POS0287 USE OF BIOLOGIC DISEASE MODIFYING ANTI-RHEUMATIC DRUGS IN RELATION TO THE RISK OF LYMPHOMA: A COHORT STUDY OF US VETERANS WITH RHEUMATOID ARTHRITIS

Background:Epidemiologic studies suggest that disease duration and degree of inflammatory activity of rheumatoid arthritis (RA) contribute to lymphoma development. However, the association of the use of biologic disease modifying anti-rheumatic drugs (bDMARDs) in patients with RA on lymphoma risk needs further evaluation.Objectives:Examine the effect of administration of bDMARDS on the incidence of lymphoma in an inception cohort of RA.Methods:We identified patients diagnosed with RA in any US Veterans Affairs (VA) facility from 1/1/2002 and 12/31/2018 using the Veteran’s Health Administration (VHA) databases. To be included, each patient was required to meet the following criteria: 1) 2+ RA diagnostic codes at least 7 days apart but no more than 365 days apart 2) a prescription for a conventional synthetic DMARD (csDMARD) within 90 days of the first RA diagnosis 3) One inpatient or outpatient visit 30 days to 2 years preceding first RA diagnosis (indicating they are a regular user of the VHA). We excluded patients for any of the following if they preceded the first RA diagnosis: 1) a prior single RA diagnostic code 2) a prescription for any DMARD medication 3) a concomitant diagnosis of another inflammatory arthritis (e.g. psoriatic arthropathy) 4) a diagnosis of lymphoma. Index date for the study is the date of the first qualifying RA diagnosis. Lymphoma diagnoses were identified through VHA records using the International Classification of Diseases-Oncology codes.Results:We identified 27,536 veterans with RA in the study period meeting the inclusion and exclusion criteria. Of these, 53% (n=14,705) were in the age range 60 to 80 years. The cohort was 89% male, 75.5% White, 13.7% African American. Over the study period, 1.2% (n=332) of the study population developed a lymphoma.Conclusion:Using the nationwide VHA we have identified a large inception cohort of patients with RA of whom 1.2% developed lymphoma over study follow-up. This data will be used in future analyses to produce estimates of the effect of biologic medications on lymphoma risk, adjusting for confounding by indication and other variables.Table 1.Baseline characteristics of the cohort based on bDMARD exposure statusCharacteristicbDMARD-naive (n= 19,095)bDMARD-exposed (n=8,441)Overall Lymphomas Age (years)171161 18-4046 40-606378 60-8010074 >8043 Males17,206 (90%)7,270 (86%)Race White14,150 (74%)6,627 (76%) Black2,674 (14%)1,090 (13%) Asian96 (0.5%)46 (0.5%) Native American or Pacific Islander371 (2%)187 (2.2%) Missing1,804 (9%)491 (6%)Acknowledgements:The work in this abstract is supported by Investigator Award from the Rheumatology Research Foundation to Dr Singh.Disclosure of Interests:None declared

P244 Clinician preferences in treating steroid resistant ulcerative colitis: a discrete-choice experiment (DCE) survey

Background The optimum treatment for steroid resistant ulcerative colitis (SRUC) is not clear and a range of options can be considered. The aim of this study was to quantify the relative importance of different treatment characteristics to clinicians and understand their preferences for benefit-risk trade-offs. Methods A discrete choice experiment (DCE) was conducted in the UK via online survey of clinicians with expertise in inflammatory bowel disease. This involved 12 tasks where respondents selected a preferred treatment option when presented with two competing, hypothetical treatment profiles for a SRUC scenario. Profiles described five treatment characteristics focusing on clinical outcomes and safety, identified from qualitative interviews with clinicians and evidence from systematic reviews. DCE responses were analysed using conditional logistic regressions. Regression coefficients were used to calculate benefit-risk trade-offs, to find the rate at which clinicians are willing to trade levels of risks in exchange for the preferred levels of clinical outcomes. Regression coefficients were also used to predict uptake rates for selected drugs currently prescribed to patients with SRUC. Results 116 clinicians completed the survey (age 46y; female 42%; consultant 60%; nurse 26%; mean experience 11y). Figure 1 shows the treatment characteristics that make respondents more likely (positive coefficient) or less likely (negative coefficient) to choose a treatment. One unit increase in both lymphoma risk and serious infection risk were strongly considered when clinicians were choosing a treatment compared to one unit increase in the symptom improvement characteristics. Clinicians would accept a higher lymphoma risk of 5 per 10,000 patient years for a 10% improvement in remission rate compared to 4 and 2 cases per 10,000 patient years for 10% better rates of clinical response and mucosal healing respectively. Clinicians at secondary hospitals accepted a lymphoma risk of 4 cases per 10,000 patient years for a 10% improvement in remission vs 7 per 10,000 patient years at tertiary centres. Similar trade-off results were found for serious infection risks. Predicted uptake comparing preferred characteristics to existing treatments was infliximab (62%), tofacitinib (18%), vedolizumab (15%) and adalimumab (5%). Conclusion Clinicians are willing to make difficult trade-offs and tolerate elevated safety risks in exchange for therapeutic benefits. Risk tolerance was highest if the treatment improved long term remission rate, lowest for mucosal healing and higher in clinicians at tertiary centres. The results help to better understand treatment decisions and identify outcomes that might be considered in choosing agents to evaluate in clinical trials.

Lymphoma risks in patients with rheumatoid arthritis treated with biological drugs—a Swedish cohort study of risks by time, drug and lymphoma subtype

Objectives To estimate the association between biological DMARDs (bDMARDs; overall and by drug) as used in RA and the risk of malignant lymphomas including subtypes. Methods By linking nationwide Swedish registers we identified cohorts of patients with RA initiating treatment with a bDMARD (n = 16 392), bDMARD-naïve (n = 55 253), an age- and sex-matched general population comparator cohort (n = 229 047), and all incident lymphomas 2001–16. We used Cox regression to calculate hazard ratios (HRs) of lymphoma taking calendar period and other factors into account. Results There were 82 lymphomas among the bDMARD-treated patients with RA, crude incidence rate 76/100 000 person-years, and 310 lymphomas among the bDMARD-naïve patients with RA, crude incidence rate 90/100 000 person-years. This resulted in an adjusted HR (aHR) associated with bDMARD treatment (vs not) of 1.08 (95% CI: 0.83, 1.41). The corresponding aHR for bDMARD-treated and bDMARD-naïve vs the general population was 1.65 (95% CI: 1.31, 2.08) and 1.56 (95% CI: 1.37, 1.78) respectively. Restricting follow-up period to after 2006, the aHR of lymphoma for patients with RA starting a first bDMARD vs bDMARD-naïve was 0.69 (95% CI: 0.47, 1.00), and for bDMARD treated vs patients with RA switching from one conventional synthetic DMARDs to another, aHR was 0.46 (95% CI: 0.28, 0.73). There were no signals of different risks with any particular TNF inhibitor (TNFi) agent. We found no different lymphoma subtype pattern following bDMARD therapy. Conclusion Treatment with bDMARDs, including both TNFi and non-TNFi bDMARDs, does not further increase the lymphoma risk in RA; instead, bDMARD treatment may actually reduce the excess lymphoma risk in RA.