention strategy, those identified as ‘clinically extremely vulnerable’, were advised to shield. This included a number of patients prescribed anti-rheumatic drugs, who were asked to continue their current treatment unless they developed symptoms of infection. Suboptimal treatment adherence (16.0%-81.0%) has been reported in patients with arthritic diseases, and is associated with psychological factors, including anxiety (1). Previous literature in non-UK cohorts has highlighted suboptimal adherence levels in immunosuppressed patients during the pandemic, although many were single centre studies (2,3).

Objectives

The aim of this multi-centre study is to investigate the impact of the COVID-19 pandemic on adherence to anti-rheumatic medications in patients with established rheumatoid (RA) and psoriatic (PsA) arthritis in the UK who had recently commenced a biologic or targeted synthetic DMARD.

Methods

Between September 2020 and May 2021, RA and PsA patients prescribed biologic or targeted synthetic anti-rheumatic drugs from two multi-centre observational studies (BRAGGSS and OUTPASS) were sent a questionnaire on medication usage, adherence, and perceptions to establish the impact of COVID-19 on these parameters. Patients were asked about compliance during the COVID-19 pandemic using a 5-point Likert scale (always, often, sometimes, rarely, and never) and the reason for non-adherence. Adherence was defined as never missing or delaying a dose, unless medically advised. Descriptive summary statistics were calculated, and logistic regression and Pearson’s chi-squared tests were employed to investigate variables associated with self-reported non-adherence.

Results

In total 159 questionnaires were returned (81.1% RA and 18.9% PsA). Methotrexate (53.5%) was the most frequently prescribed agent, followed by etanercept (25.2%), sulfasalazine (22.6%), hydroxychloroquine (21.4%) and adalimumab (19.5%). Furthermore, 68.6% of patients were prescribed ≥2 drugs. During the pandemic, 42.1% of patients reported missing or delaying a treatment dose for any reason. Adherence information was available for 97.5% of patients with 25.8% reporting non-adherence which was not medically advised. Methotrexate non-adherence was 27.1%, with similar levels reported for etanercept (20.0%), sulfasalazine (27.8%), hydroxychloroquine (35.3%) and adalimumab (29.0%). No drugs had significantly different adherence compared to methotrexate. Furthermore, there was no association between disease type or perception of disease control and adherence. Of non-adherent patients, 17.5% reported increased anxiety, fear, and increased risk due to the COVID-19 pandemic as an influencing factor. Meanwhile, 37.5% of non-adherent patients listed non-COVID-19 intentional reasons and 45.0% reported non-intentional reasons, with forgetting and running out of treatment listed most frequently.

Conclusion

In a UK cohort self-reported non-adherence was reported in 25.8% of patients during the COVID-19 pandemic, despite medical advice, with reasons including increased anxiety due to COVID-19.

References

[1]Medication adherence and persistence in patients with rheumatoid arthritis, psoriasis, and psoriatic arthritis: a systematic literature review. Patient Prefer Adherence. 2018;12:1483–503.

[2]Vakirlis E, Bakirtzi K, Papadimitriou I, Vrani F, Sideris N, Lallas A, et al. Treatment adherence in psoriatic patients during COVID-19 pandemic: Real-world data from a tertiary hospital in Greece. J Eur Acad Dermatology Venereol. 2020;34(11):e673–5.

[3]Polat Ekinci A, Pehlivan G, Gökalp MO. Surveillance of psoriatic patients on biologic treatment during the COVID-19 pandemic: A single-center experience. Dermatol Ther. 2020;(December 2020):19–22.

Acknowledgements

on behalf of the BRAGGSS consortium

Disclosure of Interests

Philippa Curry: None declared, Hector Chinoy Speakers bureau: UCB, Biogen, Consultant of: Novartis, Eli Lilly, Orphazyme, Astra Zeneca, Grant/research support from: Eli Lilly, UCB, Meghna Jani: None declared, Darren Plant: None declared, Kimme Hyrich Consultant of: consultancy/honoraria from AbbVie, Grant/research support from: Pfizer, BMS, Ann Morgan Speakers bureau: Roche, Chugai, Consultant of: GSK, Roche, Chugai, AstraZeneka, Regeneron, Sanofi, Vifor, Grant/research support from: Roche, Kiniksa Pharmaceuticals, Anthony G Wilson: None declared, John Isaacs Speakers bureau: Abbvie, Gilead, Roche, UCB, Grant/research support from: GSK, Janssen, Pfizer, Andrew Morris: None declared, Anne Barton Grant/research support from: I have received grant funding from Pfizer, Galapagos, Scipher Medicine and Bristol Myers Squibb., James Bluett Grant/research support from: Pfizer Limited. JB has received travel/conference fees from UCB, Pfizer and Eli Lilly

POS0337 DISTINCT CLUSTERS OF JIA AT METHOTREXATE INITIATION IDENTIFIED USING TOPOLOGICAL DATA ANALYSIS

Background

Stratified medicine requires the identification of unique strata of a disease within which to base prognostic and treatment decisions. Juvenile idiopathic arthritis (JIA) offers a unique challenge in its inherent heterogeneity. The current ILAR classification, whilst useful for clinical categorisation, does not correlate with treatment outcomes. Therefore, further refinement, clustering and correlation of patient characteristics with treatment response are urgently required.

Objectives

To identify novel, phenotypically consistent subgroups of children and young people (CYP) with JIA at the point of starting methotrexate, across 19 patient and disease characteristics.

Methods

MTX-naïve CYP with JIA were selected if enrolled prior to April 2021 in one of four national JIA studies contributing to the UK CLUSTER consortium. Data from 19 harmonised study variables were extracted at point of starting MTX. Topological data analysis using a Gower similarity metric was used to identify clusters with distinct characteristics. Intervals and percent overlap between clusters were varied until an optimal model identified stable, potentially clinically plausible clusters. Significant differences in characteristics between identified clusters were tested using Kruskall-Wallis and Chi-Squared statistics.

Results

Of 2915 CYP included, the majority were female (68%), of white ethnicity (90%); with the most common ILAR categories being oligoarthritis (35%) and RF-negative JIA (34%).

The optimal TDA model identified six clusters which significantly differed across 16 of the 19 clinical variables at MTX initiation: Adolescents with low-moderate disease (Cluster 1, 41%), adolescents with predominantly sJIA and moderate-high disease (Cluster 2, 4%), children with predominantly sJIA and high disease (Cluster 3, <1%), children with oligo/RF-polyarthritis and low-moderate disease (Cluster 4, 43%) and two ANA-positive groups of largely females with moderate (Cluster 5, 11%) and high (Cluster 6, 1%) disease (Figure 1). Clustered groups also significantly differed in gender proportions (p<0.001), ethnicities (p<0.001), history of uveitis (p<0.001) and disease duration to both diagnosis (p<0.001) and MTX initiation (p<0.001), but did not differ in limited joint count (p=0.117), height (p=0.245) or BMI (p=0.394) z-scores.

Figure 1.

Clusters identified at MTX initiation in children and young people recruited to the UK BSPAR-ETN, BCRD, CAPS and CHARMS studies.

Conclusion

This study shows substantial heterogeneity in JIA at the point of MTX initiation, with six clusters identified across 19 demographic and clinical variables. ILAR categories across clusters were not always indicators of disease activity or symptom burden. Future analyses will correlate MTX treatment response within each cluster to understand what role these combined factors may have on initial treatment response.

Disclosure of Interests

Stephanie Shoop-Worrall: None declared, Kimme Hyrich Speakers bureau: AbbVie, Grant/research support from: BMS, UCB, and Pfizer, Lucy Wedderburn Grant/research support from: AbbVie and Sobi, Nophar Geifman: None declared

POS0665 DEVELOPMENT OF A BIOCHEMICAL TOFACITINIB ADHERENCE ASSAY IN RHEUMATOID ARTHRITIS: THE ORAL ADHERE STUDY

Background

Tofacitinib is a potent inhibitor of the JAK1/JAK3 tyrosine kinases effective in the treatment of rheumatoid arthritis (RA). Unlike biologic DMARDs, tofacitinib is administered orally. Oral administration offers a major benefit to patients, removing the risk of injection site reactions and previous research has shown that patients prefer an oral DMARD which may affect patient’s adherence (1).

Non-adherence is a health behaviour that results in reduced response and increased healthcare costs but can be challenging to accurately measure. Direct tofacitinib measurement may be an accurate measure of adherence that could, in the future, be used in a clinical setting as part of a behaviour change intervention.

Tofacitinib can be measured using High Performance Liquid Chromatography Selected Reaction Monitoring Mass Spectrometry (HPLC-SRM-MS). Previous tofacitinib studies have demonstrated an assay sensitivity of 0.1ng/ml may be sufficient for the detection of adherence following 5mg twice daily administration (2).

Objectives

The aim of this study is to develop a HPLC-SRM-MS assay to measure biochemical tofacitinib adherence in patients with RA.

Methods

Human serum for method development was obtained from volunteers recruited to the collection of blood and urine samples from volunteers for the development of analytical methods study (UREC 12346) and the National Repository Study (REC 99/8/84) following informed consent.

Samples were spiked with Tofacitinib/Tofacitinib-d3 and subjected to protein precipitation. LC-MS/MS analysis was performed on a TSQ Vantage triple quadrupole mass spectrometer coupled with an Accela UHPLC system (Thermo Fisher Scientific, USA). Validation of the assay was tested as adapted from European Medicines Agency guidelines on Bioanalytical Validation. Specifically, the lower limit of quantification (LLOQ), carryover, accuracy, linearity, precision, recovery and stability of the assay was determined.

To investigate the ability of the assay to detect adherence, serum samples (n=10) of patients prescribed tofacitinib from the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS) were analysed (REC reference: 04/Q1403/37). Participants self-reported date and time of tofacitinib ingestion prior to venepuncture. Samples were analysed in triplicate.

Results

The assay demonstrated a tofacitinib LLOQ of 0.1ng/ml, carryover of sample following injection of a 1000 ng/ml tofacitinib was <1%, linearity of r2=0.998, within run accuracy was between 81-85% at LLOQ and between 91-107% at all other levels. Between run accuracy was within 14.9% at LLOQ and within 0.2-5.1% of the nominal concentration at all other levels. Samples of tofacitinib spiked in whole blood and left at room temperature for seven days were within 0.98-10.25% of serum samples spiked on the day of analysis for all concentrations. To demonstrate the potential of the assay to determine adherence, all 10 BRAGGSS samples revealed tofacitinib levels above 0.1ng/ml with CV<15% (Table 1).

Table 1.Sample IDTime difference between self-reported tofacitinib ingestion and blood sample (hours)Mean Tofacitanib (ng/ml, n=3)CV (%)497.026.432.82402.8100.555.51703.673.232.27681.5150.378.09201.5137.469.09301.8117.8514.97331.490.891.18795.4217.4711.40172.8108.437.43372.5335.043.48

Conclusion

A novel tofacitinib LC-MS/MS assay has been developed. The ability of the assay to measure biochemical adherence has been explored. Further research to establish the sensitivity of the assay and the ability of the assay to detect non-adherence are required.

References

[1]Alten R, Krüger K, Rellecke J, Schiffner-Rohe J, Behmer O, Schiffhorst G, et al. Examining patient preferences in the treatment of rheumatoid arthritis using a discrete-choice approach. Patient Prefer Adherence. 2016;10:2217-28.

[2]Suzuki M, Tse S, Hirai M, Kurebayashi Y. Application of Physiologically-Based Pharmacokinetic Modeling for the Prediction of Tofacitinib Exposure in Japanese. Kobe J Med Sci. 2017;62(6):E150-E61.

Acknowledgements

Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited

Disclosure of Interests

Stephanie Church Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited, Kimme Hyrich Speakers bureau: Honoraria as a speaker received from Abbvie, Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited.

Research grant award from BMS, Kayode Ogungbenro Consultant of: Afferent, Biogen, Buzzard, Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited., Richard Unwin Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited, Anne Barton Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited. AB has received grant funding from Scipher Medicine Ltd, Bristol Myers Squibb and Galapagos in the past 12 months., James Bluett Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited. JB has received travel/conference fees from UCB, Pfizer and Eli Lilly

OP0266 TREATMENT DISCONTINUATION DUE TO ADVERSE EVENTS AS AN OVERALL MEASURE OF TOLERANCE AND SAFETY OF JAK-INHIBITORS: AN INTERNATIONAL COLLABORATION OF REGISTRIES OF RHEUMATOID ARTHRITIS PATIENTS (THE “JAK-pot” STUDY)

Background

The recently presented “ORAL Surveillance Study” has suggested increased risk of serious adverse events (AEs) with tofacitinib, a JAK-inhibitor (JAKi), compared to a comparator TNF-inhibitor (TNFi). Currently, there is limited real world evidence for the tolerability and safety of JAKi (1).

Objectives

To assess the safety of JAKi compared to other biologic agents in rheumatoid arthritis (RA) patients in a real-world population, by evaluating treatment discontinuation for AEs.

Methods

Pooled patient database from 16 national RA registries from across Europe, Québec (Canada), Turkey, and Israel were used. Treatment discontinuation due to AEs by treatment groups, JAKi versus (vs) TNFi and JAKi vs bDMARDs with other modes of action (OMA), were compared as an overall measure of tolerability and safety of JAKi. Standard descriptive statistics were used for baseline characteristics. We plotted unadjusted cumulative incidence, then the cause-specific Cox model was used to account for competing risks, and to obtain association between covariates and the instantaneous hazard rate for AEs. Variables listed in Table 1 were used for adjustment in the fully-adjusted cause-specific Cox model.

Table 1.

Baseline characteristics of the study population

JAKi1(BARI, FILGO,TOFA,UPA)OMA2(ABA, ANAK, SARI, TOCI)TNFi3(ADA, CERT, ETAN, GOL, INFL)n = 9208n = 16737n = 64533Treatment duration* (yrs)0.7 [0.2, 1.7]1.1 [0.4, 2.8]1.5 [0.5, 3.9]Age57.556.853.2Female (%)81.380.773.2Disease duration (yrs)9.913.110.7Seropositivity (%)78.775.969.8Previous b/tsDMARD (%) 034.030.859.7 120.925.924.3 216.621.710.4 3 or more28.521.55.6Concomitant GC (%)44.650.741.3Concomitant CsDMARD (%)  MTX22.622.028.8  MTX + other9.59.713.1  None50.552.543.5  Other17.415.914.7CRP13.2 (24.1)13.3 (25.6)12.3 (24.1)CDAI23.7 (13.8)22.9 (13.5)22.6 (14.0)DAS 284.7 (1.5)4.7 (1.6)4.6 (1.6)HAQ1.2 (0.7)1.2 (0.7)1.1 (0.7)BMI27.1 (5.9)26.8 (5.8)26.8 (5.8)Patients with at least one Comorbidity (%)51.753.949.6

csDMARDs = classical synthetic DMARDs, MTX = methotrexate, GC = glucocorticoids, CRP = C-reactive protein, CDAI = Clinical Disease Activity Index, DAS 28 = Disease Activity Score 28, HAQ = Health Assessment Questionnaire, BMI = Body Mass Index, *Treatment duration (median [IQR]) = Last visit date – start date (if treatment is ongoing), treatment stop date – treatment start date (if treatment has stopped)

. 1BARI (baricitinib; 44.41 %), FILGO (filgotinib; 0.23%), TOFA (tofacitinib; 49.59%), UPA (upadacitinib; 5.78%); 2ABA (abatacept; 39.96%), ANAK (anakinra; 2.64%), SARI (sarilumab; 3.14%), TOCI (tocilizumab; 52.55%); 3ADA (adalimumab; 31.00%), CERT (certolizumab; 8.33%), ETAN (etanercept; 38.83%), GOLI (golimumab; 9.36%), INFL (infliximab; 12.56%)

Results

90,478 treatment courses were included in the analysis (Table 1). We observed similar crude incidence rate of treatment discontinuation due to AEs between JAKi and TNFi, but less in JAKi vs OMA (Figure 1). The fully adjusted hazard rate of treatment stop for AEs was also similar in JAKi vs TNFi (HR = 1.02 (95% CI 0.92 – 1.13)), and in JAKi vs OMA (HR= 1.08 (95% CI 0.97 – 1.20)). The rate of treatment stop for AEs was higher in women (HR = 1.29 (95% CI 1.21 – 1.37)) and with an increasing number of previous b/tsDMARDs (HR = 1.50; 1.48; 1.68 for 1, 2, and 3 or more previous b/ts DMARDs, respectively).

Figure 1.

Comparison of cumulative incidence of treatment discontinuation for adverse events in JAKi, TNFi, and OMA group

Conclusion

After adjusting for potential confounders, the rate of treatment discontinuation for AEs was comparable between JAKi and OMA or TNFi. Treatment discontinuation for AEs comprises a wide range of AEs; future analyses will be performed to investigate specific AEs, such as cancer, serious infections or major adverse cardiovascular events.

References

[1]Ann Rheum Dis 2022. doi: 10.1136/annrheumdis-2021-221915.

Disclosure of Interests

Eric Nham: None declared, Romain Aymon: None declared, Denis Mongin: None declared, Sytske Anne Bergstra: None declared, Denis Choquette Speakers bureau: DC reports speaker or consultant fees from Abbvie, Amgen, Eli Lilly, Fresenius-Kabi,Pfizer, Novartis, Sandoz, Tevapharm, Consultant of: Stated above, Catalin Codreanu Speakers bureau: CC reports speaker/consulting fees from AbbVie, Amgen, Astra Zeneca, Boehringer Ingelheim, Ewopharma, Lilly, Novartis, Pfizer, Richter, Consultant of: Stated above, Ori Elkayam Consultant of: OE has received consultant and honorary fees from Pfizer, Lilly, Abbvie, Novartis, Jansen, BI, Kimme Hyrich Speakers bureau: KLH has received speaker honoraria from Abbvie, Grant/research support from: KLH has received grant income from Pfizer and BMS, Florenzo Iannone Speakers bureau: FI has received consulting/speaker’s fees from Abbvie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Pfizer, SOBI, Roche and UCB, Consultant of: Stated above, Nevsun Inanc Speakers bureau: NI has received consultant and speaker/honoraria from Abbvie, Lilly, MSD, Novartis, Pfizer, Roche, Amgen, Celltrion,UCB., Consultant of: Stated above, Lianne Kearsley-Fleet: None declared, Eirik kristianslund: None declared, Tore K. Kvien Speakers bureau: TKK has received fees for speaking and/or consulting from several companies among them Pfizer, AbbVie, Lilly and Galapagos/Gilead, Consultant of: Stated above, Burkhard Leeb Speakers bureau: BFL has received speaker honoraria from Sandoz, Abbvie, Eli-Lilly, Pfizer, Roche, Grünenthal, Biogen, Celgene, Galina Lukina Speakers bureau: GVL has received speaker fees from Abbvie, Lilly, Novartis, MSD, Roche, Pfizer, Dan Nordström Consultant of: DCN has acted as consultant for AbbVie, BMS, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Karel Pavelka Speakers bureau: KP has received honoraria for lectures: MSD, Pfizer, Roche, Eli Lilly, Medac, UCB, SOBI, Biogen, Sandoz, Viatris, Manuel Pombo-Suarez Speakers bureau: MPS reports advisor and speaker honoraria from Janssen, Lilly, MSD, Novartis, Sanofi, Consultant of: Stated above, Ziga Rotar Speakers bureau: ZR has received fees for speaking/consulting from several companies among them Pfizer, AbbVie, and Eli Lilly, Consultant of: Stated above, Maria Jose Santos Speakers bureau: MJS has received speaker fees from Abbvie, AstraZeneca, Lilly, Novartis and Pfizer, Delphine Courvoisier: None declared, Kim Lauper Speakers bureau: KL reports speakers fees for Pfizer, Viatris and Celltrion, Consultant of: KL reports consulting fees for Pfizer, Axel Finckh Speakers bureau: AF reports honoraria and consultancies from Pfizer, BMS, MSD, Eli-Lilly, AbbVie, Galapagos, Mylan, UCB, Viatris, Consultant of: Stated above, Grant/research support from: AF reports grants from Pfizer INC, AbbVie, Galapagos, Eli Lilly

POS0033 GENETIC INVESTIGATION OF TUMOUR NECROSIS FACTOR INHIBITOR IMMUNOGENICITY IN PATIENTS WITH RHEUMATOID ARTHRITIS

Background

Rheumatoid arthritis (RA) is a chronic inflammatory disease that primarily affects the synovial joints. Tumour Necrosis Factor inhibitor (TNFi) therapy has transformed the clinical management of RA. However, monoclonal antibody derived TNFi is associated with development of immunogenicity and subsequent loss of therapeutic effects. Previous studies have observed associations between certain HLA alleles and TNFi immunogenicity. For example HLA-DQA1 and HLA-DRB1 have been associated with immunogenicity in inflammatory bowel disease 1,2 and RA 3,4, respectively.

Objectives

The aims of this study were to identify associations between HLA alleles and immunogenicity to TNFi in an observational cohort of RA patients and to replicate findings from previous studies.

Methods

Anti-drug antibody titres were measured using radioimmunoassay in serum samples from RA patients participating in Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS). An anti-drug antibody titre of ≥12 AU/mL following six months on treatment was used to define positive immunogenicity. Genotype data were generated using Illumina HumanCoreExome Arrays. Standard quality control (QC) was applied prior to HLA imputation using SNP2HLA software before low minor allele frequency markers were removed. Logistic regression was used to study the association between HLA alleles and immunogenicity, whilst the omnibus test was applied to amino acid positions; sex and concurrent conventional synthetic DMARD use were included as a covariate in all the models.

Results

In total, 445 RA patients were analysed, 377 patients (70 immunogenicity events) were underdoing adalimumab therapy and 68 certolizumab (30 immunogenicity events) therapy. Following QC, 162 HLA alleles and 361 amino acids positions were available for analysis. The strongest HLA allele association was observed for HLA-DQA1*03 when all patients were analysed (OR = 0.61; 95% CI = 0.43 – 0.86; p-value = 5e-3). The amino acids positions 187 (p-value = 5e-3) and 26 (p-value = 5e-3) within the HLA-DQA1 gene were significantly associated with immunogenicity events. When both drugs were analysed separately, they produced similar effect size for HLA-DQA1*03 association; patients treated with adalimumab (OR = 0.59; 95% CI = 0.38 – 0.88; p-value = 1e-2) and certolizumab (OR = 0.52; 95% CI = 0.24 – 1.1; p-value = 1e-1). Another strong association was found in HLA-DRB1*04 (OR = 0.62; 95% CI = 0.44 – 0.88; p-value = =7e-3) and the amino acid position of 180 (p-value = 7e-3) and 33 (p-value = 7e-3) of HLA-DRB1 gene. Additionally, the similar protective effect between the two presented alleles suggested possibility of linkage disequilibrium, upon investigation the r2 between the 2 alleles is 0.69.

Conclusion

The current study increases the evidence for association between immunogenicity development with HLA-DQA1 and HLA-DRB1 alleles in patients receiving monoclonal antibody derived TNFi therapy. Further well powered studies are now required to determine the utility of HLA markers as a potential tool to aid the clinical management of RA.

References

[1]Sazonovs, A. et al. HLA-DQA1*05 Carriage Associated With Development of Anti-Drug Antibodies to Infliximab and Adalimumab in Patients With Crohn’s Disease. Gastroenterology158, 189–199 (2020).

[2]Billiet, T. et al. Immunogenicity to infliximab is associated with HLA-DRB1. Gut64, 1344–1345 (2015).

[3]Liu, M. et al. Identification of HLA-DRB1 association to adalimumab immunogenicity. PLoS One13, e0195325 (2018).

[4]Rigby, W. et al. HLA-DRB1 risk alleles for RA are associated with differential clinical responsiveness to abatacept and adalimumab: data from a head-to-head, randomized, single-blind study in autoantibody-positive early RA. Arthritis Res. Ther.23, 245 (2021).

Disclosure of Interests

Chuan Fu Yap: None declared, Nisha Nair: None declared, Kimme Hyrich Speakers bureau: Abbvie, Grant/research support from: Pfizer and BMS, Anthony G Wilson: None declared, John Isaacs Speakers bureau: Abbvie, Gilead, Roche, UCB, Grant/research support from: GSK, Janssen, Pfizer, Ann Morgan Speakers bureau: Roche, Chugai, Consultant of: GSK, Roche, Chugai, AstraZeneca, Regeneron, Sanofi, Vifor, Grant/research support from: Roche, Kiniksa Pharmaceuticals, Anne Barton Grant/research support from: I have received grant funding from Pfizer, Galapagos, Scipher Medicine and Bristol Myers Squibb., Darren Plant: None declared

AB0011 DNA METHYLATION AS A BIOMARKER OF TOCILIZUMAB RESPONSE IN RHEUMATOID ARTHRITIS PATIENTS

Background

Tocilizumab (TCZ) is a disease-modifying antirheumatic biologic drug, which targets the IL-6 signalling pathway and is effective in ameliorating disease activity in rheumatoid arthritis (RA). However, approximately 50% of patients do not respond adequately to TCZ and some patients report adverse events. Considering there is growing evidence that DNA methylation is implicated in RA susceptibility and response to some biologics (1, 2), we investigated DNA methylation as a candidate biomarker for response to TCZ in RA.

Objectives

To identify differential DNA methylation signatures in whole blood associated with TCZ response in patients with RA.

Methods

Epigenome-wide DNA methylation patterns were measured using the Infinium EPIC 850k BeadChip (Illumina) in whole blood-derived DNA samples from patients with RA. DNA was extracted from blood samples taken pre-treatment and following 3 months on therapy, and response was determined at 6 months using the Clinical Disease Activity Index (CDAI). Patients who had good response (n=10) or poor response (n=10) to TCZ by 6 months were selected. Samples from secondary poor responders (n=10) (patients who had an improvement of CDAI and were in remission at 3 months, followed by a worsening of CDAI at 6 months) were also analysed. Differentially methylated positions (DMPs) were identified using linear regression, adjusting for gender, age, cell composition, smoking status, and glucocorticoid use.

Results

In the pre-treatment samples, 20 DMPs were significantly associated with response status at 6 months (unadjusted p-value <10-6), whilst in the 3 month samples, 21 DMPs were associated with response. One DMP, cg03121467, was significantly less methylated in good responders compared to poor responders in the pre-treatment samples. This DMP is close to EPB41L4A and may play a role in β–catenin signalling. Interestingly, cg10136146 was significantly less methylated in secondary poor responders compared to both good and poor responders in the 3 month samples. This DMP maps close to CD81, which plays a role in mediating the development and activation of B and T lymphocytes.

Conclusion

These preliminary results provide evidence that DNA methylation patterns may predict response to TCZ. Further regional and pathway analyses is in progress and validation of these findings in other larger data sets is required.

References

[1]Liu,Y., Aryee,M.J., Padyukov,L., Fallin,M.D., Hesselberg,E., Runarsson,A., Reinius,L., Acevedo,N., Taub,M., Ronninger,M., et al. (2013) Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat. Biotechnol., 31, 142–147.

[2]Plant,D., Webster,A., Nair,N., Oliver,J., Smith,S.L., Eyre,S., Hyrich,K.L., Wilson,A.G., Morgan,A.W., Isaacs,J.D., et al. (2016) Differential Methylation as a Biomarker of Response to Etanercept in Patients With Rheumatoid Arthritis. Arthritis Rheumatol. (Hoboken, N.J.), 68, 1353–60.

Disclosure of Interests

Nisha Nair: None declared, Darren Plant: None declared, John Isaacs Speakers bureau: Abbvie, Gilead, Roche, UCB, Grant/research support from: GSK, Janssen, Pfizer, Ann Morgan Speakers bureau: Roche/Chugai, Consultant of: GSK, Roche, Chugai, AstraZeneca, Regeneron, Sanofi, Vifor, Grant/research support from: Roche, Kiniksa Pharmaceuticals, Kimme Hyrich Consultant of: AbbVie, Grant/research support from: Pfizer, BMS, Anne Barton Grant/research support from: I have received grant funding from Pfizer, Galapagos, Scipher Medicine and Bristol Myers Squibb., Anthony G Wilson: None declared

POS1212 SARS-CoV-2 VACCINE SAFETY IN ADOLESCENTS WITH INFLAMMATORY RHEUMATIC AND MUSCULOSKELETAL DISEASES AND ADULTS WITH JUVENILE IDIOPATHIC ARTHRITIS

Background

There is a lack of data on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) vaccination safety in children and young people (CYP) with rheumatic and musculoskeletal diseases (RMDs). Current vaccination guidance is based on data from adults with RMDs or CYP without RMDs.

Objectives

To describe the characteristics and outcomes of adolescents with inflammatory RMDs and adults with juvenile idiopathic arthritis (JIA) vaccinated against SARS-CoV-2.

Methods

We described patient characteristics, flares, and adverse events in adolescent cases under 18 with inflammatory RMDs and adult cases aged 18 or above with JIA submitted to the European Alliance of Associations for Rheumatology (EULAR) COVAX registry.

Results

Thirty-six adolescent cases were reported from 4 countries, the most frequent diagnosis was JIA (42%). Over half (56%) reported early reactogenic-like adverse events (AEs) experienced within 7 days of vaccination. One mild polyarthralgia flare and one serious AE (malaise) were reported. No CYP reported SARS-CoV-2 infection post-vaccination.

In addition to the adolescent cases, eleven countries reported 74 adult JIA cases. Among these, 62% reported early reactogenic-like AEs and two flares were reported (mild polyarthralgia and moderate uveitis). No serious AEs of special interest were reported among adults with JIA. Three 20-30 year old females were diagnosed with SARS-CoV-2 post-vaccination; all fully recovered.

Conclusion

In this observational registry dataset, SARS-CoV-2 vaccines appeared safe in adolescents with RMDs and adults with JIA, with a low frequency of disease flares, serious AEs, and SARS-CoV-2 re-infection seen in both populations.

Table 1.

Characteristics of adolescents with RMDs and adults with JIA reported to the EULAR COVAX registry

Adolescents with RMDs (N=36)Adults with JIA (N=74)SexFemale21 (58)54 (73)Male15 (42)20 (27)Age (median [IQR])15 [14.5, 17]26 [23, 31]Primary RMD diagnosisNon-systemic JIA10 (28)63 (85)Systemic JIA5 (14)11 (15)Systemic lupus erythematosus5 (14)Spondyloarthritis/psoriatic arthritis5 (14)Vasculitis/other RMD #11 (30)RMD disease activityRemission23 (64)33 (45)Minimal8 (22)21 (28)Moderate2 (6)12 (16)Severe1 (3)1 (1)Not applicable/missing2 (6)7 (10)RMD medicationNone9 (25)3 (4)b-DMARD9 (25)50 (68)cs-DMARD21 (58)25 (34)ts-DMARD5 (14)2 (3)Systemic glucocorticoids5 (14)1 (1)Colchicine7 (10)Other immunosuppressant *COVAX typePfizer/BioNTech33 (92)50 (68)Moderna2 (6)10 (14)AstraZeneca/Oxford1 (3)10 (14)Janssen1 (1)CoronaVac2 (3)UNK1 (1)COVAX doses111 (31)8 (11)22 (24)61 (82)31 (3)5 (7)RMD flareYes1 (3)2 (3)AEYes20 (56)46 (62)Early AEInjection site pain8 (22)16 (22)Redness6 (17)2 (3)Muscle pain1 (3)9 (12)Joint pain4 (11)3 (4)Headache9 (25)10 (14)Fever1 (3)26 (35)Chills2 (6)5 (7)Fatigue1 (3)13 (18)VomitingAE of special interestNon-serious1 (3)1 (1)Serious – important medical event1 (3)

All data are N(%) of the column unless stated otherwise.

# Other RMD includes Sjogren’s syndrome, systemic sclerosis, undifferentiated connective tissue disease, non-monogenic auto-inflammatory syndrome, chronic recurrent multifocal osteomyelitis, and other inflammatory arthritis

* Other immunosuppressant includes ciclosporin, mycophenolate mofetil/mycophenolic acid.

RMD, rheumatic and musculoskeletal disease; JIA, juvenile idiopathic arthritis; EULAR, European Alliance of Associations for Rheumatology; ANCA-associated vasculitis, anti-neutrophil cytoplasmic antibody-associated vasculitis; cs-, conventional synthetic; b-, biological; ts-, targeted synthetic; DMARD, disease-modifying anti-rheumatic drug; COVAX, Coronavirus vaccine; AE, adverse event.

Acknowledgements

We wish to thank all healthcare providers who entered data into the registry.

Disclosure of Interests

Saskia Lawson-Tovey: None declared, Anja Strangfeld Speakers bureau: AbbVie, MSD, Roche, BMS, Pfizer, Elsa Mateus: None declared, Laure Gossec Consultant of: AbbVie, Amgen, BMS, Galapagos, Gilead, GSK, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis, UCB, Grant/research support from: Amgen, Galapagos, Lilly, Pfizer, Sandoz, Loreto Carmona: None declared, Pedro Machado Speakers bureau: AbbVie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche, UCB, Consultant of: AbbVie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche, UCB, BERND RAFFEINER: None declared, Inita Bulina Speakers bureau: AbbVie, Pfizer, Janssen, Boehringer Ingelheim, Daniel Clemente Speakers bureau: Novartis, GSK, Julija Zepa Speakers bureau: AbbVie, Novartis, Janssen/Johnson & Johnson, Ana Maria Rodrigues Speakers bureau: Amgen, AbbVie, Grant/research support from: Amgen, Pfizer, AstraZeneca, Xavier Mariette Consultant of: BMS, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sanofi-Aventis, UCB, Grant/research support from: Ose, Kimme Hyrich Speakers bureau: AbbVie, Grant/research support from: Pfizer, BMS, UCB

AB0345 THERAPEUTIC DRUG LEVELS TO ACHIEVE GOOD EULAR RESPONSE IN PATIENTS WITH RHEUMATOID ARTHRITIS RECEIVING ADALIMUMAB: RESULTS FROM THE BIOLOGICS IN RHEUMATOID ARTHRITIS GENETICS AND GENOMICS STUDY SYNDICATE (BRAGGSS) COHORT

Background

Rheumatoid arthritis (RA) is a systemic inflammatory disease often treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs) such as Adalimumab (ADL), a tumour-necrosis factor inhibitor (TNFi). However, it is known that about a third of patients do not respond to ADL treatment. Previous studies have reported associations between poor response, decreased serum drug levels (SDLs) and poor adherence, but a therapeutic SDL has not been defined nor applied in clinical practice.

Objectives

To assess median ADL SDLs in RA European Alliance of Associations for Rheumatology (EULAR) good vs non/moderate responders, and to determine cut-off SDLs associated with a “Good” response in fully adherent RA patients.

Methods

In a prospective observational cohort study, patients with RA were treated with ADL. At baseline, 3-, 6-, and 12-months patients had 4-component DAS28 scores, self-reported treatment adherence data and SDLs measured. Median drug levels and receiver-operator characteristics (ROC) curves were used to compare SDLs between responders and non-responders, and to establish cut-off SDLs in self-reported fully adherent patients. Serum drug levels were measured using a sandwich ELISA produced by Progenika Biopharma. Patients were considered fully adherent if they self-reported never having altered, forgotten or omitted any dose of their biologic drug at follow-up. Between group comparisons were assessed using Fisher’s exact test, with a threshold for significance set at p<0.05. Statistical analyses were performed in R Version 4.1.0 and RStudio Version 1.4.1106.

Results

A total of 283 RA patients taking ADL were included in the analysis. Baseline characteristics are shown in Table 1. Of these patients 93 (32.9%) self-reported being fully adherent to treatment at 3 months follow-up and had SDLs measured.

Table 1.

Baseline characteristics of patient cohort with RA taking ADL (n=283)

CharacteristicnMissing (%)Age at baseline, median years (IQR)58 (51, 64)0Disease duration, median years (IQR)7 (3, 16)0Female Sex, n (%)206 (73)0BMI, median (IQR)27.4 (23.7, 31.9)0Smoking Status132 (46)Current, n (%)57 (38)-Ex, n (%)32 (21)-Non, n (%)62 (41)-On concurrent DMARD(s)1 (0.4)No, n (%)34 (12)-Yes, n (%)248 (88)-Baseline DAS Score, median (IQR)5.61 (5.18, 6.14)On MTX at baseline38 (13)No, n (%)44 (18)Yes, n (%)201 (82)

In 93 fully adherent RA patients taking ADL at 3 months, good EULAR responders had significantly higher SDLs compared to non/moderate EULAR responders (p=0.0234). In 47/93 (50.5%) fully adherent good responders median SDL at 3 months was 10.94mg/L (IQR 7.75 to 12.0), whereas in 46/93 (49.5%) non/moderate responders, median SDL at 3 months was 9.014 (IQR 6.96 to 11.1).

ROC analysis (see Figure 1) reported a 3-month non-trough ADL SDL cut-off of 7.5mg/L in fully adherent RA patients which discriminated Good EULAR responders compared to non/moderate responders with an AUC of 0.63 (95% CI 0.52 – 0.75), 39.1% specificity, and 80.9% sensitivity.

Figure 1.

ROC curve analysis: EULAR non/moderate vs good responders with 3 month ADL SDLs.

Conclusion

In keeping with previous work, SDLs were higher in adherent compared with non-adherent patients, but this is the first study to demonstrate that SDLs are higher in fully adherent good EULAR responders compared with non/moderate responders. Based on our methods, cut-offs of 7.5mg/L for ADL may be useful targets in clinical practice to achieve good EULAR response.

References

[1]Jani M, Chinoy H, Warren RB, Griffiths CEM, Plant D, Fu B, et al. Clinical Utility of Random Anti–Tumor Necrosis Factor Drug–Level Testing and Measurement of Antidrug Antibodies on the Long-Term Treatment Response in Rheumatoid Arthritis. Arthritis & Rheumatology. 2015;67(8):2011-9.

[2]Pouw MF, Krieckaert CL, Nurmohamed MT, van der Kleij D, Aarden L, Rispens T, et al. Key findings towards optimising adalimumab treatment: the concentration-effect curve. Ann Rheum Dis. 2015;74(3):513-8.

Disclosure of Interests

None declared

OP0252 FACTORS ASSOCIATED WITH SEVERE COVID-19 OUTCOMES IN PATIENTS WITH IDIOPATHIC INFLAMMATORY MYOPATHY: RESULTS FROM THE COVID-19 GLOBAL RHEUMATOLOGY ALLIANCE PHYSICIAN-REPORTED REGISTRY

Background

There is a paucity of data in the literature about the outcome of patients with idiopathic inflammatory myopathy (IIM) who have been infected with SARS-CoV-2.

Objectives

To investigate factors associated with severe COVID-19 outcomes in patients with IIM.

Methods

Data on demographics, number of comorbidities, region, COVID-19 time period, physician-reported disease activity, anti-rheumatic medication exposure at the clinical onset of COVID-19, and COVID-19 outcomes of IIM patients were obtained from the voluntary COVID-19 Global Rheumatology Alliance physician-reported registry of adults with rheumatic disease (from 17 March 2020 to 27 August 2021). An ordinal COVID-19 severity scale was used as primary outcome of interest, with each outcome category being mutually exclusive from the other:a) no hospitalization, b) hospitalization (and no death), or c) death. Odds ratios (OR) were estimated using multivariable ordinal logistic regression. In ordinal logistic regression, the effect size of a categorical predictor can be interpreted as the odds of being one level higher on the ordinal COVID-19 severity scale than the reference category.

Results

Complete hospitalization and death outcome data was available in 348 IIM cases. Mean age was 53 years, and 223 (64.1%) were female. Overall, 167/348 (48.0%) people were not hospitalized, 136/348 (39.1%) were hospitalized (and did not die), and 45/348 (12.9%) died. Older age (OR=1.59 per decade of life, 95%CI 1.32-1.93), male sex (OR=1.63, 95%CI 1.004-2.64; versus female), high disease activity (OR=4.05, 95%CI 1.29-12.76; versus remission), presence of two or more comorbidities (OR=2.39, 95%CI 1.22-4.68; versus none), prednisolone-equivalent dose >7.5 mg/day (OR=2.37, 95%CI 1.27-4.44; versus no glucocorticoid intake), and exposure to rituximab (OR=2.60, 95%CI 1.23-5.47; versus csDMARDs only) were associated with worse COVID-19 outcomes (Table 1).

Table 1.

Multivariable logistic regression analysis of factors associated with the ordinal COVID-19 severity outcomes. AZA, azathioprine; CI, confidence interval; combo, combination; CSA, ciclosporin; CYC, cyclophosphamide; DMARD, disease-modifying anti-rheumatic drug; b/tsDMARD, biologic/targeted synthetic DMARD, csDMARD, conventional synthetic DMARD; HCQ, hydroxychloroquine; IVIg, intravenous immunoglobulin; LEF, leflunomide; MMF, mycophenolate mofetil; mono, monotherapy; MTX, methotrexate; OR, odds ratio; Ref, reference; RTX, rituximab; SSZ, sulfasalazine; TAC, tacrolimus.

VariableOR (95%CI)P-valueVariableOR (95%CI)P-valueAge (per decade)1.59 (1.32-1.93)<0.001ComorbiditiesMale sex1.63 (1.004-2.64)0.048NoneRefNAPrednisolone-equivalent doseOne1.46 (0.79-2.72)0.228NoneRefNATwo or more2.39 (1.22-4.68)0.011>0 to 7.5mg/day1.10 (0.57-2.11)0.779Physician-reported disease activity>7.5mg/day2.37 (1.27-4.44)0.007RemissionRefNAIVIg0.41 (0.15-1.16)0.093Low/moderate1.23 (0.67-2.28)0.504DMARDsHigh4.05 (1.29-12.76)0.018csDMARD only (mono or combi - HCQ, MTX, LEF, SSZ)RefNARegionNo DMARD1.84 (0.90-3.75)0.094EuropeRefNAb/tsDMARD mono or combi (except RTX)1.60 (0.49-5.26)0.435North America0.89 (0.49-1.61)0.694CSA/CYC/TAC mono or combi (except RTX or b/tsDMARDs)1.55 (0.52-4.58)0.429Other4.25 (2.21-8.16)<0.001AZA mono1.70 (0.69-4.19)0.249Time periodMMF mono1.22 (0.53-2.82)0.634Before 15 June 2020RefNAAZA/MMF combi (except RTX or b/tsDMARDs)0.71 (0.25-2.00)0.51716 June - 30 September 20200.58 (0.26-1.27)0.171RTX mono or combi2.60 (1.23-5.47)0.012After 1 October 20200.58 (0.35-0.95)0.032

Conclusion

These are the first global registry data on the impact of COVID-19 on IIM patients. Older age, male gender, higher comorbidity burden, higher disease activity, higher glucocorticoid intake and rituximab exposure were associated with worse outcomes. These findings will inform risk stratification and management decisions for IIM patients.

References

None

Disclosure of Interests

Su-Ann Yeoh: None declared, Milena Gianfrancesco: None declared, Saskia Lawson-Tovey: None declared, Kimme Hyrich Speakers bureau: AbbVie unrelated to this work, Grant/research support from: Pfizer, BMS, both unrelated to this work, Anja Strangfeld Speakers bureau: AbbVie, Celltrion, MSD, Janssen, Lilly, Roche, BMS, Pfizer, all unrelated to this work, Laure Gossec Consultant of: AbbVie, Amgen, BMS, Galapagos, Gilead, GSK, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis, UCB, all unrelated to this work, Grant/research support from: Amgen, Galapagos, Lilly, Pfizer, Sandoz, all unrelated to this work, Loreto Carmona: None declared, Elsa Mateus Consultant of: Boehringer Ingelheim Portugal, not related to this work, Martin Schaefer: None declared, Christophe Richez Speakers bureau: Abbvie, Amgen, Astra Zeneca, Biogen, BMS, Celltrion, Eli Lilly, Galapagos, GSK, MSD, Novartis, and Pfizer, all unrelated to this abstract, Consultant of: Abbvie, Amgen, Astra Zeneca, Biogen, BMS, Celltrion, Eli Lilly, Galapagos, GSK, MSD, Novartis, and Pfizer, all unrelated to this abstract, Eric Hachulla Speakers bureau: Johnson & Johnson, GlaxoSmithKline, Roche-Chugai, all unrelated to this work, Consultant of: Bayer, Boehringer Ingelheim, GlaxoSmithKline, Johnson & Johnson, Roche-Chugai, Sanofi-Genzyme, all unrelated to this work, Grant/research support from: CSL Behring, GlaxoSmithKline, Johnson & Johnson, Roche-Chugai, Sanofi-Genzyme, all unrelated to this work, Marie Holmqvist: None declared, Carlo Alberto Scirè Grant/research support from: AbbVie, Lilly, both unrelated to this work, Rebecca Hasseli: None declared, Arundathi Jayatilleke: None declared, Tiffany Hsu: None declared, Kristin D’Silva: None declared, Victor Pimentel-Quiroz: None declared, Monica Vasquez del Mercado: None declared, Samuel Katsuyuki Shinjo: None declared, Edgard Reis Neto: None declared, Laurindo Rocha Jr: None declared, Ana Carolina de Oliveira e Silva Montandon Speakers bureau: GSK, not related to this work, Paula Jordan: None declared, Emily Sirotich: None declared, Jonathan Hausmann Speakers bureau: Novartis, Biogen, Pfizer, not related to this work, Consultant of: Novartis, Biogen, Pfizer, not related to this work, Jean Liew Grant/research support from: Pfizer research grant, completed in 2021, not related to this work, Lindsay Jacobsohn: None declared, Monique Gore-Massy Speakers bureau: Aurinia Pharmaceuticals, Boehringer Ingelheim, Bristol-Myers Squibb, not related to this work, Consultant of: Aurinia Pharmaceuticals, Boehringer Ingelheim, Bristol-Myers Squibb, not related to this work, Paul Sufka: None declared, Rebecca Grainger Speakers bureau: AbbVie, Janssen, Novartis, Pfizer and Cornerstones, all unrelated to this work, Consultant of: AbbVie, Novartis, both unrelated to this work, Suleman Bhana Shareholder of: Pfizer, Inc, Speakers bureau: AbbVie, Horizon, Novartis, and Pfizer, all unrelated to this work, Consultant of: AbbVie, Horizon, Novartis, and Pfizer, all unrelated to this work, Employee of: Pfizer, Inc, Zachary Wallace: None declared, Philip Robinson Speakers bureau: Abbvie, Janssen, Roche, GSK, Novartis, Lilly, UCB, all unrelated to this work, Paid instructor for: Lilly, unrelated to this work, Consultant of: GSK, Kukdong, Atom Biosciences, UCB, all unrelated to this work, Grant/research support from: Janssen, Pfizer, UCB and Novartis, all unrelated to this work, Jinoos Yazdany Consultant of: Aurinia, Astra Zeneca, Pfizer, all unrelated to this work, Grant/research support from: Astra Zeneca, Gilead, BMS Foundation, all unrelated to this work, Pedro Machado Speakers bureau: Abbvie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this work., Consultant of: Abbvie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this work.

AB1435 CLINICAL PREDICTION MODELS FOR METHOTREXATE OUTCOMES IN PATIENTS WITH RHEUMATOID ARTHRITIS: SYSTEMATIC REVIEW AND CRITICAL APPRAISAL

Background

Methotrexate (MTX) is the preferred first line therapy for rheumatoid arthritis (RA). MTX has several advantages over other treatments including effectiveness and low cost; however, around 40% of patients are classed as non-responders after 6 months (1). Therefore, there is a clinical need to identify patients at high-risk of poor outcomes, such that patients could potentially be fast tracked onto alternative therapies to improve their clinical outcomes and quality of life. Such risk stratification is possible through prognostic prediction models, although models which have previously been developed appear to have had little impact on practice. This may be in part due to methodological features of their development and validation but, to date, no review has collated the evidence in this field.

Objectives

This systematic review aimed to (i) identify and summarise multivariable prediction models of MTX treatment outcomes in biologic-naïve adult RA patients, and (ii) critically appraise their methodological properties.

Methods

The electronic databases Medline and Embase were searched to identify studies developing or validating prediction models of MTX outcomes in the population of interest, including demographic, disease-specific or treatment-related covariates, published after 2005. Models were stratified by outcome definition, and information on participants, predictors, model performance, handling of missing data and model validation were extracted. A risk of bias (ROB) assessment using PROBAST (prediction model risk of bias assessment tool) was carried out. Two reviewers were independently involved in screening, data extraction, and ROB stages.

Results

The included studies used three main outcome definitions: a state of disease activity, such as low disease activity or remission; the EULAR response criteria; or discontinuation due to adverse events (AEs). Some studies incorporated AEs into a composite outcome with disease activity and few accounted for potential competing risks, which are events that preclude the occurrence of the primary outcome of interest. Not handling competing risks may result in under-prediction, leading to potentially compromised risk stratification. There was a lack of internal validation using cross sampling techniques, which is critical for reducing overfitting, as well as external validation in new data, a process necessary to ensure reproducibility and generalisability of a prediction model to the larger patient population. Missing data was mostly handled using complete case analysis, leading to potentially biased risk estimates. The ROB assessment showed overall high ROB of the included studies.

Conclusion

This systematic review summarises current prediction models of MTX treatment outcomes in RA. It highlights several methodological shortcomings, such as poor handling of missing data and competing risks to the primary outcome, and a lack of internal and external validation. These should be addressed in future model development and validation to improve accuracy of predictions. Without tackling these issues, prediction of MTX treatment outcomes will remain at high risk of bias and should not be recommended for informing risk stratification for RA treatment decisions.

References

[1]Sergeant JC, Hyrich KL, Anderson J, Kopec-Harding K, Hope HF, Symmons DPM, et al. Prediction of primary non-response to methotrexate therapy using demographic, clinical and psychosocial variables: Results from the UK Rheumatoid Arthritis Medication Study (RAMS). Arthritis Res Ther. 2018;20(1):1–11.

Disclosure of Interests

Celina Gehringer: None declared, Glen Martin: None declared, Kimme Hyrich Speakers bureau: Abbvie, Grant/research support from: BMS and Pfizer, Suzanne Verstappen: None declared, Jamie Sergeant: None declared

POS1309 THE IMPACT OF PSORIASIS ON PATIENT-REPORTED OUTCOMES IN JUVENILE PSORIATIC ARTHRITIS

Background

Children with juvenile psoriatic arthritis (JPsA) are treated similarly to patients in other categories of juvenile idiopathic arthritis (JIA) despite distinctive clinical features, including either a personal or family history of psoriasis. It remains unknown whether the unique features in JPsA lead patients to experience the impact of disease differently from other JIA patients, and how the presence of psoriasis within JPsA affects patient outcomes. These gaps in our knowledge suggest there may be unmet treatment needs in these children and young people.

Objectives

To compare patient-reported outcomes in patients with JPsA and other JIA categories. Additionally, this study explores whether the presence or absence of psoriasis in patients with JPsA is associated with different outcomes.

Methods

Children and young people with JIA were selected if recruited to the Childhood Arthritis Prospective Study (CAPS), a UK multicentre inception cohort of JIA, between January 2001 and March 2018. Detailed demographic information, clinical data and patient-reported outcomes were collected at initial presentation to paediatric rheumatology. Patient-reported outcomes included well-being as measured on the parental global evaluation (10cm), functional ability (Childhood Health Assessment Questionnaire: CHAQ), pain (10cm), health-related quality of life (Child Health Questionnaire, CHQ psychosocial score), depressive symptoms (Mood and Feelings Questionnaire, MFQ) and parent psychosocial health (General Health Questionnaire: GHQ). The CHQ and MFQ questionnaires were completed in a subset of children recruited until 2015 and aged above 5 and 7 years old, respectively.

Patient-reported outcomes in patients with JPsA versus other JIA categories, and in patients with and without psoriasis within JPsA, were tested via multivariable linear regression, adjusted for age at first presentation, gender, disease duration, ethnicity, and number of active joints.

Results

A total of 1653 patients had JIA; the majority (64.7%) were female and median age at onset was 6.5 years (IQR 2.7 - 10.8). A total of 111 (6.7%) were categorised with JPsA, of which 62 (55.9%) were female with median age at onset of 10.2 (IQR 4.3 - 12.5). In those with JPsA, 35% had psoriasis at diagnosis.

There were no significant differences between JPsA and non-JPsA in terms of parental global evaluation of wellbeing (p=0.21), CHAQ (p=0.19), pain (p=0.38), CHQ psychosocial (p=0.18), GHQ (p=0.31) or MFQ (p=0.34).

Within JPsA, depressive symptom score on the MFQ was higher in patients with psoriasis compared to those without (coefficient=9.8, 95% CI=0.5 to 19.0, p-value=0.04). However, there were no significant differences in parental global evaluation (p=0.4), CHAQ (p=0.3), pain (p=0. 3), CHQ psychosocial score (p=0.5) or GHQ (p=0.7) between those with and without psoriasis in JPsA (Table 1).

Table 1.

Patient-reported outcomes in children and young people with JPsA with and without psoriasis

Patient-reported outcome at JPsA diagnosisOutcomeCoefficient95% confidence intervalp-valueReference – JPsA without psoriasisReferenceReferenceReferenceWellbeing: Parent global (0-10cm)JPsA with psoriasis0.5-0.8, 1.80.45Function: CHAQ (0-3)0.2-0.2, 0.60.28Pain (0-10cm)0.8-0.8, 2.40.34Psychosocial health: CHQ (0-100)-2.4-10.1, 5.30.52Parent psychosocial: GHQ (0-84)-1.3-8.7, 6.10.72Depressive symptoms: MFQ (0-66)9.80.5, 19.00.04

Conclusion

Despite the differences in clinical features present in JPsA compared to the other JIA subtypes, there were no statistically significant differences in patient-reported outcomes overall at diagnosis. However, within the JPsA group, even when adjusting for age, children with psoriasis at time of arthritis diagnosis reported higher depressive symptom scores compared to those without psoriasis. When treating children with JPsA, attention to diagnosing and treating both arthritis and psoriasis may help mood. If poor mood persists in this subtype, then further allied health care may be required.

Acknowledgements

We thank all the children and young people and their families involved in CAPS, as well as clinical staff and administrators. We also thank the data management team at the University of Manchester (UK). CAPS is funded by Versus Arthritis (UK grant 20542). This report includes independent research funded by the NIHR Biomedical Research Centre Funding Scheme. The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, the NIHR or the Department of Health. KLH is additionally supported by the Centre for Epidemiology Versus Arthritis (UK grant 21755) at the University of Manchester, UK.

Disclosure of Interests

Jie Man (Jasmine) Low: None declared, Kimme Hyrich Speakers bureau: AbbVie, Grant/research support from: BMS, UCB, and Pfizer, Nophar Geifman: None declared, Stephanie Shoop-Worrall: None declared

POS0504 LOW SOCIAL SUPPORT, WORSE FINANCIAL STATUS AND LIMITED PHYSICAL ACTIVITY AT RHEUMATOID ARTHRITIS ONSET PREDICTS EXCESS DISABILITY OVER 10 YEARS

Background

Previous research has identified trajectory groups of people with rheumatoid arthritis (RA) characterised by excess disability (with respect to inflammation)1. These excess disability trajectories were relatively fixed from symptom onset, indicating that sociodemographic and lifestyle factors prior to onset may partially determine the disability trajectories of people with RA, potentially mediated by patient reported outcomes (PROMs).

Objectives

To (i) investigate the relationship between social support, financial status and lifestyle factors and excess disability group membership in RA, and (ii) evaluate the mediating effect of pain, fatigue, anxiety and depression on this relationship.

Methods

Data came from the Etude et Suivi des Polyarthrites Indifférenciées Récentes (ESPOIR) study, a prospective cohort from 14 centres across France. Inclusion criteria were: >2 swollen joints for 6 weeks-6 months, certain / possible diagnosis of RA, aged 18-70 years, and no disease modifying treatments or glucocorticoids for >2 weeks. A previous study applying trajectory analysis to the 10-year disability and inflammation scores of the ESPOIR participants identified pairs of trajectories characterised by similar inflammation but different disability1. For the current analysis, those in the higher disability trajectories of each pair were allocated into the “excess disability” group. At baseline, participants reported demographics, patient reported outcomes (pain / fatigue visual analogue scales; anxiety / depression: Arthritis Impact Measurement Scales), social support (availability of financial and accommodation support, family contact, married / co-habiting), financial status (personal and family income, job status and level, home owner, ability to go to the cinema/shows / go on holiday) and lifestyle factors (smoking, alcohol, body mass index [BMI; from height and weight], physical activity). Structural equation modelling was used to combine the social support and financial status data into latent variables, and then assess the direct and indirect (mediated by PROMs) effects of these variables as well as lifestyle factors on excess disability (adjusted for age and gender).

Results

In total, 538 people with RA were included (mean [standard deviation] age: 48.3 [12.2] years; 79.2% women), 200 (37.2%) of whom had excess disability over 10 years. The excess disability group were older, included more women, and had worse PROMs at baseline compared with the no excess disability group (Table 1). Less social support (β 0.17, 95% CI 0.08, 0.26) and worse financial status (β 0.30, 95% CI 0.19, 0.41) both predicted excess disability group membership, as did lower physical activity (β 0.17, 95% CI 0.09, 0.25) (Figure 1), whereas smoking, alcohol and BMI at baseline did not. Only a small proportion of this effect was mediated by the PROMs (social support: 21%, financial status: 31%, physical activity: 28%; Figure 1).

Table 1.

Baseline characteristics

Excess disability,No excess disability,Mean (SD) / N (%)Mean (SD) / N (%)N200338Age, years50.4 (10.7)47.0 (12.8)Women, N(%)174 (87.0%)252 (74.6%)Symptom duration, months3.63 (2.02)3.36 (1.62)Pain VAS47.1 (27.4)37.0 (26.9)Fatigue VAS59.3 (27.2)46.5 (26.5)AIMS anxiety5.61 (2.25)4.71 (2.27)AIMS depression4.47 (2.24)3.47 (1.97)Health Assessment Questionnaire1.39 (0.64)0.93 (0.61)DAS28-2C4.04 (1.28)3.99 (1.34)

AIMS = Arthritis Impact Measurement Scales, DAS28-2C = two-component Disease Activity Score, SD = standard deviation, VAS = visual analogue scale

Conclusion

Disability resulting from RA is a complex phenomenon, arising from more than just joint inflammation. This analysis indicates that lack of social support, financial instability and lower physical fitness at symptom onset may explain the excess disability associated with RA. As only a small portion of the effect is mediated by PROMs, health and social inequalities may need to be targeted directly by interventions.

References

[1]Gwinnutt et al (2021), Ann Rheum Dis, 80(Suppl 1)

Disclosure of Interests

James Gwinnutt: None declared, Sam Norton: None declared, Kimme Hyrich Speakers bureau: Abbvie, Grant/research support from: Bristol-Myers Squibb, Pfizer, Mark Lunt: None declared, Bernard Combe: None declared, Nathalie Rincheval: None declared, Adeline Ruyssen-Witrand: None declared, Bruno Fautrel: None declared, Suzanne Verstappen: None declared.

POS0509 DEVELOPMENT OF A MULITNOMIAL PREDICTION MODEL OF TREATMENT RESPONSE TO ETANERCEPT IN A MULTI-CENTRE COHORT OF PATIENTS WITH ESTABLISHED RA

Background

Treatment response in rheumatoid arthritis (RA) is assessed through EULAR response groups of good, moderate, and poor response. Clinical prediction models from the literature typically frame this as a binary model, to differentiate poor from good and moderate responders. Here, we develop a multinomial model, to predict each group separately, after 3 months on the anti-TNF drug Etanercept (ETN).

Objectives

Develop and validate a multinomial prediction model of treatment response to ETN in RA, based on baseline clinical covariates.

Methods

We identified patients treated with ETN or biosimilars (N = 778) from the Biologics in RA Genetics and Genomics Study Syndicate (BRAGGSS). Response groups were derived from the CRP based 4C-DAS28 at baseline and 3 month follow up, yielding 310 good, 320 moderate, and 148 poor responders. A multinomial logistic regression model was fitted, using good responders as reference category. Multiple imputation by chained equations was used to impute missing data, and models were internally validated via bootstrapping. We report model accuracy, as well as calibration, and compare effect sizes across response groups. Table 1shows the baseline statistics, and odds ratios for the included covariates.

Table 1.

Baseline covariate statistics and odds ratios (in bold: significant at p < 0.05); HADS: Hospital Anxiety and Depression Scale

VariableMean (± SD)ORModerate [95% CI]pORPoor [95% CI]por % YesSwollen Joint8.84450.980.350.948e-3Count (SJC)(± 5.20)[0.95 1.02][0.89 0.98]Tender Joint14.68771.076e-61.050.01Count (TJC)(± 6.74)[1.04 1.10][1.01 1.08]General Health74.74291.000.60.981e-3Visual Analog Scale (GHVAS)(±17.79)[0.99 1.01][0.97 0.99]CRP19.07391.000.220.990.26(±25.07)[1.00 1.01][0.98 1.00]BMI30.30351.000.481.000.41(±23.28)[0.99 1.01][0.99 1.01]Age of47.33301.010.121.020.06onset(±13.86)[1.00 1.03][1.00 1.04]Disease9.94011.000.840.990.45duration(±10.35)[0.98 1.02][0.96 1.02]HAQ1.60851.480.022.951e-6(± 0.65)[1.06 2.08][1.91 4.54]HADS-Anxiety8.08681.040.191.060.12(± 4.54)[0.98 1.10][0.99 1.13]HADS-Depression7.38411.060.120.970.55(± 4.02)[0.99 1.13][0.89 1.06]Concurrent81.49%0.412e-40.520.03DMARD[0.26 0.66][0.28 0.94]Female78.66%1.390.121.110.71[0.92 2.10][0.65 1.87]Seropositive77.89%0.540.020.470.01[0.33 0.89][0.26 0.86]1st Biologic90.62%1.060.860.480.03[0.55 2.06][0.24 0.94]

Results

Adjusted for optimism, the multinomial model achieves an accuracy of 50.7% (IQR: 50 – 51.3%), with calibration slopes of 0.574 (IQR: 0.569 - 0.579) and 0.534 (IQR: 0.525 - 0.544) for moderate and poor response, respectively. Figure 1 shows a comparison of odds ratios (OR) for the different outcome groups. The Health Assessment Questionnaire (HAQ) score is the biggest driver of both moderate and poor response. Previous biologic treatment also predicts poor but not moderate response. Compared to the multinomial model, a binary model, that discriminates poor from moderate and good responders, underestimates the effect size of HAQ.

Figure 1.

Odds ratios of FIRSTBIO and HAQ for moderate and poor response. Size of crosses indicate 95% confidence intervals.

Conclusion

The model predicts EULAR response groups moderately well but is poorly calibrated, which can partly be explained by the generally higher sample size requirement of multinomial modelling. In the multinomial model, moderate and poor response is largely driven by the same covariates, which leads to blurred boundaries between good and poor responders, when response groups are merged to create a binary problem. Future research should consider the most appropriate model choice to describe data, including the use of multinomial instead of binomial models. More research and bigger sample sizes are required to improve on this multinomial model.

Disclosure of Interests

Michael Stadler: None declared, Stephanie Ling: None declared, Nisha Nair: None declared, John Isaacs Speakers bureau: Abbvie, Gilead, Roche, UCB, Grant/research support from: GSK, Janssen, Pfizer, Kimme Hyrich Speakers bureau: Abbvie, Grant/research support from: Pfizer and BMS, Ann Morgan Speakers bureau: Roche/ Chuga, Consultant of: GSK, Roche, Chugai, AstraZeneka, Regeneron, Sanofi, Vifor, Grant/research support from: Roche, Kiniksa Pharmaceuticals, Anthony G Wilson: None declared, Darren Plant: None declared, John Bowes: None declared, Anne Barton Grant/research support from: Pfizer, Galapagos, Scipher Medicine, and Bristol Myers Squibb.

AB0337 BASELINE C-REACTIVE PROTEIN PREDICTS ADHERENCE TO ADALIMUMAB THERAPY AT 3 MONTHS IN AN OBSERVATIONAL COHORT OF PATIENTS WITH RHEUMATOID ARTHRITIS

Background

Adherence to biologic treatment in rheumatoid arthritis (RA) is often self-reported and little is known about the predictors of adherence to biologic medications. Many studies have reported the predictors of adherence to be linked to psychological factors. A systematic review [1] identified several predictors of adherence to methotrexate in RA patients with the strongest predictors related to psychological factors including beliefs in medication necessity and absence of low mood. Mild disease activity was also found to be a significant predictor of adherence from this study. It is unknown whether similar factors will predict adherence in an established cohort of patients with RA starting biologic therapy.

Objectives

To investigate levels of self-reported adherence to adalimumab treatment and identify the contribution of demographic, physical and psychological factors to medication adherence in an RA cohort.

Methods

Patients with RA who were commencing on adalimumab were recruited through the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS), a large UK multicentre prospective observational cohort study. Demographics, baseline clinical and psychological measures including illness and medication beliefs were collected. Self-reported adherence, defined as the patient has never stopped, altered, missed, forgot to take, or took a lower dose than prescribed of adalimumab, were recorded at 3 months. Potential baseline predictors of adherence to adalimumab therapy were determined using logistic regression analyses.

Results

202 patients were included; 76% female, median (IQR): age 59 (52-67) years, pre-treatment DAS28-CRP score 5.6 (5.1-6.1) and disease duration 5 (2-15) years. During the first 3 months following commencement of adalimumab, 176 (87%) patients reported full adherence. Univariable analyses found that high baseline C-reactive protein (CRP) [odds ratio (OR) 1.04 per mg/L, 95% CI 1.01, 1.09] was associated with adherence to adalimumab at 3 months. However, there were no associations identified from the psychological variables and this includes perceived necessity towards medication [OR 0.92, 95% CI 0.79, 1.05], hospital depression score [OR 0.94, 95% CI 0.84, 1.06] and hospital anxiety score [OR 0.97, 95% CI 0.88, 1.08].

Conclusion

These findings suggest that the psychological measures were less able to predict adherence to adalimumab therapy. The high percentage of adherence during the first three months of therapy may limit power to detect small effects in this cohort. Further research to investigate whether psychological variables correlate with drug levels as an alternative surrogate for adherence and to consider including other biological agents with a longer follow-up timeline are needed.

High baseline CRP levels were associated with adherence. This finding suggests active disease with higher levels of inflammation in RA may be a factor for adherence in patients who are commencing biologic therapy.

References

[1]Hope, H. F., Bluett, J., Barton, A., Hyrich, K. L., Cordingley, L., & Verstappen, S. M. M. (2016). Psychological factors predict adherence to methotrexate in rheumatoid arthritis; findings from a systematic review of rates, predictors and associations with patient-reported and clinical outcomes. RMD Open, 2(1), e000171. https://doi.org/10.1136/rmdopen-2015-000171

Disclosure of Interests

Adlan Wafi Ramli: None declared, Nisha Nair: None declared, Kimme Hyrich Consultant of: AbbVie, Grant/research support from: Pfizer, BMS, John Isaacs Speakers bureau: Abbvie, Gilead, Roche, UCB, Grant/research support from: GSK, Janssen, Pfizer, Ann Morgan Speakers bureau: Roche/Chugai, Consultant of: GSK, Roche, Chugai, AstraZeneca, Regeneron, Sanofi, Vifor, Grant/research support from: Roche, Kiniksa Pharmaceuticals, Darren Plant: None declared, Anthony G Wilson: None declared, Anne Barton Grant/research support from: I have received grant funding from Pfizer, Galapagos, Scipher Medicine and Bristol Myers Squibb.

OP0249 CHARACTERISTICS ASSOCIATED WITH POOR COVID-19 OUTCOMES IN PEOPLE WITH PSORIASIS AND SPONDYLOARTHRITIS: DATA FROM THE COVID-19 PsoProtect AND GLOBAL RHEUMATOLOGY ALLIANCE PHYSICIAN-REPORTED REGISTRIES

Background

Some factors associated with severe COVID-19 outcomes have been identified in patients with psoriasis (PsO) and inflammatory/autoimmune rheumatic diseases, namely older age, male sex, comorbidity burden, higher disease activity, and certain medications such as rituximab. However, information about specificities of patients with PsO, psoriatic arthritis (PsA) and axial spondyloarthritis (axSpA), including disease modifying anti-rheumatic drugs (DMARDs) specifically licensed for these conditions, such as IL-17 inhibitors (IL-17i), IL-23/IL-12 + 23 inhibitors (IL-23/IL-12 + 23i), and apremilast, is lacking.

Objectives

To determine characteristics associated with severe COVID-19 outcomes in people with PsO, PsA and axSpA.

Methods

This study was a pooled analysis of data from two physician-reported registries: the Psoriasis Patient Registry for Outcomes, Therapy and Epidemiology of COVID-19 Infection (PsoProtect), comprising patients with PsO/PsA, and the COVID-19 Global Rheumatology Alliance (GRA) registry, comprising patients with PsA/axSpA. Data from the beginning of the pandemic up to 25 October, 2021 were included. An ordinal severity outcome was defined as: 1) not hospitalised, 2) hospitalised without death, and 3) death. A multivariable ordinal logistic regression model was constructed to assess the relationship between COVID-19 severity and demographic characteristics (age, sex, time period of infection), comorbidities (hypertension, other cardiovascular disease [CVD], chronic obstructive lung disease [COPD], asthma, other chronic lung disease, chronic kidney disease, cancer, smoking, obesity, diabetes mellitus [DM]), rheumatic/skin disease (PsO, PsA, axSpA), physician-reported disease activity, and medication exposure (methotrexate, leflunomide, sulfasalazine, TNFi, IL17i, IL-23/IL-12 + 23i, Janus kinase inhibitors (JAKi), apremilast, glucocorticoids [GC] and NSAIDs). Age-adjustment was performed employing four-knot restricted cubic splines. Country-adjustment was performed using random effects.

Results

A total of 5008 individuals with PsO (n=921), PsA (n=2263) and axSpA (n=1824) were included. Mean age was 50 years (SD 13.5) and 51.8% were male. Hospitalisation (without death) was observed in 14.6% of cases and 1.8% died. In the multivariable model, the following variables were associated with severe COVID-19 outcomes: older age (Figure 1), male sex (OR 1.53, 95%CI 1.29-1.82), CVD (hypertension alone: 1.26, 1.02-1.56; other CVD alone: 1.89, 1.22-2.94; vs no hypertension and no other CVD), COPD or asthma (1.75, 1.32-2.32), other lung disease (2.56, 1.66-3.97), chronic kidney disease (2.32, 1.50-3.59), obesity and DM (obesity alone: 1.36, 1.07-1.71; DM alone: 1.85, 1.39-2.47; obesity and DM: 1.89, 1.34-2.67; vs no obesity and no DM), higher disease activity and GC intake (remission/low disease activity and GC intake: 1.96, 1.36-2.82; moderate/severe disease activity and no GC intake: 1.35, 1.05-1.72; moderate/severe disease activity and GC intake 2.30, 1.41-3.74; vs remission/low disease activity and no GC intake). Conversely, the following variables were associated with less severe COVID-19 outcomes: time period after 15 June 2020 (16 June 2020-31 December 2020: 0.42, 0.34-0.51; 1 January 2021 onwards: 0.52, 0.41-0.67; vs time period until 15 June 2020), a diagnosis of PsO (without arthritis) (0.49, 0.37-0.65; vs PsA), and exposure to TNFi (0.58, 0.45-0.75; vs no DMARDs), IL17i (0.63, 0.45-0.88; vs no DMARDs), IL-23/IL-12 + 23i (0.68, 0.46-0.997; vs no DMARDs) and NSAIDs (0.77, 0.60-0.98; vs no NSAIDs).

Conclusion

More severe COVID-19 outcomes in PsO, PsA and axSpA are largely driven by demographic factors (age, sex), comorbidities, and active disease. None of the DMARDs typically used in PsO, PsA and axSpA, were associated with severe COVID-19 outcomes, including IL-17i, IL-23/IL-12 + 23i, JAKi and apremilast.

Acknowledgements

We thank all the contributors to the COVID-19 PsoProtect, GRA and EULAR Registries.

Disclosure of Interests

None declared

Risk-mitigating behaviours in people with inflammatory skin and joint disease during the COVID-19 pandemic differ by treatment type: a cross-sectional patient survey

BACKGROUND

Registry data suggest that people with immune-mediated inflammatory diseases (IMIDs) receiving targeted systemic therapies have fewer adverse coronavirus disease 2019 (COVID-19) outcomes compared with patients receiving no systemic treatments.

OBJECTIVES

We used international patient survey data to explore the hypothesis that greater risk-mitigating behaviour in those receiving targeted therapies may account, at least in part, for this observation.

METHODS

Online surveys were completed by individuals with psoriasis (globally) or rheumatic and musculoskeletal diseases (RMDs) (UK only) between 4 May and 7 September 2020. We used multiple logistic regression to assess the association between treatment type and risk-mitigating behaviour, adjusting for clinical and demographic characteristics. We characterized international variation in a mixed-effects model.

RESULTS

Of 3720 participants (2869 psoriasis, 851 RMDs) from 74 countries, 2262 (60·8%) reported the most stringent risk-mitigating behaviour (classified here under the umbrella term 'shielding'). A greater proportion of those receiving targeted therapies (biologics and Janus Kinase inhibitors) reported shielding compared with those receiving no systemic therapy [adjusted odds ratio (OR) 1·63, 95% confidence interval (CI) 1·35-1·97]. The association between targeted therapy and shielding was preserved when standard systemic therapy was used as the reference group (OR 1·39, 95% CI 1·23-1·56). Shielding was associated with established risk factors for severe COVID-19 [male sex (OR 1·14, 95% CI 1·05-1·24), obesity (OR 1·37, 95% CI 1·23-1·54), comorbidity burden (OR 1·43, 95% CI 1·15-1·78)], a primary indication of RMDs (OR 1·37, 95% CI 1·27-1·48) and a positive anxiety or depression screen (OR 1·57, 95% CI 1·36-1·80). Modest differences in the proportion shielding were observed across nations.

CONCLUSIONS

Greater risk-mitigating behaviour among people with IMIDs receiving targeted therapies may contribute to the reported lower risk of adverse COVID-19 outcomes. The behaviour variation across treatment groups, IMIDs and nations reinforces the need for clear evidence-based patient communication on risk-mitigation strategies and may help inform updated public health guidelines as the pandemic continues.

OP0162 ETANERCEPT RESPONSE CLUSTERS IN JUVENILE IDIOPATHIC ARTHRITIS

Background:

In children and young people (CYP) with JIA, we have previously identified clusters with different patterns of disease impact following methotrexate (MTX) initiation. It is unclear whether clusters of treatment response following etanercept (ETN) therapy exist and whether, in a group of CYP who have responded inadequately to or had adverse events on methotrexate, similar treatment response patterns exist. Novel response patterns would aid stratified treatment approaches through better understanding and potential forecasting of more specific response patterns across multiple domains of disease.

Objectives:

To identify and characterise trajectories of juvenile arthritis disease activity score (JADAS) components following ETN initiation for JIA.

Methods:

ETN-naïve CYP with non-systemic JIA were selected if enrolled prior to January 2019 in at least one of four CLUSTER consortium studies: BSPAR-ETN, BCRD, CAPS and CHARMS, at point of starting ETN as their first biological therapy. JADAS components (active joint count, physician’s global assessment (0-10cm), parental global evaluation (0-10cm) and standardised ESR (0-10) were collected at ETN initiation and during the following year.

Multivariate group-based trajectory models, that identify clusters of CYP with similar patterns of change over time, were used to explore ETN response clusters across the different JADAS components. Censored-normal (global scores, ESR) and zero-inflated Poisson (active joint count) models were used, adjusting for year of ETN initiation. Optimal models were selected based on a combination of model fit (BIC), parsimony, and clinical plausibility.

Results:

Of the 1003 CYP included, the majority were female (70%) and of white ethnicity (90%), with rheumatoid factor-negative JIA the most common disease category (39%).

The optimal model identified five trajectory clusters of disease activity following initiation of ETN (Figure 1). Clusters following ETN were similar and covered similar proportions of CYP to those previously identified following MTX: Fast (Group 1: 13%) and Slow (Group 2: 10%) response, active joint count improves but either physician (Group 3: 6%) or parent global scores (Group 4: 34%) remain persistently raised and a group with persistent raised scores across all JADAS components (Group 5: 36%). Compared to the persistent disease cluster, those with greater improvement had lower age and higher functional ability at ETN initiation and those with persistent raised parent global scores had lower ESR levels and were less likely to be RF-positive at ETN initiation.

Figure 1.

Clusters identified following ETN initiation in children and young people recruited to the UK BSPAR-ETN, BCRD, CAPS and CHARMS studies.

Conclusion:

This study has identified that within CYP initiating ETN, similar response clusters are evident to those previously identified following MTX. This commonality suggests a new framework for understanding treatment response, beyond a simple responder/non-responder analysis at a set point, which applies across multiple drugs despite different mechanisms of action and previous unfavourable treatment outcomes. Understanding both clinical factors associated with, and biological mechanisms underpinning, these clusters would aid stratified medicine in JIA.

Acknowledgements:

We thank the children, young people and families involved in CLUSTER, as well as clinical staff, administrators and data management teams. Funding for CLUSTER has been provided by generous grants from the MRC, Versus Arthritis, GOSH children’s charity, Olivia’s vision and the NIHR Manchester and GOSH BRC schemes.

Disclosure of Interests:

Stephanie Shoop-Worrall: None declared, Kimme Hyrich Speakers bureau: Abbvie, Grant/research support from: BMS, UCB, Pfizer, Lucy Wedderburn Speakers bureau: Pfizer, Grant/research support from: Abbvie, Sobi, Wendy Thomson Grant/research support from: Abbvie, Sobi, Nophar Geifman Grant/research support from: Abbvie, Sobi

OP0286 CHARACTERISTICS ASSOCIATED WITH SEVERE COVID-19 OUTCOMES IN SYSTEMIC LUPUS ERYTHEMATOSUS (SLE): RESULTS FROM THE COVID-19 GLOBAL RHEUMATOLOGY ALLIANCE (COVID-19 GRA)

Background:

An increased risk of severe COVID-19 outcomes may be seen in patients with autoimmune diseases on moderate to high daily doses of glucocorticoids, as well as in those with comorbidities. However, specific information about COVID-19 outcomes in SLE is scarce.

Objectives:

To determine the characteristics associated with severe COVID-19 outcomes in a multi-national cross-sectional registry of COVID-19 patients with SLE.

Methods:

SLE adult patients from a physician-reported registry of the COVID-19 GRA were studied. Variables collected at COVID-19 diagnosis included age, sex, race/ethnicity, region, comorbidities, disease activity, time period of COVID-19 diagnosis, glucocorticoid (GC) dose, and immunomodulatory therapy. Immunomodulatory therapy was categorized as: antimalarials only, no SLE therapy, traditional immunosuppressive (IS) drug monotherapy, biologics/targeted synthetic IS drug monotherapy, and biologic and traditional IS drug combination therapy. We used an ordinal COVID-19 severity outcome defined as: not hospitalized/hospitalized without supplementary oxygen; hospitalized with non-invasive ventilation; hospitalized with mechanical ventilation/extracorporeal membrane oxygenation; and death. An ordinal logistic regression model was constructed to assess the association between demographic characteristics, comorbidities, medications, disease activity and COVID-19 severity. This assumed that the relationship between each pair of outcome groups is of the same direction and magnitude.

Results:

Of 1069 SLE patients included, 1047 (89.6%) were female, with a mean age of 44.5 (SD: 14.1) years. Patient outcomes included 815 (78.8%) not hospitalized/hospitalized without supplementary oxygen; 116 (11.2) hospitalized with non-invasive ventilation, 25 (2.4%) hospitalized with mechanical ventilation/extracorporeal membrane oxygenation and 78 (7.5%) died. In a multivariate model (n=804), increased age [OR=1.03 (1.01, 1.04)], male sex [OR =1.93 (1.21, 3.08)], COVID-19 diagnosis between June 2020 and January 2021 (OR =1.87 (1.17, 3.00)), no IS drug use [OR =2.29 (1.34, 3.91)], chronic renal disease [OR =2.34 (1.48, 3.70)], cardiovascular disease [OR =1.93 (1.34, 3.91)] and moderate/high disease activity [OR =2.24 (1.46, 3.43)] were associated with more severe COVID-19 outcomes. Compared with no use of GC, patients using GC had a higher odds of poor outcome: 0-5 mg/d, OR =1.98 (1.33, 2.96); 5-10 mg/d, OR =2.88 (1.27, 6.56); >10 mg/d, OR =2.01 (1.26, 3.21) (Table 1).

Table 1.

Characteristics associated with more severe COVID-19 outcomes in SLE. (N=804)

OR (95% CI)Age, years1.03 (1.01, 1.04)Sex, Male1.93 (1.21, 3.08)Race/Ethnicity, Non-White vs White1.47 (0.87, 2.50)RegionEuropeRef.North America0.67 (0.29, 1.54)South America0.67 (0.29, 1.54)Other1.93 (0.85, 4.39)Season, June 16th 2020-January 8th 2021 vs January-June 15th 20201.87 (1.17, 3.00)Glucocorticoids0 mg/dayRef.0-5 mg/day1.98 (1.33, 2.96)5-10 mg/day2.88 (1.27, 6.56)=>10 mg/day2.01 (1.26, 3.21)Medication CategoryAntimalarial onlyRef.No IS drugs2.29 (1.34, 3.91)Traditional IS drugs as monotherapy1.17 (0.77, 1.77)b/ts IS drugs as monotherapy1.00 (0.37, 2.71)Combination of traditional and b/ts IS1.00 (0.55, 1.82)Comorbidity BurdenNumber of Comorbidities (excluding renal and cardiovascular disease)1.39 (0.97, 1.99)Chronic renal disease2.34 (1.48, 3.70)Cardiovascular disease1.93 (1.34, 3.91)Disease Activity, Moderate/ high vs Remission/ low 2.24 (1.46, 3.43)

IS: immunosuppressive. b/ts: biologics/targeted synthetics

Conclusion:

Increased age, male sex, glucocorticoid use, chronic renal disease, cardiovascular disease and moderate/high disease activity at time of COVID-19 diagnosis were associated with more severe COVID-19 outcomes in SLE. Potential limitations include possible selection bias (physician reporting), the cross-sectional nature of the data, and the assumptions underlying the outcomes modelling.

Acknowledgements:

The views expressed here are those of the authors and participating members of the COVID-19 Global Rheumatology Alliance and do not necessarily represent the views of the ACR, EULAR) the UK National Health Service, the National Institute for Health Research (NIHR), or the UK Department of Health, or any other organization.

Disclosure of Interests:

Manuel F. Ugarte-Gil Grant/research support from: Pfizer, Janssen, Graciela S Alarcon: None declared, Andrea Seet: None declared, Zara Izadi: None declared, Cristina Reategui Sokolova: None declared, Ann E Clarke Consultant of: AstraZeneca, BristolMyersSquibb, GlaxoSmithKline, Exagen Diagnostics, Leanna Wise: None declared, Guillermo Pons-Estel: None declared, Maria Jose Santos: None declared, Sasha Bernatsky: None declared, Lauren Mathias: None declared, Nathan Lim: None declared, Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Gilead, Inova, Janssen, and Optum unrelated to this work., Grant/research support from: Amgen and Bristol-Myers Squibb, Zachary Wallace Consultant of: Viela Bio and MedPace, Grant/research support from: Bristol-Myers Squibb and Principia/Sanofi, Kimme Hyrich Speakers bureau: Abbvie, Grant/research support from: MS, UCB, and Pfizer, Anja Strangfeld Speakers bureau: AbbVie, MSD, Roche, BMS, Pfizer, Grant/research support from: AbbVie, BMS, Celltrion, Fresenius Kabi, Lilly, Mylan, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis, and UCB, Laure Gossec Consultant of: Abbvie, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sanofi-Aventis, UCB, Grant/research support from: Lilly, Mylan, Pfizer, Loreto Carmona: None declared, Elsa Mateus Grant/research support from: Pfizer, Abbvie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern, GAfPA, Saskia Lawson-Tovey: None declared, Laura Trupin: None declared, Stephanie Rush: None declared, Gabriela Schmajuk: None declared, Patti Katz: None declared, Lindsay Jacobsohn: None declared, Samar Al Emadi: None declared, Emily Gilbert: None declared, Ali Duarte-Garcia: None declared, Maria Valenzuela-Almada: None declared, Tiffany Hsu: None declared, Kristin D’Silva: None declared, Naomi Serling-Boyd: None declared, Philippe Dieudé Consultant of: Boerhinger Ingelheim, Bristol-Myers Squibb, Lilly, Sanofi, Pfizer, Chugai, Roche, Janssen unrelated to this work, Grant/research support from: Bristol-Myers Squibb, Chugaii, Pfizer, unrelated to this work, Elena Nikiphorou: None declared, Vanessa Kronzer: None declared, Namrata Singh: None declared, Beth Wallace: None declared, Akpabio Akpabio: None declared, Ranjeny Thomas: None declared, Suleman Bhana Consultant of: AbbVie, Horizon, Novartis, and Pfizer (all <$10,000) unrelated to this work, Wendy Costello: None declared, Rebecca Grainger Speakers bureau: Abbvie, Janssen, Novartis, Pfizer, Cornerstones, Jonathan Hausmann Consultant of: Novartis, Sobi, Biogen, all unrelated to this work (<$10,000), Jean Liew Grant/research support from: Pfizer outside the submitted work, Emily Sirotich Grant/research support from: Board Member of the Canadian Arthritis Patient Alliance, a patient run, volunteer based organization whose activities are largely supported by independent grants from pharmaceutical companies, Paul Sufka: None declared, Philip Robinson Speakers bureau: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer and UCB (all < $10,000), Consultant of: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer and UCB (all < $10,000), Pedro Machado Speakers bureau: Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all < $10,000)., Consultant of: Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all < $10,000), Milena Gianfrancesco: None declared, Jinoos Yazdany Consultant of: Eli Lilly and AstraZeneca unrelated to this project

POS1292 REAL-WORLD EFFECTIVENESS OF ETANERCEPT AND ADALIMUMAB IN CHILDREN AND YOUNG PEOPLE WITH JUVENILE IDIOPATHIC ARTHRITIS (JIA) WITHOUT UVEITIS

Background:

Biologic therapies have revolutionised treatment pathways and outcomes for patients with juvenile idiopathic arthritis (JIA). Although there is a choice of approved TNF inhibitors available as a first biologic, there lacks data to inform treatment choices in clinical practice.

Objectives:

To compare short-term outcomes on etanercept and adalimumab in children and young people with JIA without uveitis, including drug survival, arthritis disease activity and function ability at 1 year.

Methods:

All patients starting a first biologic (etanercept/adalimumab including biosimilars) from 2010 in the UK JIA biologic registers (BCRD and BSPAR ETN) were included. Those with systemic JIA or with any history of uveitis were excluded. Data were collected at start of therapy, 6 months, 1 year, and then annually, including patient demographic, disease activity and drug therapy. In this analysis, drug survival and arthritis disease activity / function ability at 1 year (range 3-15 months) were investigated; comparing between therapies using logistic / linear regression, adjusted for propensity deciles.

Results:

There were 550 patients with outcome data available (to 30 Sept 2020); 384 etanercept, 166 adalimumab. At registration, 68% female, median age 12 years old (IQR 8-14), median disease duration 1 year (IQR 1-4), 72% on concomitant methotrexate. Disease activity was similar between both therapies at baseline and one year. At one year, 70% were still on biologic therapy; most stopping therapy for ineffectiveness (45%), adverse events (31%), or patient / family choice (15%). Inactive disease and minimal disease activity was achieved in 26% and 46% respectively, 48% achieved a minimally clinical important improvement in their functional ability (CHAQ improvement >0.13).

All PatientsAdalimumabEtanerceptN550166384Females68%59%71%Age (years), median (IQR)12 (8-14)12 (10-14)11 (8-14)Disease duration (years), median (IQR)1 (1-4)1 (0-3)1 (1-4)ILARPersistent oligo9%6%11%Extended oligo20%14%23%RF negative37%32%39%RF positive11%11%12%Psoriatic5%8%4%Enthesitis-related16%27%11%Undifferentiated1%2%1%Concomitant oral steroids16%20%15%Concomitant methotrexate72%84%66%Follow-up time, yearsMedian (IQR)2.5 (1.4-3.8)2.1 (1.2-3.1)3.0 (1.6-4.0)Min-Max0.4 - 8.20.4 - 7.30.4 - 8.2Drug SurvivalStill on drug at one year70%67%71%Still on drug at two years47%50%46%CHAQBaseline, mean (SE)0.9 (0.04)0.8 (0.06)1.0 (0.04)One Year, mean (SE)0.7 (0.03)0.5 (0.06)0.7 (0.04)Change, mean (SE)-0.2 (0.04)-0.2 (0.06)-0.2 (0.04)Regression coef (95% CI)--0.09 (-0.2, 0.04)RefPD Adjusted coef (95% CI)--0.08 (-0.2, 0.07)RefMCID (CHAQ)Proportion achieved48%48%48%OR (95% CI)-1.0 (0.6, 1.5)RefPD Adjusted OR (95% CI)-1.2 (0.8, 1.9)RefJADASBaseline, mean (SE)14 (0.4)14 (0.7)14 (0.4)One Year, mean (SE)5 (0.3)4 (0.5)6 (0.3)Change, mean (SE)-9 (0.4)-9 (0.7)-8 (0.5)Regression coef (95% CI)--1.1 (-2.3, -0.01)*RefPD Adjusted coef (95% CI)--1.0 (-2.8, 0.8)RefInactive Disease (JADAS<1)Proportion achieved26%32%24%OR (95% CI)-1.5 (1.0, 2.4)RefPD Adjusted OR (95% CI)-1.5 (0.9, 2.4)RefMinimal Disease Activity (MDA) [excludes enthesitis-related JIA]N=473N=121N=352Proportion achieved46%49%45%OR (95% CI)-1.2 (0.8, 1.9)RefPD Adjusted OR (95% CI)-1.2 (0.8, 2.0)Ref

Childhood Health Assessment Questionnaire (CHAQ), confidence interval (CI), International League Against Rheumatism (ILAR), interquartile range (IQR), odds ratio (OR), propensity decile (PD), rheumatoid factor (RF), standard error (SE). *p<0.05

Conclusion:

This is the first comparative effectiveness analysis of adalimumab and etanercept within UK children receiving TNFi therapies for JIA. Despite large patient numbers, there was no evidence of difference between adalimumab and etanercept regarding arthritis disease control or treatment persistence. For children without uveitis, both adalimumab and etanercept can be considered as effective treatment options for children and young people with JIA.

Disclosure of Interests:

None declared

OP0241 SERIOUS INFECTION WITH TOCILIZUMAB COMPARED TO TNF-INHIBITORS AND OTHER BDMARDS IN RHEUMATOID ARTHRITIS PATIENTS: DOES LINE OF THERAPY MATTER?

Background:

In the real-world, tocilizumab is prescribed to a population of patients different from those prescribed TNF-inhibitors, often older with longer disease duration, worse functional status and more previous b- or tsDMARDs.

Objectives:

The aim of this study was to evaluate if and how the risk of serious infection on tocilizumab and other bDMARDs differs when stratifying by line of therapy in a real-world population of rheumatoid arthritis patients.

Methods:

We included patients registered in the BSRBR-RA treated with tocilizumab, etanercept, adalimumab, infliximab, certolizumab, abatacept or rituximab, including biosimilars. Primary outcome was the occurrence of a serious infection (defined as infection requiring hospitalisation, intravenous antibiotics or resulting in death). Primary covariate of interest was line of therapy (from first to fifth line of therapy). Every change to another b- or tsDMARD was considered a new line of therapy, but not a change between a bio-original and a biosimilar.

Hazard ratios (HR) of serious infections were estimated using an inverse probability weighted Cox regression, based on a propensity score including baseline patient and disease characteristics, and adjusting for time in study (see Table). The reference group was etanercept, which included the highest number of patients. Treatment exposure was analysed without and with stratification by line of therapy.

Table.NETNTCZADAIFXCERTRTXABAN33,91610,6552,6327,8394,4301,6165,5561,188Patient-years19,1294,34214,5048,1352,72612,0091,686Infections8071926814817443374Incidence per 100 patient-years (95%CI)4.2 (3.9-4.5)4.4 (3.8-5.1)4.7 (4.4-5.1)5.9 (5.4-6.6)2.7 (2.2-3.4)3.6 (3.3-4.0)4.0 (3.2-5.1)Unadjusted HR (95%CI)Ref.1.0 (0.9-1.2)1.1 (1.0-1.2)1.4 (1.2-1.6)0.6 (0.5-0.8)0.9 (0.8-1.0)0.9 (0.7-1.2)Adjusted HR* (95% CI)All lines of therapy33,916Ref.1.2 (1.0-1.5)1.1 (1.0-1.3)1.3 (1.1-1.6)0.8 (0.6-1.0)1.0 (0.8-1.1)1.2 (0.8-1.7)1stline16,152Ref.0.9 (0.5-1.5)1.1 (1.0-1.3)1.3 (1.1-1.6)0.6 (0.5-0.9)1.6 (1.2-2.2)-2ndline10,378Ref.1.4 (1.0-2.0)1.1 (0.9-1.4)1.1 (0.7-1.6)0.9 (0.4-2.2)1.0 (0.8-1.2)0.9 (0.5-1.9)3rdline4,676Ref.1.4 (0.9-2.3)1.3 (0.8-2.2)0.9 (0.4-1.9)0.9 (0.3-2.9)0.8 (0.5-1.2)1.5 (0.7-2.9)4thline1,947Ref.1.0 (0.5-2.3)1.3 (0.4-3.7)1.4 (0.5-4.4)0.2 (0.0—2.1)1.0 (0.5.-2.2)0.9 (0.4-2.3)5thline763Ref.0.9 (0.2-3.5)2.5 (0.5-12.4)0.7 (0.1-7.1)3.3 (0.6-18.4)0.9 (0.2-3.5)0.8 (0.2-3.5)

ABA, abatacept; ADA, adalimumab; CERT, certolizumab; ETN, etanercept; HR, hazard ratio; IFX, infliximab; RTX, rituximab; TCZ, tocilizumab

*Adjusted using inverse probably weighting (with age, gender, concomitant steroids, concomitant DMARDs, comorbidities, seropositivity, smoking, disease duration, HAQ and DAS28 at baseline in the model) and time since study entry (categorised from 0 to 4, 0 starting just before or at the moment of entering study, 1 starting during the first year, 2 starting during the second year until 4 for the fourth year and more)

Results:

A total of 33,916 treatment courses were included (Table) contributing to 62,532 years of follow-up. Compared to etanercept, participants starting abatacept, tocilizumab and rituximab were older, had more previous bDMARDs, longer disease duration and more comorbidities. The crude HR of serious infections were higher with infliximab and adalimumab, lower with certolizumab and rituximab, and not significantly different for abatacept and tocilizumab compared to etanercept. After adjustment, HR of serious infections were higher with tocilizumab, adalimumab and infliximab. However, when stratified by line of therapy, HR were no longer significantly different compared to etanercept for tocilizumab, adalimumab and infliximab for most lines of therapy.

Conclusion:

Whilst initially there appears to be a difference in rates of serious infection between biologic therapies, line of therapy may be a confounding factor when comparing the risk of serious infections between bDMARDs.

Disclosure of Interests:

Kim Lauper Consultant of: Gilead-galapagos, Grant/research support from: AbbVie, Lianne Kearsley-Fleet: None declared, Rebecca Davies: None declared, Kath Watson: None declared, Mark Lunt: None declared, Kimme Hyrich Consultant of: AbbVie, Grant/research support from: Pfizer, BMS

OP0183 IDENTIFICATION OF A SUBGROUP OF PEOPLE WITH RHEUMATOID ARTHRITIS CHARACTERISED BY HIGH DISABILITY OVER 10 YEARS, DESPITE LOW INFLAMMATION: RESULTS FROM TWO EUROPEAN PROSPECTIVE COHORT STUDIES

Background:

Long-term studies in rheumatoid arthritis (RA) have reported low inflammation yet high disability over time. It is important to determine which factors are driving this disparity, so appropriate interventions can be used to reduce this gap.

Objectives:

To identify a subgroup of people with RA with low inflammation yet high disability over 10 years, and describe their characteristics.

Methods:

Data came from two cohorts of inflammatory arthritis with regular assessments over 10 years: the Norfolk Arthritis Register (NOAR, inclusion: ≥2 swollen joints for ≥4 weeks) from the UK and the Etude et Suivi des Polyarthrites Indifférenciées Récentes study (ESPOIR, inclusion: early RA) from France. Participants provided demographic data and completed patient reported outcomes (PROs, including the Health Assessment Questionnaire [HAQ]). The 2-component Disease Activity Score (DAS28-2C)1, a measure of inflammation, was calculated from swollen joint counts and C-reactive protein level. Inclusion criteria for this analyis: <2 years baseline symptom duration; HAQ and DAS28-2C at baseline and one other follow-up; recruited after 1/1/2000. HAQ and DAS28-2C were modelled simultaneously using a multivariate group-based trajectory model, to identify groups of participants with similar trajectories of HAQ and DAS28-2C over 10 years. Baseline demographics and PROs were compared between the trajectory groups using logistic regression. Analyses performed separately in NOAR and ESPOIR.

Results:

1001 NOAR and 767 ESPOIR participants were included. In both cohorts, a four group trajectory model had the best fit (Figure). Two subgroups were identified in each cohort that demonstrated the hypothesised relationship: similar DAS28-2C but differing HAQ scores (red trajectories in Figure), titled “High HAQ” and “Low HAQ” (mean difference in HAQ over follow-up [95% confidence interval (CI)]: NOAR 0.76 [0.73, 0.80]; ESPOIR 0.89 [0.82, 0.96]). At baseline, the High HAQ groups in both NOAR and ESPOIR were older, had a higher proportion of women, and had higher levels of fatigue (NOAR: odds ratio [OR] 1.16 [95% CI 1.06, 1.28]; ESPOIR: OR 1.20 [95% CI 1.05, 1.36] [Table]) and pain (NOAR only).

Table 1.

Baseline characteristics / logistic regression analysis

NOARESPOIRVariableLow HAQ trajectory, mean (SD)High HAQ trajectory, mean (SD)OR (95% CI)Low HAQ trajectory, mean (SD)High HAQ trajectory, mean (SD)OR (95% CI)N (%)343 (59%)239 (41%)-131 (55%)108 (45%)-Age, years54.9 (14.2)62.1 (13.8)1.07 (1.05, 1.08)47.8 (13.3)51.8 (11.2)1.04 (1.01, 1.06)Women, N (%)224 (65.3%)176 (73.6%)1.82 (1.12, 2.78)100 (76.3%)95 (88.0%)2.73 (1.20, 6.23)Symptom duration, months7.8 (5.1)8 (5.4)1.10 (0.98, 1.05)3.4 (1.8)3.6 (1.8)1.16 (0.98, 1.37)Current smoker, N (%)77 (22.4%)50 (20.9%)1.19 (0.71, 2.00)61 (46.6%)52 (48.1%)1.52 (0.82, 2.83)DAS28-2C3.14 (1.46)3.21 (1.56)-4.65 (1.31)4.41 (1.35)-HAQ0.8 (0.6)1.4 (0.5)-1.1 (0.6)1.6 (0.6)-Pain (0-10)3.7 (2.4)4.6 (2.5)1.16 (1.07, 1.26)4.1 (2.8)5.1 (2.6)1.07 (0.95, 1.20)Fatigue (0-10)4.3 (2.8)5.3 (2.5)1.16 (1.06, 1.28)5.0 (2.6)6.5 (2.5)1.20 (1.05, 1.36)AIMS anxiety3.99 (1.96)4.42 (1.99)1.06 (0.88, 1.29)4.9 (2.26)5.98 (2.25)1.10 (0.94, 1.29)AIMS depression2.85 (1.87)3.38 (1.87)1.10 (0.94, 1.29)3.96 (1.99)5.08 (2.32)1.12 (0.94, 1.33)RF+, N (%)142 (41.4%)106 (44.4%)0.94 (0.60, 1.46)79 (60.3%)50 (46.3%)0.77 (0.34, 1.75)Anti-CCP+, N (%)113 (32.9%)86 (36.0%)1.35 (0.84, 2.17)76 (58.0%)45 (41.7%)0.89 (0.39, 2.05)

Conclusion:

There is a group of people with RA with high levels of disability, despite low inflammation. These results underline the potential need for pain and fatigue management in people with RA, even when inflammation is low.

References:

[1]Hensor et al (2019). Rheumatology (Oxford) 58(8)

Acknowledgements:

Thanks to the participants of NOAR and ESPOIR and those working in the recruiting centres

ESPOIR Funding:

An unrestricted grant from Merck Sharp and Dohme (MSD) was allocated for the first 5 years of the cohort study. Two additional grants from INSERM supported part of the biological database. The French Society of Rheumatology, Abbvie, Pfizer, Lilly and more recently Fresenius and Biogen supported the ESPOIR cohort study.

Disclosure of Interests:

James Gwinnutt Grant/research support from: Research grant from Bristol Myers Squibb unrelated to this project, Sam Norton Consultant of: Pfizer and AstraZeneca, Kimme Hyrich Consultant of: Abbvie, Grant/research support from: Pfizer and BMS, Mark Lunt: None declared, Bernard Combe: None declared, Nathalie Rincheval: None declared, Adeline Ruyssen-Witrand: None declared, Bruno Fautrel: None declared, Jacqueline Chipping: None declared, Alex MacGregor: None declared, Suzanne Verstappen: None declared

POS0357 MiRNAs CORRELATE WITH IMPROVEMENT IN DISEASE ACTIVITY IN PATIENTS WITH RHEUMATOID ARTHRITIS ON TUMOUR NECROSIS FACTOR INHIBITORS

Background:

Tumour necrosis factor inhibitors (TNFi) although effective in the treatment of rheumatoid arthritis (RA), show a variable response rate. Therefore, there is a need to identify treatment response predictors to inform therapy selection in order to practise precision medicine. MicroRNAs (miRNAs) are endogenous, single-stranded, non-coding RNAs that can alter gene expression by regulating messenger RNA translation. There is evidence for miRNA involvement in RA pathogenesis and they may serve as a useful biomarker of treatment response.

Objectives:

To identify miRNAs associated with response to TNFi in RA.

Methods:

Biologic naïve patients were selected from the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS), a prospective multi-center UK study investigating treatment response biomarkers to TNFi with a primary outcome measure of change in DAS28 scores. Patients were stratified into European League Against Rheumatism (EULAR) good or non-responders based on their 3 or 6-month DAS28-CRP score.

Pre-treatment and 3-month post-treatment serum samples were substrates for miRNA profiling, which was conducted by FIRALIS using the HTG EdgeSeq miRNA whole transcriptome V2 targeted sequencing assay. Linear modelling using R package limma compared miRNA expression at (i) pre-treatment and at three-months, in EULAR good-responders and non-responders (ii) longitudinal change in expression from pre-treatment to three-months in EULAR good and non-responders.

A literature search was conducted to identify miRNAs associated with RA as a diagnostic and/or treatment response predictor. Data on these miRNAs were extracted from the miRNAs identified in the serum samples. A correction for multiple testing was applied to statistical tests.

Results:

A total of 54 patients were analysed; of these, 35 (65%) were female, median disease duration [inter-quartile range] was 6 years [2 – 14] (n=51), and 44/51 (86%) patients were on a concomitant disease modifying anti-rheumatic drug. Of the 54 patients, 39 (72%) were classified as EULAR good-responders and 15 (28%) as non-responders. 1880 miRNAs were detected in the serum samples. 64 miRNAs were identified to be associated with RA from the literature, of which, 26 were identified in the serum samples tested.

No difference in pre-treatment or three-month miRNA levels was seen comparing EULAR good-responders and non-responders (FDR p<0.05). There was a significant differential expression of four miRNAs at 3-months in good-responders compared with pre-treatment levels; miR-125a-3p (downregulated, p-value 0.002), miR-149-3p (upregulated, p-value 0.004), miR-766-3p (downregulated, p-value 0.008), miR-146b-5p (upregulated, p-value 0.006). No significant differences were observed between 3-months and baseline in non-responders.

Conclusion:

Although no pre-treatment miRNAs were associated with TNFi response, changes in the levels of four miRNAs were detected at 3-months compared to baseline in EULAR good-responders. Future work involves validation of these samples in a larger patient cohort and analysing miRNA levels at 6 and 12 months. Replication and validation of these results in larger studies are required to analyse the role of miRNAs in stratifying EULAR good-responders from non-responders at three-months, and as treatment response predictors to TNFi in RA.

Acknowledgements:

Joint last-author: Dr. Darren Plant

Disclosure of Interests:

Trixy David: None declared, Nisha Nair: None declared, James Oliver: None declared, Eric Schordan: None declared, Hüseyin Firat: None declared, Kimme Hyrich Consultant of: consultancy/honoraria from AbbVie, Grant/research support from: Pfizer, UCB, BMS, Ann Morgan: None declared, Anthony G Wilson: None declared, John D Isaacs Speakers bureau: consultancy/speaker fees from AbbVie, Gilead, Roche, UCB, Consultant of: consultancy/speaker fees from AbbVie, Gilead, Roche, UCB, Grant/research support from: Pfizer, Darren Plant: None declared, Anne Barton: None declared

OP0288 MACHINE LEARNING ALGORITHMS TO PREDICT COVID-19 ACUTE RESPIRATORY DISTRESS SYNDROME IN PATIENTS WITH RHEUMATIC DISEASES: RESULTS FROM THE GLOBAL RHEUMATOLOGY ALLIANCE PROVIDER REGISTRY

Background:

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening complication of COVID-19 and has been reported in approximately one-third of hospitalized patients with COVID-191. Risk factors associated with the development of ARDS include older age and diabetes2. However, little is known about factors associated with ARDS in the setting of COVID-19, in patients with rheumatic disease or those receiving immunosuppressive medications. Prediction algorithms using traditional regression methods perform poorly with rare outcomes, often yielding high specificity but very low sensitivity. Machine learning algorithms optimized for rare events are an alternative approach with potentially improved sensitivity for rare events, such as ARDS in COVID-19 among patients with rheumatic disease.

Objectives:

We aimed to develop a prediction model for ARDS in people with COVID-19 and pre-existing rheumatic disease using a series of machine learning algorithms and to identify risk factors associated with ARDS in this population.

Methods:

We used data from the COVID-19 Global Rheumatology Alliance (GRA) Registry from March 24 to Nov 1, 2020. ARDS diagnosis was indicated by the reporting clinician. Five machine learning algorithms optimized for rare events predicted ARDS using 42 variables covering patient demographics, rheumatic disease diagnoses, medications used at the time of COVID-19 diagnosis, and comorbidities. Model performance was assessed using accuracy, area under curve, sensitivity, specificity, positive predictive value, and negative predictive value. Adjusted odds ratios corresponding to the 10 most influential predictors from the best performing model were derived using hierarchical multivariate mixed-effects logistic regression that accounted for within-country correlations.

Results:

A total of 5,931 COVID-19 cases from 67 countries were included in the analysis. Mean (SD) age was 54.9 (16.0) years, 4,152 (70.0%) were female, and 2,399 (40.5%) were hospitalized. ARDS was reported in 388 (6.5% of total and 15.6% of hospitalized) cases. Statistically significant differences in the risk of ARDS were observed by demographics, diagnoses, medications, and comorbidities using unadjusted univariate comparisons (data not shown). Gradient boosting machine (GBM) had the highest sensitivity (0.81) and was considered the best performing model (Table 1). Hypertension, interstitial lung disease, kidney disease, diabetes, older age, glucocorticoids, and anti-CD20 monoclonal antibodies were associated with the development of ARDS while tumor necrosis factor inhibitors were associated with a protective effect (Figure 1).

Table 1.

Performance of machine learning algorithms.

GBMSVMGLMNETNNETRFAccuracy0.790.680.660.660.67AUC0.750.700.740.580.74Sensitivity0.810.680.650.680.67Specificity0.490.600.730.480.68PPV0.960.960.970.950.97NPV0.160.120.130.090.13

GBM: Gradient Boosting Machine, SVM: Support vector machines, GLMNET: Lasso and Elastic-Net Regularized Generalized Linear Models, NNET: Neural Networks, RF: Random Forest. AUC: Area Under Curve; PPV: Positive Predictive Value; NPV: Negative Predictive Value.

Conclusion:

In this global cohort of patients with rheumatic disease, a machine learning model, GBM, predicted the onset of ARDS with 81% sensitivity using baseline information obtained at the time of COVID-19 diagnosis. These results identify patients who may be at higher risk of severe COVID-19 outcomes. Further studies are necessary to validate the proposed prediction model in external cohorts and to evaluate its clinical utility. Disclaimer: The views expressed here are those of the authors and participating members of the COVID-19 Global Rheumatology Alliance, and do not necessarily represent the views of the ACR, NIH, (UK) NHS, NIHR, or the department of Health.

References:

[1]Tzotzos SJ, Fischer B, Fischer H, Zeitlinger M. 2020;24(1):516.

[2]Wu C, Chen X, Cai Y, et al. JAMA Intern Med. 2020;180(7):934-943.

Acknowledgements:

The COVID-19 Global Rheumatology Alliance.

Disclosure of Interests:

Zara Izadi: None declared, Milena Gianfrancesco: None declared, Kimme Hyrich Speakers bureau: Abbvie and grant income from BMS, UCB, and Pfizer, all unrelated to this study., Anja Strangfeld Speakers bureau: AbbVie, MSD, Roche, BMS, Pfizer, outside the submitted work., Grant/research support from: A consortium of 13 companies (among them AbbVie, BMS, Celltrion, Fresenius Kabi, Lilly, Mylan, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis, and UCB) supporting the German RABBIT register., Laure Gossec Consultant of: Abbvie, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sanofi-Aventis, UCB., Grant/research support from: Lilly, Mylan, Pfizer, all unrelated to this study., Loreto Carmona Consultant of: Loreto Carmona’s institute works by contract for laboratories among other institutions, such as Abbvie Spain, Eisai, Gebro Pharma, Merck Sharp & Dohme España, S.A., Novartis, Farmaceutica, Pfizer, Roche Farma, Sanofi Aventis, Astellas Pharma, Actelion Pharmaceuticals España, Grünenthal GmbH, and UCB Pharma., Elsa Mateus Grant/research support from: LPCDR received grants from Abbvie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern, GAfPA and Pfizer., Saskia Lawson-Tovey: None declared, Laura Trupin: None declared, Stephanie Rush: None declared, Gabriela Schmajuk: None declared, Lindsay Jacobsohn: None declared, Patti Katz: None declared, Samar Al Emadi: None declared, Leanna Wise: None declared, Emily Gilbert: None declared, Maria Valenzuela-Almada: None declared, Ali Duarte-Garcia: None declared, Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Gilead, Inova, Janssen, and Optum unrelated to this work., Grant/research support from: Amgen and Bristol-Myers Squibb., Tiffany Hsu: None declared, Kristin D’Silva: None declared, Naomi Serling-Boyd: None declared, Suleman Bhana Employee of: Suleman Bhana reports non-branded marketing campaigns for Novartis (<$10,000)., Wendy Costello: None declared, Rebecca Grainger Speakers bureau: Abbvie, Janssen, Novartis, Pfizer, Cornerstones and travel assistance from Pfizer (all < $10,000)., Jonathan Hausmann Consultant of: Novartis, unrelated to this work (<$10,000)., Jean Liew Grant/research support from: Pfizer, outside the submitted work., Emily Sirotich Grant/research support from: Emily Sirotich is a Board Member of the Canadian Arthritis Patient Alliance, a patient run, volunteer-based organization whose activities are largely supported by independent grants from pharmaceutical companies., Paul Sufka: None declared, Zachary Wallace Consultant of: Viela Bio and MedPace, outside the submitted work., Grant/research support from: Bristol-Myers Squibb and Principia/Sanofi., Pedro Machado Speakers bureau: Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all < $10,000)., Philip Robinson Consultant of: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer and UCB and travel assistance from Roche (all < $10,000)., Jinoos Yazdany Consultant of: Eli Lilly and Astra Zeneca, unrelated to this project.

POS0093 HETEROGENEITY IN ADVERSE EVENT ASSESSMENT BETWEEN COUNTRIES PARTICIPATING IN AN INTERNATIONAL COLLABORATION OF REGISTRIES OF RHEUMATOID ARTHRITIS PATIENTS USING JANUS KINASE INHIBITORS (THE JAK-POT STUDY)

Background:

Industry, regulators, and the rheumatology community have recognized the need for observational studies to monitor the safety of new antirheumatic agents. Registries provide a unique opportunity to understand the safety of newer therapies, but pharmacovigilance studies require large number of patients to evaluate rare drug-related adverse-events (AEs). Because JAK-inhibitors (JAKi) have only recently been approved for the treatment of rheumatoid arthritis, it makes sense to combine data from several registries in order to obtain a sufficiently large sample size to promote earlier detection of adverse events.

Objectives:

The purpose of this analysis was to evaluate how AEs are assessed in the various registries in preparation for a collaborative pharmacovigilance analysis, and present preliminary results.

Methods:

The “JAK-pot” collaboration includes 19 RA registries. The principal investigators of the participating registries were sent a structured questionnaire on AE assessment and 18 (94%) provided complete responses on the AE assessment procedures of their registries. We present simple descriptive statistics of the AE assessment procedures employed by the participating registries.

Results:

The 19 registries represent 7186 patients initiating a JAKi (Table 1), who are on average 57 years old, with a mean disease duration 11 years, seropositive (83%), female (82%) and with moderate disease activity at treatment initiation.

Table 1.Country, registryN° of patients on JAKi includedAustria, BIOREG87Belgium, TARDIS2113Canada, RHUMADATA363Czech Republic, ATTRA197Denmark, DANBIO506Finland, ROB-FIN229Germany, RABBIT620Italy, GISEA244Israel, I-RECORD96Netherlands, METEOR4Norway, NOR-DMARD97Portugal, REUMA.PT44Romania, RRBR252Russia, ARBITER428Slovenia, biorx.si141Spain, BIOBADASER139Switzerland, SCQM738Turkey, TURKBIO404UK, BSRBR484

After ineffectiveness, AEs was the second most common reason for JAKi discontinuation (25.5%), with large differences between registries (Figure 1).

Of the participating registries, 2 registries do not collect AEs, while 16 (89%) assess incident AEs, by means of a pre-specified extraction form (3 registries), by free text (5 registries), by a combination of both (6 registries) and/or the use of linkage to external electronic records (3registries). AEs are coded using a predefined coding system by 11 registries (MeDRA (8), other (3)), but nearly all are recording the severity of the AE (15, 94%), AE related-death (15, 94%), or AE-related hospitalisation (15, 94%). AEs of special interest, such as serious infections (15, 94%), thromboembolic events (15, 94%), or shingles (9, 56%), are recorded by most registries. Incident AEs are linked by the treating physician to specific therapies in 11 registries (69%), while the other 5 registries extrapolate potential causal associations based on therapy start and stop dates. A pre-specified adjudication process for AEs is made only by 5 registries (31%).

Conclusion:

Substantial heterogeneity exists among registries regarding AE assessment within the JAK-pot collaboration. These differences must be taken into account when analysing the safety of JAKi across different countries in collaborative studies. For comparative analyses, stratified analyses by country are required to account for differential AE assessment and varying degrees of potential under-reporting.

Disclosure of Interests:

Kim Lauper: None declared, Denis Mongin: None declared, Sytske Anne Bergstra: None declared, Denis Choquette: None declared, Catalin Codreanu: None declared, Diederik De Cock: None declared, Lene Dreyer: None declared, Ori Elkayam: None declared, Kimme Hyrich: None declared, Florenzo Iannone: None declared, Nevsun Inanc: None declared, Eirik kristianslund: None declared, Tore K. Kvien: None declared, Burkhard Leeb: None declared, Galina Lukina: None declared, Dan Nordström: None declared, Karel Pavelka: None declared, Manuel Pombo-Suarez: None declared, Ziga Rotar: None declared, Maria Jose Santos: None declared, Anja Strangfeld: None declared, Delphine Courvoisier: None declared, Axel Finckh Speakers bureau: Eli-Lilly, Pfizer, Consultant of: Eli-Lilly, Pfizer, Grant/research support from: BMS, Pfizer.

POS1183 OUTCOMES OF COVID-19 INFECTION AMONG CHILDREN AND YOUNG PEOPLE WITH PRE-EXISTING RHEUMATIC AND MUSCULOSKELETAL DISEASES

Background:

It remains unknown whether children and young people with rheumatic and musculoskeletal diseases (RMD) who acquire COVID-19 infection have a more severe COVID-19 course, due to either underlying disease or immunosuppressive treatments.

Objectives:

To describe outcomes among children and young people with underlying RMD who acquire COVID-19 infection.

Methods:

All children and young people <18 years of age with COVID-19 (presumptive or confirmed) reported to the EULAR COVID-19 Database, which collects details regarding RMD diagnosis and treatment, COVID infection and outcomes, between 27 March 2020 and 29 January 2021 (cutoff date for this analysis) were included. Patient characteristics and COVID-19 outcomes are presented.

Results:

A total of 151 children and young people (age range 2-17 years; Table 1) have been reported to the database from 12 countries; mostly Spain (N=30), France (N=29), Israel (N=29), and Czechia (N=25). Most patients had a diagnosis of juvenile idiopathic arthritis (JIA; N=92; 61%). Other diagnoses were autoinflammatory syndrome (including TRAPS, CAPS, FMF; 12%), and systemic lupus erythematosus (4%). There were 14 (9%) hospitalisations and 1 (0.7%) death reported due to COVID-19. The most commonly reported symptoms were fever (46%), cough (34%), anosmia (19%), and headache (19%). Only 19 (13%) patients reported glucocorticoid use. DMARD therapy was used by 104 (69%) patients; 67 (44%) were on csDMARDs (methotrexate [N=54], antimalarials [N=7]), 45 (30%) on anti-TNF, 9 (6%) on IL-6 inhibitors, and 7 (5%) on IL-1 inhibitors. Among the 145 patients with hospitalisation data, patients on any DMARD therapy (cs/b/tsDMARDs) had similar odds for hospitalisation compared with those not on therapy, adjusted for age (odds ratio 0.7; 95% CI 0.2, 2.4).

All PatientsN151GenderFemale94 (62%)Male56 (37%)Unknown1 (<1%)Age, yearsMedian (IQR)12 (8, 15)Range2 to 17Top Rheumatology DiagnosesJuvenile Idiopathic Arthritis (JIA)92 (61%)Polyarthritis50 (33%)Oligoarthritis31 (21%)Systemic11 (7%)Autoinflammatory syndrome (e.g.18 (12%)TRAPS, CAPS, FMF)6 (4%)Systemic Lupus ErythematosusComorbiditiesNone stated112 (74%)Obesity9 (6%)Ocular inflammationAsthma9 (6%)3 (2%)Required HospitalisationYes14 (9%)No131 (87%)Missing6 (4%)Top 5 Symptoms ReportedFever69 (46%)Cough51 (34%)Anosmia28 (19%)Headache28 (19%)Fatigue23 (15%)Deaths due to COVID-19Yes1 (<1%)Treatment at onset of COVID-19 infectionGlucocorticoids19 (13%)csDMARDs67 (44%)Methotrexate54 (36%)Antimalarials7 (5%)Mycophenolate5 (3%)bDMARDs64 (42%)Anti-TNF45 (30%)IL-69 (6%)IL-18 (5%)Any DMARD104 (69%)

Conclusion:

These initial data on outcomes of COVID-19 in paediatric RMDs are very reassuring, with less than 1 in 10 patients reporting hospitalisation. Due to the database design and inherent reporting bias, this is likely an overestimate, suggesting that overall outcomes among this population appear to be generally good, with mild infection. Increasing case reports to the database will allow further exploration of drug- and disease-specific outcomes.

Disclosure of Interests:

None declared.

OP0105 FEASIBILITY AND USEFULNESS OF MAPPING BIOLOGIC REGISTRIES TO A COMMON DATA MODEL: ILLUSTRATION USING COMORBIDITIES

Background:

The Observational and Medical Outcomes Partnerships (OMOP) common data model (CDM) provides a framework for standardising health data with a view towards federated analyses, thus maximising the use and power of combining disparate datasets.

Objectives:

To assess feasibility and usefulness of mapping biologic registry data from different European countries to the OMOP CDM and present initial descriptive data regarding comorbidities.

Methods:

Five biologic registries, as part of a funded FOREUM project, have been mapped to the OMOP CDM: 1) the Czech biologics register (ATTRA), 2) Registro Español de Acontecimientos Adversos de Terapias Biológicas en Enfermedades Reumáticas (BIOBADASER), 3) British Society for Rheumatology Biologics Register for Rheumatoid Arthritis (BSRBR-RA), 4) German biologics register ‘Rheumatoid arthritis observation of biologic therapy’ (RABBIT), and 5) Swiss register ‘Swiss Clinical Quality Management in Rheumatic Diseases’ (SCQM). The mapping includes socio-demographic, observation period within the studies, baseline comorbidities, and baseline medications. Only patients with RA were included. Using R, registers received identical scripts to run on their mapped databases to produce an initial description of patient characteristics without the need to share patient-level data.

Results:

A total of 54,458 individuals are included the five registries being mapped to the OMOP CDM, see table. Age and gender distribution was similar across registries. All registers reported on cardiovascular system comorbidities, diabetes mellitus, mental disorders, and respiratory system comorbidities. However, it was noted that results of comorbidity mapping relies on what each register collect on each patient at the point of registration.

Whilst the Charlson comorbidity index could be calculated within each registry, due to lack of the specific coding needed, such as “uncomplicated diabetes mellitus” / “end-organ damage diabetes mellitus”, it was felt to be an inaccurate measure. The granularity of the comorbidities was insufficient, as many registers coded, for example, diabetes mellitus without any extra information.

Table 1.

OARSI scores

RegistryATTRABIOBADASERBSRBR-RARABBITSCQMCountryCzechiaSpainUnited KingdomGermanySwitzerlandNumber of Participants23343012251791365210281Gender FemaleMale1808 (77%)526 (23%)2372 (79%)640 (21%)18995 (75%)6184 (25%)10191 (75%)3461 (25%)7584 (74%)2697 (26%)Age at observation start date59 (52, 66)56 (47, 63)58 (49, 66)58 (50, 67)57 (47, 66)First observation start dateFeb-2002Oct-1999Oct-2001Aug-2006March-1995Number of comorbidities1 (1, 2)1 (0, 2)1 (0, 2)2 (1, 3)2 (1, 4)Disorder of cardiovascular system1609 (69%)208 (7%)2239 (9%)6330 (46%)3969 (39%)Diabetes mellitus331 (14%)273 (9%)1770 (7%)1591 (12%)792 (8%)Depressive Disorder165 (7%)04971 (20%)1023 (7%)1337 (13%)Disorder of respiratory system215 (9%)209 (7%)4125 (16%)1282 (9%)1630 (16%)

Conclusion:

This is the first analysis of data from the newly mapped OMOP CDM across five European registers. Through mapping the registers into a CDM, and using the same script, the ability to undertake collaborative analysis without sharing patient level data outside of the country can be realised. Due to differences in study design and data capture, there needs to be a focus on harmonising the coding and analysing of the comorbidities and drugs across registries.

Disclosure of Interests:

Lianne Kearsley-Fleet: None declared, Kimme Hyrich: None declared, Martin Schaefer: None declared, Doreen Huschek: None declared, Anja Strangfeld: None declared, Jakub Zavada Speakers bureau: Abbvie, Eli-Lilly, UCB, Sanofi., Consultant of: Abbvie, UCB, Sanofi, Gilead., Markéta Lagová: None declared, Delphine Courvoisier Speakers bureau: Medtalks Switzerland, Christoph Tellenbach: None declared, Kim Lauper Speakers bureau: Medtalks Switzerland, Carlos Sánchez-Piedra: None declared, Nuria Montero: None declared, Jesús-Tomás Sánchez-Costa: None declared, Daniel Prieto-Alhambra Consultant of: Amgen (speaker fees and advisory board membership fees paid to DPA’s department) and UCB (consultancy fees paid to DPA’s department), Grant/research support from: grants and other from AMGEN, grants, non-financial support and other from UCB Biopharma, grants from Les Laboratoires Servier, outside the submitted work., Edward Burn: None declared

OP0006 ASSOCIATIONS OF BASELINE USE OF BIOLOGIC OR TARGETED SYNTHETIC DMARDS WITH COVID-19 SEVERITY IN RHEUMATOID ARTHRITIS: RESULTS FROM THE COVID-19 GLOBAL RHEUMATOLOGY ALLIANCE

Background:

Targeted DMARDs may dampen the inflammatory response in COVID-19, perhaps leading to a less severe clinical course. However, some DMARD targets may impair viral immune defenses. Due to sample size limitations, previous studies of DMARD use and COVID-19 outcomes have combined several heterogeneous rheumatic diseases and medications, investigating a single outcome (e.g., hospitalization).

Objectives:

To investigate the associations of baseline use of biologic or targeted synthetic (b/ts) DMARDs with a range of poor COVID-19 outcomes in rheumatoid arthritis (RA).

Methods:

We analyzed voluntarily reported cases of COVID-19 in patients with rheumatic diseases in the COVID-19 Global Rheumatology Alliance physician registry (March 12, 2020 - January 6, 2021). We investigated RA treated with b/tsDMARD at the clinical onset of COVID-19 (baseline): abatacept (ABA), rituximab (RTX), Janus kinase inhibitors (JAK), interleukin-6 inhibitors (IL6i), or tumor necrosis factor inhibitors (TNFi). The outcome was an ordinal scale (1-4) for COVID-19 severity: 1) no hospitalization, 2) hospitalization without oxygen need, 3) hospitalization with any oxygen need or ventilation, or 4) death. Baseline covariates including age, sex, smoking, obesity, comorbidities (e.g., cardiovascular disease, cancer, interstitial lung disease [ILD]), concomitant non-biologic DMARD use, glucocorticoid use/dose, RA disease activity, country, and calendar time were used to estimate propensity scores (PS) for b/tsDMARD. The primary analysis used PS matching to compare each drug class to TNFi. Ordinal logistic regression estimated ORs for the COVID-19 severity outcome. In a sensitivity analysis, we used traditional multivariable ordinal logistic regression adjusting for covariates without matching.

Results:

Of the 1,673 patients with RA on b/tsDMARDs at the onset of COVID-19, (mean age 56.7 years, 79.6% female) there were n=154 on ABA, n=224 on RTX, n=306 on JAK, n=180 on IL6i, and n=809 on TNFi. Overall, 498 (34.3%) were hospitalized and 112 (6.7%) died. Among all patients, 353 (25.3%) were ever smokers, 197 (11.8%) were obese, 462 (27.6%) were on glucocorticoids, 1,002 (59.8%) were on concomitant DMARDs, and 299 (21.7%) had moderate/high RA disease activity. RTX users were more likely than TNFi users to have ILD (11.6% vs. 1.7%) and history of cancer (7.1% vs. 2.0%); JAK users were more likely than TNFi users to be obese (17.3% vs. 9.0%). After propensity score matching, RTX was strongly associated with greater odds of having a worse outcome compared to TNFi (OR 3.80, 95% CI 2.47, 5.85; Figure). Among RTX users, 42 (18.8%) died compared to 27 (3.3%) of TNFi users (Table). JAK use was also associated with greater odds of having a worse COVID-19 severity (OR 1.52, 95%CI 1.02, 2.28). ABA or IL6i use were not associated with COVID-19 severity compared to TNFi. Results were similar in the sensitivity analysis and after excluding cancer or ILD.

Table 1.

Frequencies for the ordinal COVID-19 severity outcome for patients with RA on biologic or targeted synthetic DMARDs (n=1673).

COVID-19 outcomes by severity scale (n,%)ABAn=154RTXn=224JAKn=306IL6in=180TNFi n=8091)Not hospitalized113 (73.3%)121 (54.0%)220 (71.9%)150 (83.3%)666 (82.3%)2)Hospitalization without oxygenation10 (6.5%)14 (6.2%)11 (3.6%)9 (5.0%)53 (6.5%)3)Hospitalization with any oxygenation or ventilation16 (10.4%)47 (21.0%)52 (17.0%)16 (8.9%)63 (7.8%)4)Death15 (9.7%)42 (18.8%)23 (7.5%)5 (2.8%)27 (3.3%)

Conclusion:

In this large global registry of patients with RA and COVID-19, baseline use of RTX or JAK was associated with worse severity of COVID-19 compared to TNFi use. The very elevated odds for poor COVID-19 outcomes in RTX users highlights the urgent need for risk-mitigation strategies, such as the optimal timing of vaccination. The novel association of JAK with poor COVID-19 outcomes requires replication.

Acknowledgements:

The views expressed here are those of the authors and participating members of the COVID-19 Global Rheumatology Alliance and do not necessarily represent the views of the ACR, EULAR, the UK National Health Service, the National Institute for Health Research, the UK Department of Health, or any other organization.

Disclosure of Interests:

Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Gilead, Inova, Janssen, and Optum, unrelated to this work, Grant/research support from: Amgen and Bristol-Myers Squibb, unrelated to this work, Zachary Wallace Consultant of: Viela Bio and MedPace, outside the submitted work., Grant/research support from: Bristol-Myers Squibb and Principia/Sanofi, Andrea Seet: None declared, Milena Gianfrancesco: None declared, Zara Izadi: None declared, Kimme Hyrich Speakers bureau: Abbvie unrelated to this study, Grant/research support from: BMS, UCB, and Pfizer, all unrelated to this study, Anja Strangfeld Paid instructor for: AbbVie, MSD, Roche, BMS, Pfizer, outside the submitted work, Grant/research support from: grants from a consortium of 13 companies (among them AbbVie, BMS, Celltrion, Fresenius Kabi, Lilly, Mylan, Hexal, MSD, Pfizer, Roche, Samsung, Sanofi-Aventis, and UCB) supporting the German RABBIT register, outside the submitted work, Laure Gossec Consultant of: Abbvie, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sanofi-Aventis, UCB, unrelated to this study, Grant/research support from: Lilly, Mylan, Pfizer, all unrelated to this study, Loreto Carmona: None declared, Elsa Mateus Grant/research support from: grants from Abbvie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern, GAfPA; grants and non-financial support from Pfizer, outside the submitted work, Saskia Lawson-Tovey: None declared, Laura Trupin: None declared, Stephanie Rush: None declared, Gabriela Schmajuk: None declared, Patti Katz: None declared, Lindsay Jacobsohn: None declared, Samar Al Emadi: None declared, Leanna Wise: None declared, Emily Gilbert: None declared, Ali Duarte-Garcia: None declared, Maria Valenzuela-Almada: None declared, Tiffany Hsu: None declared, Kristin D’Silva: None declared, Naomi Serling-Boyd: None declared, Philippe Dieudé Consultant of: Boerhinger Ingelheim, Bristol-Myers Squibb, Lilly, Sanofi, Pfizer, Chugai, Roche, Janssen unrelated to this work, Grant/research support from: Bristol-Myers Squibb, Chugaii, Pfizer, unrelated to this work, Elena Nikiphorou: None declared, Vanessa Kronzer: None declared, Namrata Singh: None declared, Manuel F. Ugarte-Gil Grant/research support from: Janssen and Pfizer, Beth Wallace: None declared, Akpabio Akpabio: None declared, Ranjeny Thomas: None declared, Suleman Bhana Consultant of: AbbVie, Horizon, Novartis, and Pfizer (all <$10,000) unrelated to this work, Wendy Costello: None declared, Rebecca Grainger Speakers bureau: Abbvie, Janssen, Novartis, Pfizer, Cornerstones, Jonathan Hausmann Consultant of: Novartis, Sobi, Biogen, all unrelated to this work (<$10,000), Jean Liew Grant/research support from: Yes, I have received research funding from Pfizer outside the submitted work., Emily Sirotich Grant/research support from: Board Member of the Canadian Arthritis Patient Alliance, a patient run, volunteer based organization whose activities are largely supported by independent grants from pharmaceutical companies, Paul Sufka: None declared, Philip Robinson Speakers bureau: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer and UCB (all < $10,000), Consultant of: Abbvie, Eli Lilly, Janssen, Novartis, Pfizer and UCB (all < $10,000), Pedro Machado Speakers bureau: Yes, I have received consulting/speaker’s fees from Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all < $10,000)., Consultant of: Yes, I have received consulting/speaker’s fees from Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche and UCB, all unrelated to this study (all < $10,000)., Jinoos Yazdany Consultant of: Eli Lilly and AstraZeneca unrelated to this project

LB0002 COVID-19 VACCINE SAFETY IN PATIENTS WITH RHEUMATIC AND MUSCULOSKELETAL DISEASE

Background:

The consequences of the COVID-19 outbreak are unprecedented and have been felt by everyone around the world, including people with rheumatic and musculoskeletal diseases (RMDs). With the development of vaccines, the future is becoming brighter. Vaccines are a key pillar of public health and have been proven to prevent many serious diseases. However, vaccination also raises questions, especially for patients with inflammatory RMDs and/or treated with drugs that influence their immune system.

Objectives:

Our aim was to collect safety data among RMD patients receiving COVID-19 vaccines.

Methods:

The EULAR COVID-19 Vaccination (COVAX) Registry is an observational registry launched on 5 February 2021. Data are entered voluntarily by clinicians or associated healthcare staff; patients are eligible for inclusion if they have an RMD and have been vaccinated against SARS-CoV-2. Descriptive statistics are presented.

Results:

As of 27 April 2021, 1519 patients were reported to the registry. The majority were female (68%) and above the age of 60 (57%). Mean age was 63 years (SD 16), ranging from 15 to 97 years. A total of 28 countries contributed to the registry, with France (60%) and Italy (13%) as the highest contributors. The majority (91%) had inflammatory RMDs. Inflammatory joint diseases accounted for 51% of cases, connective tissue diseases 19%, vasculitis 16%, other immune mediated inflammatory diseases 4%, and non-inflammatory/mechanical RMDs 9%. The most frequent individual diagnoses were rheumatoid arthritis (30%), axial spondyloarthritis (8%), psoriatic arthritis (8%), systemic lupus erythematosus (SLE, 7%) and polymyalgia rheumatica (6%). At the time of vaccination, 45% were taking conventional synthetic DMARDs, 36% biological DMARDs, 31% systemic glucocorticoids, 6% other immunosuppressants (azathioprine; mycophenolate; cyclosporine; cyclophosphamide; tacrolimus), and 3% targeted synthetic DMARDs. The most frequent individual DMARDs were methotrexate (29%), TNF-inhibitors (18%), antimalarials (10%) and rituximab (6%). The vaccines administered were: 78% Pfizer, 16% AstraZeneca, 5% Moderna and 1% other/unknown; 66% of cases received two doses and 34% one dose. Mean time from 1st and 2nd dose to case report was 41 days (SD 26) and 26 days (SD 23), respectively. COVID-19 diagnosis after vaccination was reported in 1% (18/1519) of cases. Mean time from first vaccination until COVID-19 diagnosis was 24 days (SD 17). Disease flares were reported by 5% (73/1375) of patients with inflammatory RMDs, with 1.2% (17/1375) classified as severe flares. Mean time from closest vaccination date to inflammatory RMD flare was 5 days (SD 5). The most common flare types were arthritis (35/1375=2.5%), arthralgia (29/1375=2.1%), cutaneous flare (11/1375=0.8%) and increase in fatigue (11/1375=0.8%). Potential vaccine side effects were reported by 31% of patients (467/1519). The majority were typical early adverse events within 7 days of vaccination, namely pain at the site of injection (281/1519=19%), fatigue (171/1519=11%) and headache (103/1519=7%). Organ/system adverse events were reported by 2% (33/1519) but only 0.1% (2/1519) reported severe adverse events, namely a case of hemiparesis in a patient with systemic sclerosis/SLE overlap syndrome (ongoing at the time of reporting), and a case of giant cell arteritis in a patient with osteoarthritis (recovered/resolved without sequelae).

Conclusion:

The safety profiles for COVID-19 vaccines in RMD patients was reassuring. Most adverse events were the same as in the general population, they were non-serious and involved short term local and systemic symptoms. The overwhelming majority of patients tolerated their vaccination well with rare reports of inflammatory RMD flare (5%; 1.2% severe) and very rare reports of severe adverse events (0.1%). These initial findings should provide reassurance to rheumatologists and vaccine recipients, and promote confidence in COVID-19 vaccine safety in RMD patients, namely those with inflammatory RMDs and/or taking treatments that influence their immune system.

Acknowledgements:

EULAR COVID-19 Task Force; European Reference Network on rare and Complex Connective Tissue and Musculoskeletal Diseases; European Reference Network on Rare Immunodeficiency, Autoinflammatory and Autoimmune Diseases Network; all rheumatologists contributing to the EULAR COVAX Registry.

Disclosure of Interests:

Pedro M Machado Consultant of: Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this manuscript., Grant/research support from: Orphazyme, unrelated to this manuscript., Speakers bureau: Abbvie, BMS, Celgene, Eli Lilly, Janssen, MSD, Novartis, Orphazyme, Pfizer, Roche and UCB, all unrelated to this manuscript., Saskia Lawson-Tovey: None declared, Kimme Hyrich Grant/research support from: BMS, UCB, and Pfizer, all unrelated to this manuscript., Speakers bureau: Abbvie, Loreto Carmona Consultant of: her institute works by contract for laboratories among other institutions, such as Abbvie Spain, Eisai, Gebro Pharma, Merck Sharp & Dohme España, S.A., Novartis Farmaceutica, Pfizer, Roche Farma, Sanofi Aventis, Astellas Pharma, Actelion Pharmaceuticals España, Grünenthal GmbH, and UCB Pharma, all unrelated to this manuscript., Laure Gossec Grant/research support from: AbbVie, Amgen, BMS, Biogen, Celgene, Gilead, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis, UCB, all unrelated to this manuscript., Speakers bureau: Amgen, Lilly, Janssen, Pfizer, Sandoz, Sanofi, Galapagos, all unrelated to this manuscript., Elsa Mateus Grant/research support from: LPCDR received support for specific activities: grants from Abbvie, Novartis, Janssen-Cilag, Lilly Portugal, Sanofi, Grünenthal S.A., MSD, Celgene, Medac, Pharmakern, GAfPA; grants and non-financial support from Pfizer; non-financial support from Grünenthal GmbH, outside the submitted work., Anja Strangfeld Speakers bureau: AbbVie, MSD, Roche, BMS, and Pfizer, all unrelated with this manuscript., BERND RAFFEINER: None declared, Tiphaine Goulenok: None declared, Olilvier Brocq: None declared, Martina Cornalba: None declared, José A Gómez-Puerta Speakers bureau: AbbVie, BMS, GSK, Janssen, Lilly, MSD, Roche and Sanofi., Eric Veillard: None declared, Ludovic Trefond: None declared, Jacques-Eric Gottenberg: None declared, Julien Henry: None declared, Patrick Durez: None declared, Gerd Rüdiger Burmester: None declared, Marta Mosca: None declared, Eric Hachulla: None declared, Hans Bijlsma: None declared, Iain McInnes: None declared, Xavier Mariette Consultant of: BMS, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sanofi-Aventis, UCB, and grant from Ose, all unrelated to this manuscript.

OP0231 COMPARATIVE EFFECTIVENESS OF JAK-INHIBITORS, TNF-INHIBITORS, ABATACEPT AND IL-6 INHIBITORS IN AN INTERNATIONAL COLLABORATION OF REGISTERS OF RHEUMATOID ARTHRITIS PATIENTS (THE “JAK-POT” STUDY)

Background:

In many countries, JAK-inhibitors (JAKi) have only recently been approved as treatment for patients with rheumatoid arthritis (RA).

Objectives:

To evaluate the effectiveness of JAKi compared to bDMARDs in RA patients in the real-world population in an international collaboration of registers (the “JAK-pot” collaboration).

Methods:

Patients initiating either JAKi, TNFi, IL-6i or abatacept (ABA) during a time period when JAKi were available in each country (19 registers, Table) were included. We compared the effectiveness of JAKi and bDMARDs in terms of retention using crude and adjusted survival analysis. Missing covariates were imputed using multiple imputation.

Results:

Among 25521 included patients, 6063 initiated a JAKi, 13879 a TNFi, 2348 ABA, and 3231 an IL-6i. Patients were on average 55 years old, with a mean disease duration 10 years, mostly seropositive (67%), female (77%) and with moderate disease activity at treatment initiation. The main reason of stopping treatment was ineffectiveness (49%), followed by adverse events (21%). Patients on JAKi were treated more often as monotherapy, had higher CRP and disease activity at baseline and had experienced more previous ts/bDMARDs. Crude median retention was 1.4 (95% CI 1.2-1.5) years for JAKi, 1.6 (1.6-1.7) for TNFi, 1.5 (1.3-1.7) for IL6i and 1.1 (1.0-1.3) for ABA. After adjustment, the hazard ratio (HR) for discontinuation tended to be lower for JAKi (HR 0.86 (0.65-1.13)) compared to TNFi, but comparable for ABA (1.02 (0.94-1.10)) and IL6i (0.99 (0.88-1.10)) (Figure 1). HRs differed notably between countries (Figure 2).

Table 1.

Registers

Country, registerNJAKi, n (%)Austria, BIOREG*Belgium, TARDIS62882113 (33.6)Canada, RHUMADATA528114 (21.6)Czech Republic, ATTRA374253 (67.6)Denmark, DANBIO4721506 (10.7)Finland, ROB-FIN807234 (29.0)Germany, RABBIT*Italy, GISEA757250 (33.0)Israel, I-RECORD40094 (23.5)Netherlands, METEOR16424 (0.2)Norway, NOR-DMARD50799 (19.5)Portugal, REUMA.PT79744 (5.5)Romania, RRBR593328 (55.3)Russia, ARBITER526483 (91.8)Slovenia, BIORX.SI583146 (25.0)Spain, BIOBADASER781139 (17.8)Switzerland, SCQM2956796 (26.9)Turkey, TURKBIO2150397 (18.5)UK, BSRBR111163 (5.7)

*Registers planning to participate in future studies but not included yet

Conclusion:

The adjusted overall drug retention of JAKi tended to be higher than for TNFi, with large variation between countries. Other measures of effectiveness, such as the evaluation of CDAI remission and low disease activity are planned to shape a more comprehensive picture of JAKi effectiveness in the real world.

Disclosure of Interests:

Kim Lauper: None declared, Denis Mongin: None declared, Sytske Anne Bergstra: None declared, Denis Choquette Grant/research support from: Rhumadata is supported by grants from Pfizer, Amgen, Abbvie, Gylead, BMS, Novartis, Sandoz, eli Lilly,, Consultant of: Pfizer, Amgen, Abbvie, Gylead, BMS, Novartis, Sandoz, eli Lilly,, Speakers bureau: Pfizer, Amgen, Abbvie, Gylead, BMS, Novartis, Sandoz, eli Lilly,, Catalin Codreanu Consultant of: Speaker and consulting fees from AbbVie, Accord Healthcare, Alfasigma, Egis, Eli Lilly, Ewopharma, Genesis, Mylan, Novartis, Pfizer, Roche, Sandoz, UCB, Speakers bureau: Speaker and consulting fees from AbbVie, Accord Healthcare, Alfasigma, Egis, Eli Lilly, Ewopharma, Genesis, Mylan, Novartis, Pfizer, Roche, Sandoz, UCB, Diederik De Cock: None declared, Lene Dreyer: None declared, Ori Elkayam Speakers bureau: AbbVie, BMS, Pfizer, Roche, Sanofi-Aventis, Novartis, Jansen, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Florenzo Iannone Consultant of: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Speakers bureau: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Nevsun Inanc: None declared, Eirik kristianslund: None declared, Tore K. Kvien Grant/research support from: Received grants from Abbvie, Hospira/Pfizer, MSD and Roche (not relevant for this abstract)., Consultant of: Have received personal fees from Abbvie, Biogen, BMS, Celltrion, Eli Lily, Hospira/Pfizer, MSD, Novartis, Orion Pharma, Roche, Sandoz, UCB, Sanofi and Mylan (not relevant for this abstract)., Paid instructor for: Have received personal fees from Abbvie, Biogen, BMS, Celltrion, Eli Lily, Hospira/Pfizer, MSD, Novartis, Orion Pharma, Roche, Sandoz, UCB, Sanofi and Mylan (not relevant for this abstract)., Speakers bureau: Have received personal fees from Abbvie, Biogen, BMS, Celltrion, Eli Lily, Hospira/Pfizer, MSD, Novartis, Orion Pharma, Roche, Sandoz, UCB, Sanofi and Mylan (not relevant for this abstract)., Burkhard Leeb Grant/research support from: chairman of BioReg, Consultant of: AbbVie, Pfizer, Roche, Lilly, Grünenthal, Gebro,, Paid instructor for: Lilly, Biogen, Speakers bureau: Biogen, Lilly, Pfizer, Grünenthal, Astropharma,, Galina Lukina Speakers bureau: Novartis, Pfizer, UCB, Abbvie, Biocad, MSD, Roche, Dan Nordström Consultant of: Abbvie, Celgene, Lilly, Novartis, Pfizer, Roche and UCB., Speakers bureau: Abbvie, Celgene, Lilly, Novartis, Pfizer, Roche and UCB., Karel Pavelka Consultant of: Abbvie, MSD, BMS, Egis, Roche, UCB, Medac, Pfizer, Biogen, Speakers bureau: Abbvie, MSD, BMS, Egis, Roche, UCB, Medac, Pfizer, Biogen, Manuel Pombo-Suarez Consultant of: Janssen, Lilly, MSD and Sanofi., Speakers bureau: Janssen, Lilly, MSD and Sanofi., Ziga Rotar Consultant of: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Speakers bureau: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Maria Jose Santos Speakers bureau: Novartis and Pfizer, Anja Strangfeld Speakers bureau: AbbVie, BMS, Pfizer, Roche, Sanofi-Aventis, Delphine Courvoisier: None declared, Axel Finckh Grant/research support from: Pfizer: Unrestricted research grant, Eli-Lilly: Unrestricted research grant, Consultant of: Sanofi, AB2BIO, Abbvie, Pfizer, MSD, Speakers bureau: Sanofi, Pfizer, Roche, Thermo Fisher Scientific

SAT0506 MULTIPLE PATTERNS OF ‘RESPONSE’ TO METHOTHREXATE IDENTIFIED IN A NATIONAL JUVENILE IDIOPATHIC ARTHRITIS COHORT

Background:

Disease activity following treatment for JIA is currently understood in terms of ‘response’ or ‘non-response’. This state is usually defined using composite measures such as the ACR Pedi scores or cut-offs on the Juvenile Arthritis Disease Activity Scores (JADAS). However, response is a complex state and it is likely that separate, identifiable clusters of children and young people (CYP) have different, varying levels of response across the individual measures of JIA disease activity. Identifying these clusters may facilitate stratified medicine in JIA.

Objectives:

To identify clusters of CYP with distinct patterns of change across the individual JADAS components following MTX initiation for JIA.

Methods:

MTX-naïve CYP enrolled into the MTX cohorts of the British Society for Paediatric and Adolescent Rheumatology Etanercept Cohort Study or the UK Biologics for Children with Rheumatic Diseases registers before January 2018 were selected. JADAS components (active joint count to 71, physician global assessment (0-100mm), parent global evaluation (0-100mm) and ESR (mm/hr)) were collected at MTX start and at (approximately) 6- and 12-month follow-ups. Outcomes were Log1p transformed for analysis and all outcome data were censored following start of a biologic. CYP were excluded if they had clinically inactive disease at MTX initiation, initiated a biologic within a month of MTX or had no available JADAS data at any time point.

Multivariate group-based trajectory models explored MTX response clusters over the first year following MTX initiation using censored-normal models. Linear, quadratic and cubic polynomials were tested, with one to ten trajectories tested within each polynomial group. Optimal models within each polynomial group were selected using Bayesian Information Criteria, after excluding those with groups representing <1% of the cohort, average posterior probability for assigned group <70% or relative entropy <0.5.

Results:

Of 657 CYP, the majority were female (69%) and of white ethnicity (85%), with RF-negative polyarticular JIA the most common disease category (33%).

The optimal model identified multiple patterns of disease activity following MTX initiation, with greater complexity than the traditional ‘response’ or ‘non-response’ paradigm. Although there were no substantial differences in ESR trajectories between the groups, there were differences in initial disease severity and speeds of improvement across active joint counts, physician and parental global assessments over time. In addition, individual JADAS components did not always change in parallel over time, even within the same cluster of CYP.

Conclusion:

There are multiple patterns of disease activity following MTX initiation for CYP with JIA. This suggests that a simple response/non-response analysis at a single time point may be inadequate. Understanding clinical or biological factors associated with these clusters could facilitate stratified medicine in JIA.

Acknowledgments:

The CLUSTER consortium is supported by contributions of its industry partners, currently Pfizer, AbbVie UCB, GSK, and Sobi

Disclosure of Interests:

Stephanie Shoop-Worrall: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Lucy Wedderburn Speakers bureau: Pfizer, Wendy Thomson: None declared, Nophar Geifman: None declared

SAT0103 THE EFFECT OF BODYWEIGHT ON RESPONSE TO INTRAVENOUS OR SUBCUTANEOUS TOCILIZUMAB IN PATIENTS WITH RHEUMATOID ARTHRITIS

Background:

Tocilizumab is an IL-6 receptor humanised monoclonal antibody treatment option in rheumatoid arthritis (RA) who have not responded or are intolerant of disease modifying anti-rheumatic drugs (DMARDs) or other biologics. Tocilizumab was available initially as an intravenous (IV) preparation, dosed according to weight, and more recently as a subcutaneous (SC) preparation given at 162mg/weekly irrespective of bodyweight.

Obesity is highly prevalent in RA and there has been concern that starting or switching patients to SC tocilizumab could reduce its effectiveness in those patients with a higher body weight when compared to IV tocilizumab.

Objectives:

To investigate the relationship between bodyweight and DAS28 response at 6 months in tocilizumab naïve RA patients starting IV or SC tocilizumab.

Methods:

The study population comprised RA subjects recruited to the BSRBR-RA up to 30/11/2018 commencing IV or SC tocilizumab for the first time. Patients had to be tocilizumab naïve and have at least one six monthly study follow-up recorded after starting tocilizumab. Baseline characteristics at point of starting tocilizumab are described. Linear regression, fully adjusted for relevant confounders, was used to investigate the relationship between change in DAS28 score from baseline to six months and body weight per ten kilograms (kg), and in a separate analysis, as BMI category. Multiple imputation was used to handle missing data.

Results:

1241 patients starting tocilizumab (902 IV, 339 SC) were eligible for analysis. The median age was 59 years, majority were female, and had median disease duration of 11 years at baseline. Over seventy percent had prior biologic exposure. Median weight was 77kg for IV and 76kg for SC starters, and the majority of patients were categorised as normal weight (30% IV, 37% SC) or pre-obesity (31% IV & SC) according to BMI. Median DAS28 score was 5.8 (IV) and 5.5 (SC) at start of treatment with median improvement after 6-months of 1.50 and 2.02 units respectively. The fully adjusted linear regression model showed no association between body weight or BMI and change in DAS28 score at six months for patients starting IV or SC tocilizumab. (Table).

TableBaseline VariableIntravenous TCZ patients (n=902)Subcutaneous TCZ patients (n=339)Age, median (IQR)58 (50-67)60 (51-70)Gender, n (%) female708 (78)233 (74)Disease duration, median (IQR) years11 (4-21)11 (4-21)DAS28 score, median (IQR)5.8 (5.1-6.6)5.5 (4.7-6.5)Change in DAS28 score, median (IQR)-1.50 (-3.10 - -0.23)-2.02 (-3.72- -0.37)Weight in KGs, median (IQR)77 (64-91)76 (64-88)Change in DAS28, coefficient (95% CI)Body weight per 10kgs*0.04 (-0.01-0.09)-0.005 (-0.11-0.10)BMI category*Normal weightrefrefUnderweight-0.41 (-1.27-0.46)0.08 (-1.62-1.77)Pre-obesity-0.26 (-0.57-0.05)0.02 (-0.44-0.48)Obesity class I, II & III-0.03 (-0.35-0.29)0.08 (-0.40-0.55)

*Fully adjusted for age, gender, disease duration, baseline DAS28 score, baseline HAQ score, co-morbidities, and number of previous biologics

Conclusion:

Data from this study show that body weight does not appear to affect initial response to IV or SC tocilizumab. This is reassuring given that patients are likely to be given SC tocilizumab due to ease of administration and reduced hospital costs.

Disclosure of Interests:

Rebecca Davies: None declared, Arani Vivekanantham: None declared, Mark Lunt: None declared, Kath Watson: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, James Bluett: None declared

OP0197 THE INITIAL TREATMENT OF SYSTEMIC JUVENILE IDIOPATHIC ARTHRITIS: AN INTERNATIONAL COLLABORATION AMONG 10 REGISTRIES

Background:

The introduction of biologics has transformed care for children with systemic juvenile idiopathic arthritis (SJIA). Differences in treatment approaches between countries and how they have changed over time are not well studied.

Objectives:

We contrast the initial features, treatment and 12-month outcome in SJIA across 10 JIA registers in Europe and North America.

Methods:

Data were extracted locally from 10 Registers including manifestations at diagnosis, medication use over first year and outcomes (Physician Global Assessment (PGA), active joint count (AJC)) at 12 months. Data was compared before/after 2012 to assess change over time. Weighted (w) means were used to adjust for varying number of patients/Register.

Results:

1,149 patients; 553 had medication data for 2012-2018; primarily female and Caucasian; median age at diagnosis 5.3-8 years. Median duration of symptoms prior to first visit varied (0-3.3 months). Glucocorticoid (GC) use was common in the first year (w_average 72% (range 33-96%)). Biologic use included IL-1, IL-6 and TNF inhibitors. The proportion of patients treated with biologics, primarily anakinra, increased after 2012 (Table 1). W_mean PGA and AJC at the 12±3 month visit were 1.55 and 1.57, respectively (Table 2). At one year, the proportion of patients prescribed GC varied (w_mean 40%, range 26-60%).

Conclusion:

Analysis of SJIA patients across 10 countries show that time to first rheumatology visit was highly variable. Although local factors influence treatment decisions, biologic use increased after 2011; anakinra most common. Nearly 75% of patients were prescribed steroids within the first year but seemed to decrease after 1 year. More study is needed on long-term outcomes in SJIA patients within this modern era.

1: Medication Usage within First Year (pre/post 2012 where available)Glucocorticoids (IV+PO)%Methotrexate%Biologic%Anti-IL-1%Anakinra%Tocilizumab%USA2010-2011n=922563333330USA2012-2018n=91501771705717Canada2005-2010n=8876601710100UK2001-2011n=69787110330UK2012-2018n=31485829191919Portugal2008-2011n=7342364330Portugal2012-2018n=19744732161621Sweden2009-2015n=50964662302830Denmark1997-2011n=83864013662Denmark2012-2018n=325012.575636319Turkey2000-2011n=71937758423720Turkey2012-2018n=11498524032289Germany2000-2011n=27173621376<1Germany2012-2018n=249574727191020Norway1997-2011n=26816212448Norway2012-2018n=510060100202080Finland2006-2011n=12424217008Finland2012-2018n=1225880082: Clinical Outcomes at 12 Months -all yearsAJCMedian [IQR]PGAMedian [IQR]GC Use, %USA0 [0, 0]0 [0,0]47Canada0 [0, 2]0.1 [0, 2.7]41UK0 [0, 0]0.5 [0, 1.7]53Portugal0 [0, 0]0.3 [0, 1]53Sweden0 [0, 0.5]0 [0, 0.5]31Denmark0 [0, 0]-26Turkey4 [2, 7]4 [3, 7]60Germany0 [0, 1]0 [0,2]36Norway0 [0, 0]0.5 [0, 2]45Finland0 [0, 0]0 [0, 0]33

Disclosure of Interests:

Mary Beth Son: None declared, Yukiko Kimura Consultant of: Genetech, Kristiina Aalto: None declared, Lillemor Berntson: None declared, Johnathan Dallas: None declared, Ciaran Duffy: None declared, Mia Glerup: None declared, Jaime Guzman: None declared, Troels Herlin: None declared, Petteri Hovi: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Jens Klotsche: None declared, Bo Magnusson: None declared, Vanessa McItyre: None declared, Ellen Nordal: None declared, Seza Özen: None declared, Maria Jose Santos Speakers bureau: Novartis and Pfizer, Betül Sözeri: None declared, Timothy Beukelman Consultant of: UCB, Novartis

OP0199 POINTS TO CONSIDER WHEN ANALYSING AND REPORTING COMPARATIVE EFFECTIVENESS RESEARCH WITH OBSERVATIONAL DATA IN RHEUMATOLOGY

Background:

Comparing drug effectiveness in observational settings is hampered by several major threats, among them confounding and attrition bias bias (patients who stop treatment no longer contribute information, which may overestimate true drug effectiveness).

Objectives:

To present points to consider (PtC) when analysing and reporting comparative effectiveness with observational data in rheumatology (EULAR-funded taskforce).

Methods:

The task force comprises 18 experts: epidemiologists, statisticians, rheumatologists, patients, and health professionals.

Results:

A systematic literature review of methods currently used for comparative effectiveness research in rheumatology and a statistical simulation study were used to inform the PtC (table). Overarching principles focused on defining treatment effectiveness and promoting robust and transparent epidemiological and statistical methods increase the trustworthiness of the results.

Points to consider

Reporting of comparative effectiveness observational studies must follow the STROBE guidelinesAuthors should prepare a statistical analysis plan in advanceTo provide a more complete picture of effectiveness, several outcomes across multiple health domains should be comparedLost to follow-up from the study sample must be reported by the exposure of interestThe proportion of patients who stop and/or change therapy over time, as well as the reasons for treatment discontinuation must be reportedCovariates should be chosen based on subject matter knowledge and model selection should be justifiedThe study baseline should be at treatment initiation and a description of how covariate measurements relate to baseline should be includedThe analysis should be based on all patients starting a treatment and not limited to patients remaining on treatment at a certain time pointWhen treatment discontinuation occurs before the time of outcome assessment, this attrition should be taken into account in the analysis.Sensitivity analyses should be undertaken to explore the influence of assumptions related to missingness, particularly in case of attrition

Conclusion:

The increased use of real-world comparative effectiveness studies makes it imperative to reduce divergent or contradictory results due to biases. Having clear recommendations for the analysis and reporting of these studies should promote agreement of observational studies, and improve studies’ trustworthiness, which may also facilitate meta-analysis of observational data.

Disclosure of Interests:

Delphine Courvoisier: None declared, Kim Lauper: None declared, Sytske Anne Bergstra: None declared, Maarten de Wit Grant/research support from: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Consultant of: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Speakers bureau: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Bruno Fautrel Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Consultant of: AbbVie, Biogen, BMS, Boehringer Ingelheim, Celgene, Lilly, Janssen, Medac MSD France, Nordic Pharma, Novartis, Pfizer, Roche, Sanofi Aventis, SOBI and UCB, Thomas Frisell: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Florenzo Iannone Consultant of: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Speakers bureau: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Joanna KEDRA: None declared, Pedro M Machado Consultant of: PMM: Abbvie, Celgene, Janssen, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Speakers bureau: PMM: Abbvie, BMS, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Lykke Midtbøll Ørnbjerg Grant/research support from: Novartis, Ziga Rotar Consultant of: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Speakers bureau: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Maria Jose Santos Speakers bureau: Novartis and Pfizer, Tanja Stamm Grant/research support from: AbbVie, Roche, Consultant of: AbbVie, Sanofi Genzyme, Speakers bureau: AbbVie, Roche, Sanofi, Simon Stones Consultant of: I have been a paid consultant for Envision Pharma Group and Parexel. This does not relate to this abstract., Speakers bureau: I have been a paid speaker for Actelion and Janssen. These do not relate to this abstract., Anja Strangfeld Speakers bureau: AbbVie, BMS, Pfizer, Roche, Sanofi-Aventis, Robert B.M. Landewé Consultant of: AbbVie; AstraZeneca; Bristol-Myers Squibb; Eli Lilly & Co.; Galapagos NV; Novartis; Pfizer; UCB Pharma, Axel Finckh Grant/research support from: Pfizer: Unrestricted research grant, Eli-Lilly: Unrestricted research grant, Consultant of: Sanofi, AB2BIO, Abbvie, Pfizer, MSD, Speakers bureau: Sanofi, Pfizer, Roche, Thermo Fisher Scientific

SAT0038 CHANGES IN ILLNESS PERCEPTIONS IN PEOPLE WITH RHEUMATOID ARTHRITIS OVER THE FIRST YEAR OF TREATMENT WITH METHOTREXATE

Background:

Illness perceptions at treatment onset are known to be important predictors of treatment response in rheumatoid arthritis (RA). Yet it is unknown how these perceptions change over time after the initiation of treatment, or which factors are associated with changing perceptions.

Objectives:

To identify groups of patients with early RA who have similar changes in illness perceptions over the first year following treatment, and assess predictors of these changes.

Methods:

Patients starting methotrexate (MTX) for the first time were recruited to the Rheumatoid Arthritis Medication Study (RAMS), a one-year prospective cohort. The DAS28 was calculated and patients completed a questionnaire at baseline and 12 months, reporting demographics and completing the HAQ, the Hospital Anxiety and Depression Scale (HADS), pain and fatigue visual analogue scales (VAS) and the Brief Illness Perception Questionnaire (B-IPQ). The B-IPQ consists of eight Likert scales: five represent cognitive illness perceptions (B-IPQ1-5), two represent emotional representations (B-IPQ6 & 8) and one represents illness comprehensibility (B-IPQ7). Change in illness perceptions and EULAR response were calculated over 12 months in those with data at both timepoints. Latent profile analysis was used to identify profiles of patients with similar changes in illness perceptions. Candidate predictors of profile membership were assessed using logistic regression. The association between profile and EULAR response was assessed using ordered logistic regression.

Results:

In total 1188 patients were included (mean [SD] age: 59.8 [12.7], 781 [65.7%] women). On average, illness perceptions for the whole cohort improved over 12 months, other than patients’ perception of longevity of arthritis (B-IPQ2) and of treatment helpfulness (B-IPQ4). Three profiles were identified: Small Improvers (N=900), Small Deteriorators (N=78) and Large Improvers (N=210) (Figure). Small Improvers improved on all B-IPQ items other than their perception of longevity of arthritis (B-IPQ2) and of treatment helpfulness (B-IPQ4). All B-IPQ items improved in the Large Improvers group to a greater extent than the Small Improvers, other than arthritis longevity (B-IPQ2). The perceptions of Small Deteriorators all worsened, other than arthritis comprehensibility (B-IPQ7). Higher baseline pain was associated with greater odds of being in both the Small Deteriorators and Large Improvers compared to Small Improvers (Small Deteriorators: OR 1.56 per standard deviation (SD) increase in pain [95% CI 1.11, 2.18]; Large Improvers: OR 1.46 per SD increase in pain [95% CI 1.15, 1.85]). Odds of better EULAR response were greater in the Large Improvers (OR 4.37 [95% CI 3.01, 6.33]) and worse in the Small Deteriorators (OR 0.50 [95% CI 0.29, 0.87]) compared to Small Improvers.

Conclusion:

In general, illness perceptions improved over the first year of MTX treatment and improvements were associated with better treatment response. Worsening illness perceptions may be driven by poor treatment response. These poor illness perceptions at follow-up may compound poor treatment response in the future. Greater understanding of patients’ initial and subsequent illness perceptions is crucial, given the association with treatment response.

Figure:

Disclosure of Interests:

James Gwinnutt Grant/research support from: BMS, Sam Norton: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Mark Lunt: None declared, Anne Barton Consultant of: AbbVie, Lis Cordingley Grant/research support from: Unrestricted award from Pfizer unrelated to current abstract, Speakers bureau: Janssen, AbbVie, Celgene, Sanofi, Eli Lilly, Novartis all unrelated to current abstract, Suzanne Verstappen Grant/research support from: BMS, Consultant of: Celltrion, Speakers bureau: Pfizer

OP0285 TOWARDS IMPLEMENTING THE OMOP CDM ACROSS FIVE EUROPEAN BIOLOGIC REGISTRIES

Background:

The Observational and Medical Outcomes Partnerships (OMOP) common data model (CDM) provides a framework for standardising health data.

Objectives:

To map national biologic registry data collected from different European countries to the OMOP CDM.

Methods:

Five biologic registries are currently being mapped to the OMOP CDM: 1) the Czech biologics register (ATTRA), 2) Registro Español de Acontecimientos Adversos de Terapias Biológicas en Enfermedades Reumáticas (BIOBADASER), 3) British Society for Rheumatology Biologics Register for Rheumatoid Arthritis (BSRBR-RA), 4) German biologics register ‘Rheumatoid arthritis observation of biologic therapy’ (RABBIT), and 5) Swiss register ’Swiss Clinical Quality Management in Rheumatic Diseases’ (SCQM).

Data collected at baseline are being mapped first. Details that uniquely identify individuals are mapped to the person table, with the observation_period table defining the time a person may have had clinical events recorded. Baseline comorbidities are mapped to the condition_occurrence CDM table, while baseline medications are mapped to the drug_exposure CDM table. This mapping is summarised in Figure 1.

Figure 1.

Overview of initial mapping

Results:

A total of 64,901 individuals are included in the 5 registries being mapped to the OMOP CDM, see table 1. The number of unique baseline conditions being mapped range from 17 in BSRBR-RA to 108 in RABBIT, while the number of baseline medications range from 26 in ATTRA to 802 in BSRBR-RA. Those registries which captured more comorbidities or medications generally allowed for these to be inputted as free text.

Table 1.

Summary of initial code mapping

RegistryNumber of individualsNumber of mapped baseline conditionsNumber of mapped baseline medicationsATTRA5,3262626BIOBADASER6,4963051BSRBR-RA21,69517802RABBIT13,06210878SCQM18,3222633

Conclusion:

Due to differences in study design and data capture, the baseline information captured on comorbidities and drugs across registries varies greatly. However, these data have been mapped and mapping biologic registry data to the OMOP CDM is feasible. The adoption of the OMOP CDM will facilitate collaboration across registries and allow for multi-database studies which include data from both biologic registries and other sources of health data which have been mapped to the CDM.

Disclosure of Interests:

Edward Burn: None declared, Lianne Kearsley-Fleet: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Martin Schaefer: None declared, Doreen Huschek: None declared, Anja Strangfeld Speakers bureau: AbbVie, BMS, Pfizer, Roche, Sanofi-Aventis, Jakub Zavada Speakers bureau: Abbvie, UCB, Sanofi, Elli-Lilly, Novartis, Zentiva, Accord, Markéta Lagová: None declared, Delphine Courvoisier: None declared, Christoph Tellenbach: None declared, Kim Lauper: None declared, Carlos Sánchez-Piedra: None declared, Nuria Montero: None declared, Jesús-Tomás Sanchez-Costa: None declared, Daniel Prieto-Alhambra Grant/research support from: Professor Prieto-Alhambra has received research Grants from AMGEN, UCB Biopharma and Les Laboratoires Servier, Consultant of: DPA’s department has received fees for consultancy services from UCB Biopharma, Speakers bureau: DPA’s department has received fees for speaker and advisory board membership services from Amgen

SAT0054 PREVALENCE AND PREDICTORS OF METHOTREXATE-ASSOCIATED ADVERSE EVENTS IN PATIENTS WITH RHEUMATOID ARTHRITIS

Background:

Methotrexate (MTX) is the first-choice treatment for rheumatoid arthritis (RA), but the exact prevalence rates and predictors of important adverse events (AEs) associated with MTX treatment are less well investigated.

Objectives:

To determine the prevalence of MTX AEs (gastrointestinal (GI), mucocutaneous, neurological, haematological, pulmonary, and liver enzymes elevation), and to identify baseline demographic, clinical and drug related predictors of liver and GI AEs.

Methods:

The Rheumatoid Arthritis Medication Study (RAMS) is a UK multi-centre prospective cohort study of patients with RA commencing MTX for the first time. Relevant demographic, medication, clinical and disease related data, and blood samples were collected from patients at baseline. Data on MTX therapy and occurrence of AEs were reported at six and twelve month follow-ups, and include recorded laboratory values of alanine transaminase (ALT) enzyme.

The prevalence rates of AEs were calculated based on the number of patients who reported the AE at either 6 or 12 month follow-up visits. The association between candidate baseline predictors and occurrence of GI or liver AEs was assessed using multivariable logistic regression.

Results:

In total, 2089 participants were included (mean age=58.4±13.5 years; 1390 [66.5%] women). Of those, 1816 and 1584 completed their visits at 6 and 12 months, respectively.

The frequency of abnormal ALT values (>1xULN) was 10.8% (183/1685) and 11.6% (170/1461) at 6 and 12 month follow-up visits, and 15.5% (286/1845) for either visits. The number of patients who reported GI AEs was 777 (40.6%) within 1 year of follow-up. The prevalence of mucocutaneous, neurological, haematological and pulmonary AEs were 441 (23.1%), 487 (25.5%), 116 (6.1%), and 406 (21.3%), respectively.

Male sex, having high ALT at baseline and a history of diabetes were all associated with increased risk of ALT elevation during the study period (Table 1). Contrarily men were 47% less likely to report GI AEs compared to women. Furthermore, younger age and higher baseline disease activity score (DAS28-CRP) were associated with increased risk of GI AEs occurrence.

Table 1.

Baseline predictors of elevated alanine transaminase (ALT) and gastrointestinal (GI) adverse events

VariableElevated ALTGI adverse eventsAdjusted Odds Ratio (95% CI)Age (years)1.00 (0.99, 1.01)0.99 (0.98, 1.00)Male sex1.39 (1.02, 1.90)0.53 (0.42, 0.67)Drink alcohol1.23 (0.88, 1.73)1.09 (0.86, 1.37)Current or past smoking1.10 (0.80, 1.49)1.10 (0.88, 1.37)BMI (kg/m2)1.01 (0.98, 1.03)1.02 (1.00, 1.03)Symptoms duration (months)1.00 (1.00, 1.00)1.00 (1.00, 1.00)RF positivity0.81 (0.60, 1.10)0.93 (0.75, 1.16)DAS28-CRP0.97 (0.87, 1.08)1.13 (1.04, 1.23)ALT at baseline (IU)1.03 (1.02, 1.04)–History of diabetes1.94 (1.22, 3.08)0.91 (0.62, 1.34)History of liver disease1.73 (0.43, 6.95)–History of renal disease1.29 (0.42, 3.96)1.13 (0.50, 2.52)MTX starting dose (mg/week)1.03 (0.98, 1.08)1.03 (0.99, 1.06)

Conclusion:

GI events were the most commonly reported AEs among patients with RA in the first year of MTX treatment, followed by neurological, mucocutaneous and pulmonary AEs. Identifying predictors of AEs may help to optimise drug therapy in RA by tailoring the dosing strategy or frequency of monitoring. This may lead to increased adherence and consequently improved effectiveness.

Disclosure of Interests:

Ahmad Sherbini: None declared, James Gwinnutt Grant/research support from: BMS, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Suzanne Verstappen Grant/research support from: BMS, Consultant of: Celltrion, Speakers bureau: Pfizer

SAT0137 METHOTREXATE ASSOCIATED ADVERSE EVENTS AND THEIR PREDICTORS IN METHOTREXATE-NAÏVE PATIENTS WITH RHEUMATOID ARTHRITIS: A SYSTEMATIC REVIEW

Background:

The adverse events (AEs) associated with methotrexate (MTX) treatment for rheumatoid arthritis (RA) have been studied extensively, but precise estimates of the incidence and prevalence of AEs are lacking. There is also limited published data on the predictors of AEs.

Objectives:

To summarise and pool incidence and prevalence rates of AEs in patients treated with MTX for RA, and to identify treatment, clinical and disease related predictors of AEs.

Methods:

A systematic literature search was carried out using Embase, Medline, and CENTRAL databases to identify relevant studies published between 1/1/2005 and 12/2/2019. The eligibility criteria included RCTs, non-randomized trials, and observational studies of first-time users of MTX in adults (≥ 18 years old) with RA and reported incidence, prevalence or predictors of the most common MTX related AEs, including: any AE, serious AEs, discontinuation due to AEs, elevated liver enzymes, gastrointestinal (GI), mucocutaneous (MC), central nervous system (CNS), and pulmonary AEs. Pooled proportions of GI AEs and elevated liver enzymes of patients treated with MTX monotherapy were estimated using random effects meta-analysis.

Results:

Of 3142 records screened, we included 46 articles (35 clinical trials and 11 cohort studies) with a total of 9646 patients, and a mean follow-up duration of 70±35 weeks (range: 13 - 104 weeks for RCTs, 40 - 156 weeks for observational cohorts).

Six studies reported incidence rate (IR) of any AE (range: 196 - 595 per 100 person-years), and eight studies reported IR of serious AEs (range: 3.7 - 15.9 per 100 person-years). The percentage of patients with any AE, reported in 32 studies, varied between 37% and 100% in RCTs, and between 13% and 34% in observational studies. Discontinuation of MTX due to AEs ranged between 1% and 29% in RCTs, and between 8% and 38% in observational studies. The reported prevalence of MC events (4% - 54%), CNS events (12% - 59%) and pulmonary events (10% - 67%) varied between studies.

The estimated pooled prevalence from studies with a MTX monotherapy arm was 14% (95% CI: 9%, 19%; N=7 studies) for liver enzymes elevation (Figure 1), and 29% (95% CI: 13%, 44%; N=7 studies) for GI AEs (Figure 2).

Figure 1.

Forest plot of pooled prevalence of elevated liver enzymes

Figure 2.

Forest plot of pooled prevalence of gastrointestinal adverse events

No statistically significant predictors of “any AE” were identified. For discontinuation of MTX due to AEs, RF positivity was associated with lower risk of MTX discontinuation due to MTX (HR 0.37, 95%CI: 0.21, 0.64), while other studies found that baseline HAQ score (OR 1.87, 95%CI: 1.11, 3.15) and BMI (OR 1.21, 95%CI: 1.02, 1.44) were associated with increased risk of MTX discontinuation due to AEs. ACPA positivity (OR 1.8, 95%CI: 1.1, 3.1), and high baseline alanine aminotransferase (ALT) (OR 3.1, 95%CI: 1.6, 6.2) were both independent predictors of two-fold elevation of ALT in one paper, and baseline creatinine (OR 1.03, 95%CI: 1.00, 1.07) and high baseline ALT (OR 1.03, 95%CI: 1.00, 1.06) were associated with increased risk of elevated ALT above the upper limit of normal in a different study.

Conclusion:

These findings affirm the high prevalence of GI AEs and elevated liver enzymes among patients treated with MTX for RA. The identified predictors of MTX withdrawal and elevated ALTs may be useful for identifying future patients likely to experience these AEs early in the course of treatment. However, the results of the predictors should be interpreted with caution, and further work is needed to replicate the results in studies with larger sample sizes and to assess the prognostic value of established predictors.

Disclosure of Interests:

Ahmad Sherbini: None declared, Seema Sharma: None declared, James Gwinnutt Grant/research support from: BMS, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Suzanne Verstappen Grant/research support from: BMS, Consultant of: Celltrion, Speakers bureau: Pfizer

OP0198 A SYSTEMATIC REVIEW TO INFORM THE EULAR POINTS TO CONSIDER WHEN ANALYSING AND REPORTING COMPARATIVE EFFECTIVENESS RESEARCH WITH OBSERVATIONAL DATA IN RHEUMATOLOGY

Background:

Comparative effectiveness studies using observational data are increasingly used. Despite their high potential for bias, there are no detailed recommendations on how these studies should best be analysed and reported in rheumatology.

Objectives:

To conduct a systematic literature review of comparative effectiveness research in rheumatology to inform the EULAR task force developing points to consider when analysing and reporting comparative effectiveness research with observational data.

Methods:

All original articles comparing drug effectiveness in longitudinal observational studies of ≥100 patients published in key rheumatology journals (Scientific Citation Index > 2) between 1.01.2008 and 25.03.2019 available in Ovid MEDLINE® were included. Titles and abstracts were screened by two reviewers for the first 1000 abstracts and independently checked to ensure sufficient agreement has been reached. The main information extracted included the types of outcomes used to assess effectiveness, and the types of analyses performed, focusing particularly on confounding and attrition.

Results:

9969 abstracts were screened, with 218 articles proceeding to full-text extraction (Figure 1), representing a number of rheumatic and musculoskeletal diseases. Agreement between the two reviewers for the first 1000 abstracts was 92.7% with a kappa of 0.6. The majority of the studies used several outcomes to evaluate effectiveness (Figure 2A). Most of the studies did not explain how they addressed missing data on the covariates (70%) (Figure 2B). When addressed (30%), 44% used complete case analysis and 10% last observation carried forward (LOCF). 25% of studies did not adjust for confounding factors and there was no clear correlation between the number of factors used to adjust and the number of participants in the studies. An important number of studies selected covariates using bivariate screening and/or stepwise selection. 86% of the studies did not acknowledge attrition (Figure 2C). When trying to correct for attrition (14%), 38% used non-responder (NR) imputation, 24% used LUNDEX1, a form of NR imputation, and 21% LOCF.

Conclusion:

Most of studies used multiple outcomes. However, the vast majority did not acknowledge missing data and attrition, and a quarter did not adjust for any confounding factors. Moreover, when attempting to account for attrition, several studies used methods which potentially increase bias (LOCF, complete case analysis, bivariate screening…). This systematic review confirms the need for the development of recommendations for the assessment and reporting of comparative drug effectiveness in observational data in rheumatology.

References:

[1]Kristensen et al. A&R. 2006 Feb;54(2):600-6.

Acknowledgments:

Support of the Standing Committee on Epidemiology and Health Services Research

Disclosure of Interests:

Kim Lauper: None declared, Joanna KEDRA: None declared, Maarten de Wit Grant/research support from: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Consultant of: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Speakers bureau: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Bruno Fautrel Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Consultant of: AbbVie, Biogen, BMS, Boehringer Ingelheim, Celgene, Lilly, Janssen, Medac MSD France, Nordic Pharma, Novartis, Pfizer, Roche, Sanofi Aventis, SOBI and UCB, Thomas Frisell: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Florenzo Iannone Consultant of: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Speakers bureau: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Pedro M Machado Consultant of: PMM: Abbvie, Celgene, Janssen, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Speakers bureau: PMM: Abbvie, BMS, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Lykke Midtbøll Ørnbjerg Grant/research support from: Novartis, Ziga Rotar Consultant of: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Speakers bureau: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Maria Jose Santos Speakers bureau: Novartis and Pfizer, Tanja Stamm Grant/research support from: AbbVie, Roche, Consultant of: AbbVie, Sanofi Genzyme, Speakers bureau: AbbVie, Roche, Sanofi, Simon Stones Consultant of: I have been a paid consultant for Envision Pharma Group and Parexel. This does not relate to this abstract., Speakers bureau: I have been a paid speaker for Actelion and Janssen. These do not relate to this abstract., Anja Strangfeld Speakers bureau: AbbVie, BMS, Pfizer, Roche, Sanofi-Aventis, Robert B.M. Landewé Consultant of: AbbVie; AstraZeneca; Bristol-Myers Squibb; Eli Lilly & Co.; Galapagos NV; Novartis; Pfizer; UCB Pharma, Axel Finckh Grant/research support from: Pfizer: Unrestricted research grant, Eli-Lilly: Unrestricted research grant, Consultant of: Sanofi, AB2BIO, Abbvie, Pfizer, MSD, Speakers bureau: Sanofi, Pfizer, Roche, Thermo Fisher Scientific, Sytske Anne Bergstra: None declared, Delphine Courvoisier: None declared

AB1203 INVESTIGATING THE VIEWS OF COMMUNITY PHARMACISTS ON THEIR ROLE IN THE MANAGEMENT OF RHEUMATOID ARTHRITIS

Background:

Medicines optimisation is essential in the long-term management of rheumatoid arthritis (RA), particularly when considering combinations of conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs). Community pharmacists are ideally placed to optimise medicines use including monitoring side effects, counselling on dose and frequency and improving medicines adherence; however, in some countries, such as the UK, there are currently no community pharmacy services that address csDMARD use and little is known about the role community pharmacists play in managing RA as a long-term condition.

Objectives:

The objectives of this qualitative study were to understand community pharmacists’ views of their training, knowledge and current role in the management of RA.

Methods:

We conducted 9 semi-structured, face-to-face or telephone interviews with community pharmacists based in the UK; all were transcribed verbatim. A topic guide, used to inform the interviews, covered four key areas: 1) knowledge and training, 2) pharmacological management 3) patients and services, 4) potential role. The transcriptions were then imported into NVivo for thematic analysis. A coding framework was developed from continual emerging themes and applied to the transcripts.

Results:

Five male/4 female participants, the median age was 39 years (range 27 to 42) with a median number of years qualified as a pharmacist of 12 years (range 5 to 20) were included. The participants covered a range of roles including: pharmacist non-manager, pharmacist manager, locum pharmacist, superintendent pharmacist and relief pharmacist.

In assessing the current role of community pharmacists, 4 main themes were identified: (1) access to information about the patient’s condition as a barrier, (2) their lack of knowledge in the management of RA, (3) providing practical advice about taking csDMARDs, and (4) exploring the reasons for non-adherence before taking further action. In assessing the potential role of community pharmacists, a further 2 themes were identified: improving access to information about the patient’s condition before the current role can be increased and other barriers to an additional role, including time and funding.

In the theme ‘access to information as a barrier’ the most common point made was about the lack of information available to pharmacists on the individual indication for medicines. Pharmacists said this posed a barrier both to current practice and their potential role. No participants suggested the potential for an additional service specifically for RA, but some suggested that current services could be expanded to include RA as a target group. Participants discussed side effect counselling and ensuring access to medicines in detail with patients, but only 2 briefly mentioned discussing the benefits of csDMARDs.

Conclusion:

This is the first in-depth exploration of the perspectives of community pharmacists on the management of RA in community pharmacy. This study has highlighted several important barriers both environmental and personal including time, education and resources that, if addressed, could allow community pharmacists to play a greater role in the management of RA.

Disclosure of Interests:

Sarah Wood: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Suzanne Verstappen Grant/research support from: BMS, Consultant of: Celltrion, Speakers bureau: Pfizer, Douglas Steinke: None declared

THU0022 DIFFERENTIAL DNA METHYLATION AS A PREDICTOR OF TOCILIZUMAB RESPONSE IN RHEUMATOID ARTHRITIS PATIENTS

Background:

Tocilizumab (TCZ) is a biological disease-modifying antirheumatic drug that blocks IL-6 signalling and is effective in ameliorating disease activity in rheumatoid arthritis (RA). However, approximately 50% of patients do not respond adequately to TCZ and some patients report adverse events. Considering there is growing evidence that DNA methylation is implicated in RA susceptibility and response to some biologics (1, 2), we investigated DNA methylation as a candidate biomarker for response to TCZ in RA.

Objectives:

To identify differential DNA methylation signatures in whole blood associated with TCZ response in patients with RA.

Methods:

Epigenome-wide DNA methylation patterns were measured using the Infinium EPIC BeadChip (Illumina) in whole blood-derived DNA samples from patients with RA. DNA was extracted from blood samples taken pre-treatment and following 3 months on therapy, and response was determined at 6 months using the Clinical Disease Activity Index (CDAI). Patients who had good response (n=10) or poor response (n=10) to TCZ by 6 months were selected. Samples from secondary poor responders (n=10) (patients who had an improvement of CDAI and were in remission at 3 months, followed by a worsening of CDAI at 6 months) were also analysed. Differentially methylated positions and regions (DMPs/DMRs) were identified using linear regression, adjusting for gender, age, cell composition, smoking status, and glucocorticoid use. Gene Set Enrichment Analysis (GSEA) was used to identify significant pathways associated with response and Functional Epigenetic Module analysis of interactome hotspots in regions of differential methylation.

Results:

20 DMPs were significantly associated with response status at 6 months in the pre-treatment samples. Another 21 DMPs were associated with response in the 3 month samples. Within good responders, 10 DMPs showed significant change in methylation level between pre-treatment and the 3 month samples (unadjusted P-value <10-6). One DMP, cg03121467, was significantly less methylated in good responders compared to poor responders in the pre-treatment samples. This DMP is close toEPB41L4Aand thought to have a role in β–catenin signalling. GSEA of DMRs in non- and secondary non- responders identified histone acetyltransferase pathways and included theKAT2Agene, which is a repressor of NF-κB. Additional analysis of interaction hotspots of differential methylation identified significant interactions withSTAMBPandPTPN12associated with response status.

Conclusion:

These preliminary results provide evidence that DNA methylation patterns may predict response to TCZ. Validation of these findings in other larger data sets is required.

References:

[1]Liu,Y., Aryee,M.J., Padyukov,L., Fallin,M.D., Hesselberg,E., Runarsson,A., Reinius,L., Acevedo,N., Taub,M., Ronninger,M.,et al.(2013) Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis.Nat. Biotechnol.,31, 142–147.

[2]Plant,D., Webster,A., Nair,N., Oliver,J., Smith,S.L., Eyre,S., Hyrich,K.L., Wilson,A.G., Morgan,A.W., Isaacs,J.D.,et al.(2016) Differential Methylation as a Biomarker of Response to Etanercept in Patients With Rheumatoid Arthritis.Arthritis Rheumatol. (Hoboken, N.J.),68, 1353–60.

Disclosure of Interests:

Nisha Nair: None declared, Darren Plant: None declared, John Isaacs Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, Gilead, Janssen, Merck, Pfizer, Roche, Ann Morgan Grant/research support from: I have received a grant from Roche Products Ltd to establish a registry for GCA patients treated with tocilizumab., Consultant of: I have undertaken consultancy work for Roche, Chugai, Regeneron, Sanofi and GSK in the area of GCA therapeutics., Speakers bureau: I have presented on tocilizumab therapy for GCA and glucocorticoid toxicity on behalf of Roche products ltd., Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Anne Barton Consultant of: AbbVie, Anthony G Wilson: None declared