Background In 2019, 2020 and 2021, the European and US-American regulatory agencies issued warnings about venous thromboembolism, major cardiovascular events and malignancy risks associated with the Janus kinase inhibitor (JAKi) tofacitinib and required changes in labelling. Objectives To investigate whether characteristics of patients with rheumatoid arthritis receiving a JAKi versus biologic therapy differed before and after the safety warnings. Methods Data from patients who started with any JAKi or biologics treatment in the German biologics register RABBIT between 01/2017 and 04/2022 were included. Multivariate logistic regression analyses were used to understand differences in characteristics of patients starting a JAKi treatment versus a biologic treatment in three annual cohorts: 2017 the year JAKis became available in Germany, 2019 before the EMA safety warnings and 2021. In each year, only the first treatment episodes of each JAKi, TNF inhibitor, interleukin-6 inhibitor or B/T-cell targeted therapy were considered. Prior treatment episodes were possible. The logistic regressions were corrected for clustering at the patient level. In 2017, we included 549 JAKi versus 2510 bDMARD treatment episodes, in 2019 674 versus 2233 and in 2021 700 versus 1296 episodes. Results Patient characteristics at treatment start have changed over time. In 2017, compared to patients receiving a biologic, patients starting a JAKi had been treated with a higher number of therapies, had a higher (worse) physician reported heath and were more likely to have comorbidities such as hypertension, coronary heart disease, diabetes, hyperlipoproteinaemia, thrombosis, malignancy or lymphoma. In 2019, patients initiating a JAKi therapy compared to a biologic were less likely to be women, had a worse physician reported health and were more likely to receive a dose of less than 10mg glucocorticoids than none. In 2021, after the safety warnings, compared to patients receiving a biologic, those who started a JAKi were older, had a worse physician reported health, had received a higher number of previous therapies, had poorer self-reported health and were less likely to receive a high dose of glucocorticoids. Although not significant, patients with comorbidities were less likely to receive a JAKi. Conclusion The analyses show that after the launch of JAKi treatment in 2017, comorbidities increased the likelihood to receive JAKis as a new treatment option. In 2021, patients with a high disease burden and with many other previous therapies were more likely to receive JAKis, but not those with comorbidities. This development shows that rheumatologists in Germany follow the safety recommendations and consider the patients' disease burden and risk factors when prescribing JAKis. Acknowledgements RABBIT is currently supported by a joint, unconditional grant from AbbVie, Amgen, BMS, Celltrion, Fresenius Kabi, Galapagos, Hexal, Lilly, MSD, Pfizer, Samsung Bioepis, Sanofi, VIATRIS SANTE and UCB and previously by Roche. Disclosure of Interests Doreen Huschek Grant/research support from: Non-personal, joint grant from a consortium of 14 pharmaceutical companies for the biologics register RABBIT to my institute., Peter Herzer Speakers bureau: ABBVIE, NOVARTIS, JANSSEN-CILAG, Angela Zink Grant/research support from: Previously, but not during last three years, Martin Feuchtenberger Speakers bureau: Martin Feuchtenberger reports fees from AbbVie, personal fees from Novartis, personal fees from Roche, and personal fees from UCB outside of the submitted work., Consultant of: Martin Feuchtenberger reports fees from AbbVie outside of the submitted work., Anja Strangfeld Speakers bureau: AbbVie, Amgen, BMS, Celltrion, Janssen, Lilly, Pfizer, Roche, Sanofi, UCB. Non-personal, joint grant from a consortium of 14 pharmaceutical companies for the biologics register RABBIT to my institute., Grant/research support from: Non-personal, joint grant from a consortium of 14 pharmaceutical companies for the biologics register RABBIT to my institute.Table 1Odds Ratios of the logistic regressions for treatment start of a JAKi compared to a bDMARD in 2017, 2019, 2021Year of treatment start201720192021OR LogReg (95%CI)intercept0.100.250.24Ref: age <5050-651.00 (0.77-1.31)1.27 (0.99-1.64)1.50 (1.14-1.97)65+0.81 (0.60-1.10)1.15 (0.86-1.53)1.51 (1.11-2.05)sex (m vs. f)0.96 (0.76-1.20)0.74 (0.60-0.91)1.03 (0.83-1.26)Relevant comorbidities*1.37 (1.10-1.70)1.16 (0.96-1.40)0.86 (0.70-1.06)Seropositivity0.95 (0.76-1.19)0.98 (0.13-1.20)1.16 (0.93-1.45)disease duration0.99 (0.98-1.00)0.99 (0.98-1.00)1.00 (0.99-1.01)#previous b/tsDMARDs1.37 (1.30-1.44)1.02 (0.98-1.07)1.18 (1.12-1.25)physician global health (0-10)1.16 (1.10-1.22)1.09 (1.04-1.14)1.08 (1.03-1.13)patient global health (0-10)0.95 (0.89-1.01)1.02 (0.97-1.08)0.95 (0.90-1.00)% of full physical function1.00 (0.99-1.00)1.00 (0.99-1.00)1.00 (0.99-1.00)Ref: 0 mg/d glucocorticoids>0-<10 mg/d0.89 (0.70-1.12)1.27 (1.05-1.54)1.01 (0.83-1.24)>=10 mg/d0.75 (0.54-1.03)1.01 (0.75-1.37)0.70 (0.50-0.98)*≥ 1 comorbidity mentioned in the safety warnings (hypertension, coronary heart disease, diabetes, hyperlipoproteinaemia, thrombosis, malignancy, lymphoma)

Background Results of the “ORAL Surveillance” trial showed higher risk of major adverse cardiovascular (CV) events (MACE) for Janus kinase inhibitors (JAKi) than for TNF-inhibitors (TNFi). Currently, there is limited evidence of the real-world cardiovascular safety of JAKi. Objectives To assess the incidence of MACE in rheumatoid arthritis (RA) patients treated with JAKi, compared to other biologic agents in a large multi-country real-world population. Methods Patients from 14 RA registers from across Europe, Turkey and Québec (Canada), starting JAKi, TNF-inhibitors or bDMARDs with other modes of action (OMA), were included. MACE comprised strokes, myocardial infarctions and transient ischemic attacks and were attributed to a treatment up to 3 months after treatment cessation (except for rituximab for which it was 1 year), loss of follow-up, death or end of study. Incidence rates (IR) of MACE per 1000 patient-years (PY) with 95% confidence intervals (CI) were computed. Poisson regression, with propensity score weighting (including country, disease-, and patient-characteristics, and comorbidities, see Figure 1), was used to obtain adjusted incidence rate ratios (IRR), with 95% CI. A sub-analysis was performed on patients aged ≥ 50 years and ≥ 1 CV risk factor, mimicking the “ORAL Surveillance” trial inclusion criteria (RCT-duplicate cohort). Results Over the 50'325 treatment initiations considered (Table 1) in 34'932 patients with a mean follow-up of 2.8 years, 182 incident MACE were reported. Crude incidence was higher for OMA (2.63/1000 PY) than for JAKi (1.76/1000 PY) and TNFi (1.86/1000 PY). The adjusted Poisson regression demonstrated no significant difference in the incidence of MACE between JAKi vs TNFi (IRR = 0.87 (95% CI 0.56; 1.35)), and OMA vs TNFi (IRR = 1.05 (95% CI 0.74; 1.49)) (Figure 1). The RCT-duplicate cohort accounted for 38.4% of treatment courses and had a higher incidence of MACE in each treatment group (OMA: 3.75/1000 PY, JAKi: 2.65/1000 PY, TNFi: 3.48/1000 PY). Similarly to the overall population, no significant difference in the incidence of MACE was observed between JAKi vs TNFi (IRR = 0.78 (95% CI 0.44; 1.38)), and OMA vs TNFi (IRR = 0.84 (95% CI 0.53; 1.32)). Conclusion In this real-world study, including 14 RA registers and all currently available JAKi in the respective countries, we did not find a significantly higher risk of MACE in RA patients treated with JAKi compared to TNFi. Inclusion of other registers to increase the statistical power and the evaluation of other adverse events such as thromboembolic events, cancers and serious infections are planned. References [1]Ann Rheum Dis 2022. doi: 10.1136/annrheumdis-2021-221915. Acknowledgements: NIL. Disclosure of Interests Romain Aymon: None declared, Denis Mongin: None declared, Sytske Anne Bergstra Grant/research support from: Pfizer, Denis Choquette Speakers bureau: Abbvie, Amgen, Pizer, Sandoz and Tevapharm, Consultant of: Abbvie, Amgen, Celltrion, Eli Lilly, Fresenius-Kabi, INESSS, Jamppharma, Pfizer, Sandoz and Tevapharm, Grant/research support from: Rhumadata is supported through grants from Abbvie, Amgen, Fresenius-Kabi, Eli Lilly, Pfizer, Sandoz and Tevapharm, Catalin Codreanu Speakers bureau: AbbVie, Amgen, Boehringer Ingelheim, Ewopharma, Lilly, Novartis, Pfizer, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Ewopharma, Lilly, Novartis, Pfizer, René Lindholm Cordtz: None declared, De Cock Diederik: None declared, Lene Dreyer Grant/research support from: Research grant from BMS outside the present work, Ori Elkayam Consultant of: Pfizer, Lilly, Abbvie, Novartis, Jansen, BI, Doreen Huschek: None declared, Kimme Hyrich Speakers bureau: Abbvie, Grant/research support from: Pfizer and BMS, Florenzo Iannone Speakers bureau: Abbvie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Pfizer, SOBI, Roche and UCB, Consultant of: Abbvie, BMS, Celgene, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Pfizer, SOBI, Roche and UCB, Nevsun Inanc Speakers bureau: Abbvie, Amgen, Pfizer, Novartis, Roche, Lilly, MSD, Boehringer Ingelheim and Abdi İbrahim, Consultant of: Abbvie, Amgen, Pfizer, Novartis, Roche, Lilly, MSD, Boehringer Ingelheim and Abdi İbrahim, Lianne Kearsley-Fleet: None declared, Tore K. Kvien Speakers bureau: AbbVie, Amgen, Celltrion, Gilead, Grünenthal, Novartis, Pfizer, Sandoz, UCB, Consultant of: AbbVie, Amgen, Celltrion, Gilead, Grünenthal, Novartis, Pfizer, Sandoz, UCB, Burkhard Leeb Speakers bureau: Eli-Lilly, Pfizer, Astropharma, Biogen, Celgene, Consultant of: Eli-Lilly, Pfizer, AbbVie, Biogen, Celgene, Galina Lukina Speakers bureau: AbbVie, Pfizer, Novartis, Dan Nordström Consultant of: AbbVie, BMS, Lilly, MSD, Novartis, Pfizer, Roche, UCB, Fatos Onen Speakers bureau: Abbvie, Amgen, Pfizer, Novartis, Roche, Lilly, and MSD, Consultant of: Abbvie, Amgen, Pfizer, Novartis, Roche, Lilly, and MSD, Karel Pavelka Speakers bureau: Novartis, AbbVie, Eli Lilly, Pfizer, UCB, MSD, Biogen, Celltrion, Manuel Pombo-Suarez Speakers bureau: Janssen, Merck Sharp & Dohme, Novartis and Sanofi Genzyme, Consultant of: Janssen, Merck Sharp & Dohme, Novartis and Sanofi Genzyme, Sella Aarrestad Provan: None declared, Ana Maria Rodrigues Speakers bureau: Amgen, Phizer, Astrazeneca, Novartis, Roche and Nordic pharma, Consultant of: Amgen, Phizer, Astrazeneca, Novartis, Roche and Nordic pharma, Grant/research support from: Reuma.pt is supported througth grants from Abbvie, Bristol Myers Squibb, Lilly, MSD, Novartis, Pfizer, Boehringer Ingelheim, Astrazeneca and Amgen, Ziga Rotar Speakers bureau: Abbive, Amgen, Biogen, Eli Lilly, Medis, Mediasi, Novartis, Sandoz, Pfizer, MSD, Sanofi, Roche, SOBI., Consultant of: Abbive, Amgen, Biogen, Eli Lilly, Medis, Mediasi, Novartis, Sandoz, Pfizer, MSD, Sanofi, Roche, SOBI., Anja Strangfeld Speakers bureau: AbbVie, Amgen, BMS, Celltrion, Janssen, Lilly, Pfizer, Roche, Sanofi, UCB, Grant/research support from: Non-personal, joint grant from a consortium of 14 pharmaceutical companies for the biologics register RABBIT to my institute., Patrick Verschueren Speakers bureau: Abbvie, Eli Lilly, Galapagos, Consultant of: Celltrion, Eli Lilly, Galapagos, Gilead, Nordic Pharma and Sidekick Health, Jakub Zavada Speakers bureau: Abbvie, Elli-Lilly, Sandoz, Novartis, Egis and UCB, Consultant of: Abbvie, Elli-Lilly, Sandoz, Novartis, Egis and UCB, Delphine Courvoisier: None declared, Axel Finckh Consultant of: Pfizer, BMS, MSD, Eli-Lilly, AbbVie, Galapagos, Mylan, UCB, Viatris, Grant/research support from: Pfizer INC, AbbVie, Galapagos, Eli Lilly, Kim Lauper Speakers bureau: Pfizer, Viatris and Celltrion, Consultant of: Pfizer.Table 1Baseline characteristicsJAKi (TOFA, BARI, UPA, FILGO)n = 12'715OMA (RITU, TOCI, ABA, SARI)n = 16'048TNFi (ETN, ADA, GOL, CTZ, IFX)n = 22'102Treatment duration (median [IQR])1.4 [0.5, 2.7]1.3 [0.4, 2.7]1.5 [0.6, 2.9]Age (mean (SD))58.0 (12.4)59.5 (12.7)56.5 (13.7)Female (%)81.479.278.4Disease duration (median [IQR])11.4 [5.7, 19.0]12.4 [6.3, 20.7]9.2 [4.1, 17.0]Seropositivity (%)80.581.075.1Previous b/ts DMARD (%)023.319.544.1122.324.826.5217.921.715.5≥ 336.633.913.9Concomitant csDMARD (%)MTX22.422.423.9MTX + other9.39.914.0other15.318.017.7none53.052.044.5Concomitant GC (%)44.739.832.4CRP (mg/L) (mean (SD))12.4 (23.2)13.9 (29.0)12.5 (23.3)CDAI (mean (SD))25.4 (13.9)22.8 (13.9)24.7 (13.9)DAS 28 (mean (SD))4.7 (1.6)4.2 (1.7)4.5 (1.7)HAQ (mean (SD))1.2 (0.7)1.2 (0.8)1.1 (0.7)BMI (mean (SD))27.2 (5.9)27.3 (5.9)27.4 (6.3)Patients with at least one CV risk factor* (%)54.956.654.1csDMARDs = conventional 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. *Hypertension, hyperlipidaemia, diabetes, smoking, past history of strokes or myocardial infarctions

Background EULAR developed recommendations for the management of rheumatoid arthritis (RA) suggesting treatment escalation and changes at different stages of the disease to reach at least low disease activity with latest updates in 2013 (1) , 2016 (2) , and 2019 (3) . The recommendation to consider adding a biologic disease-modifying anti-rheumatic drug (bDMARD) – or, since 2016, a Januskinase inhibitor (JAKi) – after the first conventional synthetic (cs) DMARD had failed and if poor prognostic factors (PPF) are present, was strengthened 2019. Since then, it is recommended that a bDMARD or a tsDMARD should be added . Objectives How closely are EULAR recommendations followed in daily rheumatologic practice in Germany? Methods Data were used from the long-term observational cohort RABBIT, which enrols patients with RA starting a bDMARD or JAKi, or a csDMARD after at least one previous csDMARD failure. According to the publication of the recommendations, periods from [I] 01/2014 – 12/2016, [II] 01/2017 – 06/2020 and [III] 07/2020 – 04/2021 were investigated. Patients who were in at least moderate disease activity (DAS28≥3.2) were selected and analysed, if they started a csDMARD, a bDMARD or a JAKi. Patients were further stratified by prior treatments and by the presence of PPF (≥4 swollen joints, positive rheumatoid factor or ACPA, erosions). Results Of the 15,150 patients with RA enrolled since 2007, 2,922 treatments were initiated in period [I], 4,580 in [II] and 415 in [III] (see Table 1). The proportion of patients with 1 previous csDMARD and ≥1 PPF who – in agreement with the recommendations – switched to bDMARD or JAKi, increased from 30% (only bDMARDs) in period [I] to 68% (bDMARDs + JAKi) in [III]. The proportions were even higher in patients with 2 previous csDMARDs (86% in [I], 93% in [III]). As recommended, JAKi were used more often as first line therapy (after csDMARD) in period [III]. Table 1. Number and percentages of treatment changes at different stages of the disease. Patients with 1 previous csDMARD & no PPF 1 previous csDMARD & ≥1 PPF 2 previous csDMARDs 1 previous bDMARD/ JAKi ≥2 previous bDMARDs/ JAKi EULAR Recommendation change/add csDMARD add bDMARD/ JAKi** add bDMARD/ JAKi change to another bDMARD/JAKi Total numbers of treatment changes 61 2073 2220 1700 1863 Period [I] n=25 n=848 n=986 n=543 n=520 01/2014 – 12/2016* N=2,922 csDMARD 21 (84.0%) 594 (70.0%) 134 (13.6%) 199 (36.6%) 275 (52.9%) bDMARD 4 (16.0%) 254 (30.0%) 852 (86.4%) 344 (63.4%) 245 (47.1%) Period [II] n=32 n=1,090 n=1,136 n=1,054 n=1,268 01/2017 – 06/2020 N=4,580 csDMARD 16 (50.0%) 469 (43.0%) 96 (8.5%) 261 (24.8%) 274 (21.6%) bDMARD 13 (40.6%) 509 (46.7%) 822 (72.4%) 403 (38.2%) 288 (22.7%) JAKi 3 (9.4%) 112 (10.3%) 218 (19.2%) 390 (37.0%) 706 (55.7%) Period [III] n=4 n=135 n=98 n=103 n=75 07/2020 – 04/2021 N=415 csDMARD 0 43 (31.9%) 7 (7.1%) 15 (14.6%) 9 (12.0%) bDMARD 1 (25.0%) 64 (47.4%) 60 (61.2%) 36 (35.0%) 23 (30.7%) JAKi 3 (75.0%) 28 (20.7%) 31 (31.6%) 52 (50.5%) 43 (57.3%) EULAR treatment recommendations are indicated in green. * JAKi were not available. ** Recommendation in period [I]: Addition of a bDMARD should be considered; in [II]: Addition of a bDMARD or a tsDMARD should be considered, current practice would be to start a bDMARD; in [III]: a bDMARD or a tsDMARD should be added. PPF, poor prognostic factor. Conclusion JAKi have become more established, especially in bionaive patients, but have not reached the significance of biologics in certain patient groups. The early decision for a bDMARD or JAKi has been made more frequently in recent years, yet one third of patients did not receive the recommended treatment escalation. We cannot conclude from the data, which considerations led to the decision not to escalate. Of note, German rheumatologists should rather follow the German treatment guidelines (4 ) , which are, however, very similar to the EULAR recommendations. References [1] PMID: 24161836; [2] PMID: 28264816; [3] PMID: 31969328; [4] PMID: 29968101 Acknowledgements RABBIT is supported by a joint, unconditional grant from AbbVie, Amgen, BMS, Fresenius-Kabi, Galapagos, Hexal, Lilly, MSD, Pfizer, Roche, Samsung Bioepis, Sanofi-Aventis, VIATRIS and UCB. Disclosure of Interests Yvette Meissner Speakers bureau: Pfizer, Doreen Huschek: None declared, Angela Zink Speakers bureau: AbbVie, Pfizer, Roche, Sanofi, Jörg Kaufmann: None declared, Martin Bohl-Buehler Speakers bureau: Speaker for several companies in unrestricted educational programs, each of them unrestricted state-of-the-art-talks., Consultant of: PreviPharma, basic research in osteology, no overlap with rheumatological diseases, Anja Strangfeld Speakers bureau: AbbVie, Amgen, BMS, Celltrion, Janssen, Lilly, Pfizer, Roche, Sanofi, UCB.

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.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.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.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 scoresRegistryATTRABIOBADASERBSRBR-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%)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.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

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 mappingResults: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 mappingRegistryNumber of individualsNumber of mapped baseline conditionsNumber of mapped baseline medicationsATTRA5,3262626BIOBADASER6,4963051BSRBR-RA21,69517802RABBIT13,06210878SCQM18,3222633Conclusion: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