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Systemic vaccination against SARS-CoV-2 elicited high titers of specific antibodies in the blood and in the oral cavity. Preexisting autoimmune diseases, such as rheumatoid arthritis, and biological treatments, like B cell depletion, are known to exhibit higher risk of severe COVID-19 manifestation and increased frequency of breakthrough infections after vaccination. We hypothesized that such increased risk is associated with an aberrant induction of secreted antibodies in the oral cavity. Here we evaluated the levels of secreted antibodies in the oral cavity against the SARS-CoV-2 Spike protein during the course of vaccination in RA patients with or without B cell depletion. We found that total salivary IgG levels were correlated with number of B cells in the blood. Anti-Spike IgG responses 7 days after second vaccination were induced in the oral cavity of all healthy individuals, while only 6 out 23 RA patients exhibited anti-Spike IgG in their saliva regardless of B cell depleting therapy. Importantly, both salivary and serologic anti-Spike IgG and IgA responses towards WT and omicron Spike variants were efficiently induced by third vaccination in RA patients with or without B cell depletion to the levels that were similar to healthy individuals. Altogether, these data advocate for the necessity of three dose vaccination for RA patients to mount anti-Spike antibody responses at the mucosal surfaces and annotate the reduction of secreted salivary IgG by B cell depletion.

BackgroundThe rheumatoid arthritis impact of disease score (RAID) is a patient-reported outcome measure to evaluate the impact of rheumatoid arthritis (RA) on patients’ quality of life (1). While an adapted instrument has been developed for psoriatic arthritis (2), there are no comparable instruments for other inflammatory rheumatic diseases. Since the RAID includes generic questions on pain, functional capacity, fatigue, physical and emotional well-being, sleep disturbances and coping, it could be an informative instrument beyond RA.ObjectivesTo analyse the performance of the RAID in ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), polymyalgia rheumatica (PMR), primary Sjögren’s syndrome (pSS), idiopathic inflammatory myositis (IIM), and systemic sclerosis (SSc) compared to RA.MethodsFrom 2015 to 2019, a total of 12,398 patients reported the RAID in the National Database of the German Collaborative Arthritis Centres. We calculated the age- and sex-adjusted partial correlation (0.3-0.5 weak, 0.5-0.7 moderate, > 0.7 strong correlation) between the RAID score and five other patient- or physician-reported outcomes, namely patient global (PtGl) health status, PtGl disease activity, physician global (PhGl) disease activity, the World Health Organisation Well-Being Index (WHO-5) and the EuroQoL-5 Dimensions (EQ-5D). Furthermore, for each of the diagnoses the mean difference between the RAID and the other outcomes was compared with the respective differences for RA. The EQ-5D and WHO-5 were rescaled to match the scale of the RAID (range 0-10). General linear regression was used to assess the age- and sex-adjusted effect of each diagnosis on the difference between the RAID and the five other scores with RA as the referent diagnosis. We defined the effect of a diagnosis as clinically relevant if the mean change of difference was at least one unit.ResultsThe mean RAID score in RA (3.6) was lower than in AS (4.0) and SSc (3.8) and higher than in SLE, PMR, pSS or IIM (Table 1). Across all diagnoses, the RAID correlated strongly with PtGl health status (0.72 to 0.83), moderately to strongly with PtGl disease activity (0.55 to 0.78) and WHO-5 (0.67 to 0.83), moderately with the EQ-5D (0.61 to 0.68), and weakly with PhGl disease activity (0.25 to 0.41). Small mean differences were found between the RAID and either PtGl disease activity (0 to -0.6), PtGl health status (-0.4 to -0.9) or WHO-5 (-0.7 to -1.3). A higher deviation was observed for EQ-5D (1.1 to 1.7) and PhGl disease activity (1.4 to 2.2). However, the discrepancies between the five outcomes and the RAID turned out to be similar across all diagnoses and, more importantly, comparable to RA. Linear regression revealed no clinically relevant effect of any of the diagnoses on the difference between RAID and the other outcomes (Figure 1).Table 1.Characteristics and patient- and physician-reported outcomes in patients with inflammatory rheumatic diseases.RAASSLEPMRpSSIIMSScNumber of cases7826153211421105301106386Female (%)74428964895876Age, in years (mean±SD)63±1451±1447±1573±853±1658±1458±14Disease duration, in years (mean±SD)13±1118±1315±105±612±911±812±10RAID (mean±SD)3.6±2.34.0±2.33.0±2.43.2±2.33.4±2.43.5±2.53.8±2.3PtGl health status (mean±SD)4.2±2.34.4±2.23.6±2.44.1±2.34.1±2.34.2±2.54.6±2.1PtGl disease activity (mean±SD)3.7±2.44.0±2.42.7±2.53.8±2.73.5±2.63.7±2.54.1±2.3PhGl disease activity (mean±SD)1.8±1.92.2±1.91.6±1.31.0±1.41.8±1.31.8±1.62.2±1.6EQ-5D (mean±SD)0.8±0.20.8±0.20.8±0.20.8±0.20.8±0.20.8±0.20.8±0.2WHO-5 (mean±SD)57±2553±2358±2556±2657±2560±2653±24SD, Standard Deviation; PtGl, Patient Global; PhGl, Physician Global; EuroQoL-5 Dimensions (EQ-5D); World Health Organisation Well-Being Index (WHO-5)Figure 1.ConclusionThe RAID score performed comparably well across all diagnoses investigated. These findings support the use of the RAID for measuring the impact of disease not only in RA, but also in AS, SLE, PMR, pSS, IIM and SSc.References[1]PMID: 21540201[2]PMID: 24790067AcknowledgementsWe thank all participating rheumatologists and patients for their valuable contributions.The National Database of the German Collaborative Arthritis centers is supported by the Association of Regional Cooperative Rheumatology Centres, the German Society for Rheumatology and joint contributions to the Rheumatological Training Academy and the German Rheumatism Research Centre by the following members of the Working Group of Corporate Members of the German Society for Rheumatology: AbbVie, AstraZeneca, BMS, GALAPAGOS, GSK, Lilly, Medac, MSD, Pfizer, Sanofi-Aventis and UCB.Disclosure of InterestsKatja Thiele: None declared, Katinka Albrecht: None declared, Angela Zink: None declared, Martin Aringer: None declared, Kirsten Karberg: None declared, Susanna Spaethling-Mestekemper Speakers bureau: BMS, Gilead, GSK, Janssen, Lilly, Novartis, Pfizer, Sanofi, UCB, Consultant of: Abbvie, Gilead, GSK, UCB, Novartis, Ulrich von Hinueber: None declared, Johanna Callhoff Paid instructor for: Rheumatologische Fortbildungsakademie GmbH, Grant/research support from: Abbvie, AstraZeneca, BMS, GALAPAGOS, GSK, Lilly, Medac, MSD, Pfizer, Sanofi, UCB

BackgroundThe development of RA is described by a preclinical phase of autoimmunity, that precedes clinical disease. This autoimmune phase is characterized by the presence of anti-modified protein antibodies that recognize citrullinated proteins (ACPA). A subset of individuals with ACPA develops RA, i.e. those with imaging signs of subclinical inflammation in the joints. As T cell mediated B cell activation is a key step in developing autoimmunity and RA, interventions that target this process may be useful for preventing the onset of RA. In this context, abatacept seems being an attractive therapeutic tool as it interrupts the activation of T cells and has a favourable safety profile in the treatment of RA.ObjectivesTo test whether treatment of abatacept, as compared to placebo, delays the onset of RA in ACPA positive individuals with a high risk to develop RA.MethodsARIAA is an international, randomized double-blinded placebo-controlled multi-center study in RA-at risk individuals, being ACPA positive and showing MRI signs of inflammation. The study was composed of a 6 months treatment phase with either abatacept s.c. 125 mg weekly or placebo and a 12 months observation phase with no treatment. Primary endpoint was the improvement of MRI inflammation after 6 months, secondary endpoints were the progression to RA after 6 and 18 months. The primary analysis was done on the ITT population and missing values were classified as treatment failures.ResultsBetween November 2014 and December 2019 139 RA-at risk individuals were included into ARIAA by 14 study sites (11 in Germany, 1 in the Czech Republic and 2 in Spain). Of them, 100 patients were randomized to receive either abatacept or placebo. Two patients were excluded and 98 patients could be evaluated for efficacy and safety. The primary endpoint was met: 61% of abatacept and 31% of placebo treated individuals (p=0.0043) improved in MRI inflammation. Furthermore, only 4 patients (8.2%) in the abatacept group but 17 patients in the placebo group (34.7%) progressed to RA after 6 months (p= 0.0025). Even 1 year after cessation of treatment (18 months after inclusion) the number of patients progressing to RA was lower in the abatacept group (35%) than in the placebo group (57%; p=0.0421). With respect to safety, 12 serious adverse events (each one gastritis, cellulitis, pneumonia, tendinitis calcificans, rotator cuff syndrome, cholelithiasis, peripheral artery disease, idiopathic pain syndrome, prostate cancer, penile neoplasm; trabeculectomy, cataract surgery) were reported, with only one (pneumonia) being considered to be related to treatment.ConclusionAbatacept significantly reduces subclinical joint inflammation and delays the development of RA in at-risk individuals.Table 1.ABAPBOAllN494998Females, N (%)31 (63.3)39 (79.6)70 (71.4)Age (ys); mean (±SD)51.3 (±10.8)48.5 (±12.6)49.9 (±11.7)SJC (N); mean (±SD)000TJC (N); mean (±SD)3.06 (± 3.75)3.51 (± 4.42)3.29 (±4.08)VAS Pain (mm) mean (±SD)42.2 (± 27.1)42.8 (± 33.2)42.5 (± 30.2)VAS PG (mm) mean (±SD42.2 (±28.7)43.0 (± 33.8)42.6 (± 31.2)MRI improvement, N (%)30 (61.2)15 (30.6)45 (45.9)RA at 6 months, N (%)4 (8.2)17 (34.7)21 (21.4)RA at 18 months, N (%)17 (34.7)28 (57.1)45 (45.9)AcknowledgementsThe study was supported by BMS according to the items outlined in the IIS contract and the IMI funded project RTCure.Disclosure of InterestsJürgen Rech Consultant of: Abbvie, Biogen, BMS, Chugai, GSK, Lilly, MSD; Novartis, Roche, Sanofi, Sobi, UCB, Consultancy: Biogen, BMS, Chugai, GSK, Lilly, MSD, Novartis, Roche, Sanofi, Sobi, UCB, Grant/research support from: Sobi, Novartis, Arnd Kleyer Consultant of: BMS, Pfizer, Sanofi, Abbvie, Janssen, Medac, Novartis, Lilly Deutschland GmbH, Gilead, Amgen, Grant/research support from: Novartis, Lilly Deutschland GmbH, Mikkel Østergaard: None declared, Melanie Hagen: None declared, Larissa Valor: None declared, Koray Tascilar: None declared, Gerhard Krönke: None declared, Verena Schönau: None declared, Stefan Kleinert: None declared, Xenofon Baraliakos: None declared, Juergen Braun: None declared, Martin Fleck: None declared, Andrea Rubbert-Roth: None declared, Frank Behrens: None declared, Martin Feuchtenberger: None declared, Michael Zaenker: None declared, David M Kofler: None declared, Reinhard Voll: None declared, Cornelia Glaser: None declared, Axel Hueber: None declared, Eugen Feist: None declared, Gerd Rüdiger Burmester: None declared, Kirsten Karberg: None declared, Johannes Strunk: None declared, Juan de Dios Cañete: None declared, Ladislav Šenolt: None declared, Esperanza Naredo: None declared, Georg Schett: None declared.

BackgroundVaccination is considered efficient in controlling infections incl. SARS-CoV-2. Prior studies showed that patients receiving rituximab (RTX) with low B cell counts are at increased infectious risk (1) and risk of inadequate vaccination responses (2, 3). Thus, the ability to further define and predict vaccination responses in these patients may guide their optimal protection.ObjectivesTo assess predictive biomarkers of vaccination responses upon SARS-CoV-2 vaccination in RTX treated patients.MethodsB cell characteristics before vaccination were evaluated to predict responses in 15 patients with autoimmune inflammatory rheumatic diseases receiving RTX. 11 patients with rheumatoid arthritis on other therapies (RA), 11 kidney transplant recipients (KTR) and 15 healthy volunteers (HC) served as controls. A multidimensional analysis of B cell subsets and a correlation matrix were performed to identify predictive biomarkers.ResultsSignificant differences regarding absolute B cell counts and specific subset distribution pattern between the groups were validated at baseline. Here, the majority of B cells from vaccination responders of the RTX group (RTX IgG+) comprised naïve and transitional B cells, whereas vaccination non-responders (RTX IgG-) carried preferentially plasmablasts and double negative (CD27-IgD-) B cells (Figure 1). Moreover, there was a positive correlation between neutralizing antibodies and absolute B cell numbers with B cells expressing HLA-DR and CXCR5 (involved in antigen presentation and germinal center formation) as well as an inverse correlation with CD95 expression and CD21low expression (marker for activation and exhaustion) on B cells.ConclusionSubstantial repopulation of naïve B cells upon RTX therapy appears to be essential for an adequate vaccination response requiring germinal center formation. In contrast, expression of exhaustion markers (CD21low, CXCR5-, CD95+) indicate negative predictors of vaccination responses. These results may guide optimized vaccination strategies in RTX treated patients clearly requiring antigen-inexperienced B cells for appropriate protection.References[1]Sparks JA, Wallace ZS, Seet AM, Gianfrancesco MA, Izadi Z, Hyrich KL, et al. 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 physician registry. Annals of the Rheumatic Diseases. 2021;80(9):1137-46.[2]Stefanski AL, Rincon-Arevalo H, Schrezenmeier E, Karberg K, Szelinski F, Ritter J, et al. B cell numbers predict humoral and cellular response upon SARS-CoV-2 vaccination among patients treated with rituximab. Arthritis & Rheumatology. Accepted Author Manuscript.[3]Mrak D, Tobudic S, Koblischke M, Graninger M, Radner H, Sieghart D, et al. SARS-CoV-2 vaccination in rituximab-treated patients: B cells promote humoral immune responses in the presence of T-cell-mediated immunity. Annals of the rheumatic diseases. 2021;80(10):1345-50.Disclosure of InterestsNone declared

Background:Psoriatic arthritis (PsA) is a heterogeneous disease with multiple musculoskeletal and dermatological manifestations. Due to this multifaceted clinical appearance, international guidelines do not provide a clear recommendation for one specific score to assess disease activity in PsA [1]. The Disease Activity Index for Psoriatic Arthritis (DAPSA), a validated, unidimensional score focusing on joint involvement, is one of the recommended options [1]. However, routine determination of C-reactive protein (CRP) to calculate DAPSA values takes hours to days. In contrast, quick quantitative CRP (qCRP) tests require only a few minutes and might facilitate regular assessment of the DAPSA (as Q-DAPSA) in clinical routine.Objectives:To validate the Q-DAPSA in a prospective, multicenter study of PsA patients. Since the Disease Activity Score 28 (DAS28) is not only used in rheumatoid arthritis, but also in PsA patients, the study also investigated the performance of a qCRP based DAS28 (DAS28-qCRP) in a PsA cohort.Methods:The study was conducted in five centers in Berlin, Germany. Consecutive adult (≥ 18 years) PsA patients were included. In addition to a rheumatological assessment, including patient reported outcomes (PROs), routine CRP and erythrocyte sedimentation rate (ESR) were measured in the local labs. Additionally, a qCRP testing with the „QuikRead go instrument“ (Aidian Oy, Finland) was performed locally at the study center (measurement range 0.5 - 200 mg/l for hematocrit concentrations of 40 – 45%). Statistical analysis included descriptive statistics, cross tabulation and weighted Cohen´s kappa comparing disease activity categories, Bland-Altman plots and intraclass correlation coefficient (ICC) for DAPSA, Q-DAPSA, DAS28-CRP and DAS28-qCRP.Results:In this study 104 patients were included between January and October 2020 (mean age: 51.2 years, mean disease duration: 7.1 years, 49 patients (47.1%) were male). 53 patients (51.0%) were treated with a bDMARD and 37 patients (35.6%) with csDMARDs. CRP and qCRP showed mean values of 5.20 and 6.17 mg/l, respectively. With the Q-DAPSA, 103 patients (99.0%) were assigned to the same disease activity category when compared to DAPSA (Table 1). Weighted Cohen´s kappa was 0.990 (95%CI 0.970; 1.000). ICC for numerical values of DAPSA and Q-DAPSA was 1.000 (95%CI 0.999; 1.000). The agreement of Q-DAPSA and DAPSA is shown in a Bland-Altman plot (Figure 1). DAS28-CRP and -qCRP were available for 103 patients; 101 patients (98.1%) showed the same disease activity category in the DAS28-qCRP and weighted Cohen´s kappa was 0.951 (95%CI 0.886; 1.000).Conclusion:The Q-DAPSA and DAPSA showed an almost perfect agreement on the assignment to disease activity categories (99%) with the important advantage of time. With Q-DAPSA, rheumatologists could base their clinical decision-making on a disease activity measurement by using a composite score immediately. Consequently, Q-DAPSA can be integrated in clinical routine and clinical trials and could be implemented into the treat-to-target concept in PsA patients. For rheumatologists who prefer DAS28-CRP for assessing disease activity in PsA patients, DAS28-qCRP may serve as a suitable alternative.References:[1]Smolen JS, et al. Ann Rheum Dis. 2018 Jan; 77(1):3-17.Table 1.Disease activity categories by Q-DAPSA vs. DAPASQ-DAPSA (n = 104)Remission (≤ 4)Low Disease Activity (> 4 and ≤ 14)High Disease Activity (> 14 and≤ 28)Very high Disease Activity (> 28)DAPSARemission (≤ 4)36 (34.6%)1 (1.0%)Low Disease Activity (> 4 and ≤ 14)39 (37.5%)High Disease Activity (> 14 and ≤ 28)22 (21.2%)Very high Disease Activity (> 28)6 (5.8%)The fields highlighted in red indicate that disease activity categories do not match. DAPSA = Disease activity index for Psoriatic Arthritis, Q-DAPSA = DAPSA calculated based on a quick quantitative CRPFigure 1.Bland-Altman plot for Q-DAPSA and DAPSAAcknowledgements:The authors would like to deeply thank Braun T, Doerwald C, Deter N, Höppner C, Lackinger J, Lorenz C, Lunkwitz K, Mandt B, Sron S and Zernicke J for their practical support and coordinating the study.Funding statement:The AQUA study was supported by an unrestricted research grant from Novartis. Testing kits were provided free of charge from Aidian Oy, Finland.Disclosure of Interests:None declared.

Background:There is some evidence that neuropsychiatric changes occur in systemic lupus(1) and rheumatoid arthritis(2). However, little is known regarding a possible disease-related impairment of cognitive abilities in axial spondyloarthritis (axSpA).Objectives:To evaluate patients with axSpA regarding cognitive impairments.Methods:Patients with axSpA attending two rheumatology practices were routinely evaluated by rheumatologists and underwent a computer-based memory and attention test (MAT) (3, 4) with subscale scores ranging from 0 (worst) to 15 (best). The results of short-term memory and working memory were compared to an age-, sex- and education-matched control group of healthy subjects. Descriptive results are presented as median (IQR) for interval data and n (%) for nominal data if not stated otherwise. Two-tailed Wilcoxon signed-rank tests including Bonferroni-Holm adjustment for multiple tests were conducted to investigate the magnitude of potential differences in cognitive abilities.Results:101 consecutive patients were tested (Table 1). After multiple testing adjustment for two subscales, Wilcoxon signed-rank tests returned significant findings for working memory (V = 539.5, p = 0.006, |r| = 0.204) but not for short-term memory (V = 1075, p = 0.351, |r| = 0.078). Regarding the scales’ anchors, descriptive results on pairwise differences suggested axSpA patients to have working memory scores that are on average 10.7% lower compared to control participants (mean Δ= -1.64, SD Δ= 5.95).Table 1.Patients and disease characteristicsn%MeanSDMedian25% Quantile75% QuantileAge10110051.111.6524260Age (female)4847.552.611.75444.561Age (male)5352.549.811.5514157< 13 years formal education4746.5≥ 13 years formal education5453.5HLA B27 positive n/N64/9263.4Disease duration (years)10110013.711.712421Disease duration (female, years)4847.511.69.99417.2Disease duration (male, years)5352.515.512.915523BASDAI9291.13.71.73.82.45BASFI9190.132.42.31.24.5BASMI7574.31.92.3103ASDAS98972.30.82.31.82.8Conclusion:The MAT computerized testing is a feasible test and was well accepted by patients. Results regarding working memory suggest that cognitive abilities needed to accomplish everyday tasks may be impaired in axSpA patients. Further work is needed to characterise possible causes of or associations with this cognitive impairment.References:[1]Zabala A, Salgueiro M, Saez-Atxukarro O, Ballesteros J, Ruiz-Irastorza G, Segarra R. Cognitive Impairment in Patients With Neuropsychiatric and Non-neuropsychiatric Systemic Lupus Erythematosus: A Systematic Review and Meta-analysis. J Int Neuropsychol Soc. 2018:1-11.[2]Vitturi BK, Nascimento BAC, Alves BR, de Campos FSC, Torigoe DY. Cognitive impairment in patients with rheumatoid arthritis. J Clin Neurosci. 2019;69:81-7.[3]Adler G, Bektas M, Feger M, Lembach Y. [Computer-based assessment of memory and attention: evaluation of the memory and attention test (MAT)]. Psychiatr Prax. 2012;39(2):79-83.[4]Adler G, Lembach Y. Memory and selective attention in multiple sclerosis: cross-sectional computer-based assessment in a large outpatient sample. Eur Arch Psychiatry Clin Neurosci. 2015;265(5):439-43.Acknowledgements:This study was funded by the RHADAR GbR (A Network of Rheumatologists), Bahnhofstr. 32, 82152 Planegg, Germany. RHADAR GbR has received a grant for this study from Novartis Pharma GmbH.Disclosure of Interests:Stefan Kleinert Consultant of: Novartis, Abbvie, Grant/research support from: Novartis, Praxedis Rapp: None declared., Florian Schuch Speakers bureau: Novartis, Abbvie, Gilead, Consultant of: Novartis, Abbvie, Gilead, Monika Ronneberger: None declared., Joerg Wendler Speakers bureau: Roche, Pharma, JanssenCilag, Novartis, Abbvie, Consultant of: JanssenCilag, Patrizia Sternad: None declared., Florian Popp: None declared., Peter Bartz-Bazzanella: None declared., Cay-Benedict von der Decken: None declared., Kirsten Karberg Speakers bureau: Roche, Sanofi, Abbvie, Lilly, Georg Gauler Speakers bureau: Abbvie, Gilead, Novartis, Lilly, Consultant of: Lilly, Gilead, Abbvie, Patrick Wurth Speakers bureau: Abbvie, Lilly, UCB, Medac, Susanna Spaethling-Mestekemper Speakers bureau: Abbvie, BMS, Celgene, Gilead, GSK, Hexal, Lilly, MSD, Novartis, Pfizer, Sanofi, UCB, Christoph Kuhn: None declared., Matthias Englbrecht Speakers bureau: AbbVie, Chugai, Eli Lilly, Novartis, Roche, Sanofi, Mundipharma, Paid instructor for: AbbVie, Chugai, Roche, Consultant of: AbbVie, Novartis, Roche, Sanofi, Grant/research support from: Roche, Chugai, Wolfgang Vorbrüggen: None declared., Georg Adler: None declared., Martin Welcker Speakers bureau: Abbvie, Actelion, Amgen, Biogen,BMS, Berlin Chemie, Celgene, Galapagos, Gilead, GSK, Hexal, Janssen, Medac, MSD, Mundipharma, Mylan, Novartis, Pfizer, Roche, Sanofi, SOBI, UCB, Grant/research support from: Novartis, Abbvie.

Background:According to international recommendations, the Ankylosing Spondylitis Disease Activity Score (ASDAS) is the preferred score for assessing disease activity in axial spondyloarthritis (axSpA) [1]. However, routine determination of C-reactive protein (CRP) to calculate ASDAS values takes hours to days. This limits the use of ASDAS in clinical routine and clinical trials and hinders the implementation of treat-to-target approaches in axSpA. Whereas quick quantitative CRP (qCRP) tests allow CRP assessment within a few minutes. In a pilot project the performance of qCRP-based ASDAS assessment (ASDAS-qCRP) was already investigated in a single center study of 50 newly diagnosed, bDMARD-naïve axSpA patients with promising results [2].Objectives:To validate the ASDAS-qCRP in a prospective, multicenter study of axSpA patients in a typical axSpA cohort with an appropriate sample size.Methods:The study was conducted in five centers in Germany. Consecutive adult (≥ 18 years) axSpA patients were included. In addition to a rheumatological assessment, including patient reported outcomes (PROs), routine CRP and erythrocyte sedimentation rate (ESR) were measured in the local labs. Additionally, a qCRP testing with the „QuikRead go instrument“ (Aidian Oy, Finland) was performed at the study center (measurement range 0.5 - 200 mg/l for hematocrit concentrations of 40 – 45%). Statistical analysis included descriptive statistics, cross tabulation and weighted Cohen´s kappa comparing disease activity categories, Bland-Altman plots and intraclass correlation coefficient (ICC) for ASDAS-CRP and ASDAS-qCRP.Results:In this study 251 axSpA patients were included between January and September 2020 (mean age: 38.4 years; mean disease duration: 6.2 years, 159 patients (63.3%) were male, 211 (84.1%) HLA-B27 positive and 195 (77.7%) were classified as radiographic axSpA). 143 patients (57.0%) were treated with bDMARDs. CRP and qCRP showed mean values of 2.12 and 2.17 mg/l, respectively. With the ASDAS-qCRP, 242 patients (96.4%) were assigned to the same disease activity category as compared to the ASDAS based on the conventional lab CRP measurement (Table 1). Weighted Cohen´s kappa was 0.966 (95%CI: 0.943; 0.988). ICC for ASDAS-CRP- and ASDAS-qCRP-values was 0.997 (95%CI: 0.994; 0.999). The agreement of ASDAS-qCRP and ASDAS-CRP is shown in a Bland-Altman plot (Figure 1).Table 1.Disease activity categories by ASDAS-qCRP vs. ASDAS-CRPASDAS-qCRP (n = 251)Inactive Disease(< 1.3)Low Disease Activity (1.3 - < 2.1)High Disease Activity (2.1 - 3.5)Very high Disease Activity (> 3.5)ASDAS-CRPInactive Disease(< 1.3)56 (22.3%)2 (0.8%)Low Disease Activity (1.3 - < 2.1)62 (24.7%)7 (2.8%)High Disease Activity (2.1 - 3.5)97 (38.6%)Very high Disease Activity (> 3.5)27 (10.8%)The fields highlighted in red indicate that disease activity categories do not match.ASDAS = Ankylosing Spondylitis Disease Activity Score, CRP = C-reactive protein, qCRP = quick quantitative CRPConclusion:The ASDAS-qCRP and ASDAS-CRP showed an almost perfect agreement on the assignment to disease activity categories (96%) with the important advantage of time. With ASDAS-qCRP, rheumatologists could base their clinical decision-making on a disease activity measurement by using a composite score immediately. ASDAS-qCRP, therefore, can be integrated in clinical routine and clinical trials in the future and may facilitate implementation of the treat-to-target concept in axial SpA.References:[1]Smolen JS, et al. Ann Rheum Dis. 2018 Jan; 77(1):3-17.[2]Proft F, et al. Joint Bone Spine. 2019 Jul 29.Figure 1.Bland-Altman plot for ASDAS-qCRP and ASDAS-CRPAcknowledgements:The authors would like to deeply thank Braun T, Doerwald C, Deter N, Höppner C, Lackinger J, Lorenz C, Lunkwitz K, Mandt B, Sron S and Zernicke J for their practical support and coordinating the study.Funding statement: The AQUA study was supported by an unrestricted research grant from Novartis. Testing kits were provided free of charge from Aidian Oy, Finland.Disclosure of Interests:None declared

Background:Therapeutic decisions in RA patients should be based on regular disease activity assessment using scores like the Simplified Disease Activity Index (SDAI) or the Clinical Disease Activity Index (CDAI) [1]. The CDAI has the benefit of being immediately available, while the SDAI encompasses with the C-reactive protein (CRP) an acute phase reactant and therefore is the recommended score for the use in clinical trials. However, CRP determination takes hours to days, thus hindering the treat-to-target concept using the SDAI. Quick quantitative CRP (qCRP) tests allow CRP measurement within a few minutes. Therefore, qCRP based SDAI (SDAI-Q) could combine the advantages of both scores.Objectives:To validate the SDAI-Q in a prospective, multicenter study of RA patients.Methods:The study was conducted in five centers in Berlin, Germany. Consecutive adult (≥ 18 years) RA patients were included. In addition to a rheumatological assessment, including patient reported outcomes, routine CRP was measured in the local labs. Additionally, a qCRP testing with the „QuikRead go instrument“ (Aidian Oy, Finland) was performed locally (measurement range 0.5 - 200 mg/l). Statistical analysis included descriptive statistics, cross tabulation and weighted Cohen´s kappa comparing disease activity categories, Bland-Altman plots and intraclass correlation coefficient (ICC) for CRP, qCRP, SDAI, SDAI-Q and CDAI.Results:In this study 100 RA patients were included (mean age: 60.9 years, mean disease duration: 11.4 years, 73.0% were female, 63.0% RF positive, 57.0% ACPA positive, 49.0% positive and 29% negative for both parameters). 75.0% were treated with csDMARD, 15% with tsDMARDs, 39.0% with bDMARDs and 40% with glucocorticoids (mean prednisolone equivalent: 5.4 mg prednisolone/d). Mean CRP and qCRP-levels were 6.97 and 7.89 mg/l, respectively (ICC 0.992; 95%CI: 0.987; 0.995). Comparing SDAI-Q and SDAI, all patients (100%) achieved the same disease activity status (Table 1A); weighted Cohen´s kappa was 1.000 (95%CI: 1.000; 1.000). ICC for SDAI-Q- and SDAI-values was 1.000 (95%CI: 1.000; 1.000). The agreement of SDAI-Q and SDAI is shown in a Bland-Altman plot (Figure 1). When comparing the CDAI with the SDAI-Q 93 patients (93%) were assigned to the same disease activity category (Table 1B); weighted Cohen´s kappa was 0.929 (95%CI: 0.878; 0.981). ICC for numerical values of SDAI-Q and CDAI was 0.989 (95%CI: 0.978; 0.994).Conclusion:SDAI-Q showed an absolute agreement with SDAI on the assignment to disease activity categories with the important advantage of time. With SDAI-Q, rheumatologists could base their clinical decision-making immediately on an index-based disease activity measurement by using a composite score considering acute phase reactants. Consequently, SDAI-Q can be integrated in clinical routine and clinical trials and could be implemented into the treat-to-target concept in RA patients.References:[1]Smolen JS, et al. Ann Rheum Dis. 2016 Jan; 75(1):3-15.Table 1.A) Disease activity categories by SDAI-Q vs. SDAI; B) Disease activity categories by SDAI-Q vs. CDAIASDAI-Q (n = 100)Remission (≤ 3.3)Low Disease Activity (> 3.3 and ≤ 11)Moderate Disease Activity (> 11 and ≤ 26)High Disease Activity (> 26)SDAIRemission (≤ 3.3)28 (28.0%)Low Disease Activity (> 3.3 and ≤ 11)31 (31.0%)Moderate Disease Activity (> 11 and ≤ 26)35 (35.0%)High Disease Activity (> 26)6 (6.0%)BSDAI-Q (n = 100)Remission (≤ 3.3)Low Disease Activity (> 3.3 and ≤ 11)Moderate Disease Activity (> 11 and ≤ 26)High Disease Activity (> 26)CDAIRemission (≤ 2.8)26 (26.0%)Low Disease Activity (> 2.8 and ≤ 10)2 (2.0%)28 (28.0%)2 (2.0%)Moderate Disease Activity (> 10 and ≤ 22)3 (3.0%)33 (33.0%)High Disease Activity (> 22)6 (6.0%)Fields highlighted in red indicate that disease activity categories do not match.SDAI = Simplified Disease Activity Index;SDAI-Q = SDAI calculated with a quick quantitative CRP assay;CDAI = Clinical Disease Activity Index.Figure 1.Bland-Altman plot for SDAI and SDAI-Q AcknowledgementsThe authors would like to deeply thank Braun T, Doerwald C, Deter N, Höppner C, Lackinger J, Lorenz C, Lunkwitz K, Mandt B, Sron S and Zernicke J for their practical support and coordinating the study.Funding statement:The AQUA study was supported by an unrestricted research grant from Novartis. Testing kits were provided free of charge from Aidian Oy, Finland.Disclosure of Interests:None declared

Background Making the diagnosis of axial spondyloarthritis (axSpA) may be difficult. Antibodies against CD74 have been shown to be present in 2/3 of patients with long established axial SpA. Objectives InterSpA is an international multicenter study, conducted to compare the sensitivity and specificity of anti-CD74 and HLA-B27 in patients with axSpA of recent onset. Methods Patients between 18 and 45 years suffering from inflammatory back pain (IBP) for maximally 2 years were recruited. The presence of ankylosing spondylitis, additional inflammatory rheumatic disorders and biologic therapy were exclusion criteria. MRI of sacroiliac joint was performed in all patients; HLA-B27 was detected by genotyping and anti-CD74 using a CE certified kit of Aesku Diagnostics (Wendelsheim, Germany). The sensitivity of anti-CD74 and HLA-B27 were calculated in patients fulfilling the imaging arm of ASAS criteria, in all patients fulfilling ASAS criteria and in 100 blood donors. Both the MRI reading as well as the laboratory procedures were performed blinded. Results 205 patients suffering from IBP were recruited. There were 40 recruiting failures. The remaining 165 patients had a mean age of 29.4 years, a mean duration of IBP of 12.6 months, 50% of the patients were female. Sacroiliitis was diagnosed by the local and an independent expert reader in 71/123 (57.7%). So far complete data sets are available of 91 patients. Of these, 16 fulfilled the ASAS criteria of axSpA by a pathologic MRI only, 40 by both MRI and presence of HLA-B27 and 20 by HLA-B27 only. The sensitivities of IgA anti-CD74, IgG anti-CD74 and HLA-B27 were 71.4%, 26.8% and 75% in the 56 axSpA patients with a pathologic MRI, 73.7%, 23.7% and 81.6% in all 76 patients fulfilling ASAS criteria, and 3%, 5% and 8% in the blood donors. The likelihood ratios are 23.8 (IgA anti-CD74), 5.4 (IgG anti-CD74) and 9.4 (HLA-B27) when considering the patients with a pathologic MRI only, and 24.6 (IgA anti-CD74), 4.7 (IgG anti-CD74) and 10.2 (HLA-B27) when considering all patients fulfilling ASAS criteria. Conclusions Considering their specificity and sensitivity, IgA anti-CD74 is a useful addition to our diagnostic tools for axSpA. Acknowledgement This study is funded by AbbVie Deutschland GmbH & Co. KG. AbbVie contributes to study logistics and site management. The authors determined study design, conduct of the study and the content of the publication. No payments were made to the authors for writing this manuscript. Disclosure of Interest E. Riechers Grant/research support from: AbbVie GmbH, N. T. Baerlecken Grant/research support from: AbbVie GmbH, X. Baraliakos: None declared, K. Achilles-Mehr Bakhsh: None declared, P. M. Aries: None declared, B. Bannert: None declared, K. Becker: None declared, J. F. Brandt-Jurgens: None declared, J. Braun: None declared, B. Ehrenstein: None declared, H. Euler: None declared, M. Fleck: None declared, R. Hein: None declared, K. Karberg: None declared, L. Kohler: None declared, T. Matthias: None declared, R. Max: None declared, A. Melzer: None declared, D. Meyer-Olson: None declared, J. Rech: None declared, K. Rockwitz: None declared, M. Rudwaleit: None declared, E. Schweikhard Employee of: Aesku GmbH, J. Sieper: None declared, C. Stille: None declared, U. von Hinuber: None declared, P. Wagener: None declared, H. Weidemann: None declared, S. Zinke: None declared, T. Witte Grant/research support from: AbbVie GmbH

Background Early stages of rheumatic diseases (0–12 weeks) including rheumatoid arthritis (RA) are still difficult to diagnose. For this reason, incorrect referrals to a rheumatologist result in a high use of resources in outpatient clinics. Objectives Implementation of a structured screening system for selecting and treating patients (pts) with RA or other rheumatic and musculoskeletal diseases (RMD). Methods 98 pts visited a screening appointment for early arthritis (EA) between February 2015 and September 2015 in special EA departments. Inclusion criterion was arthritis ≥ one joint for less than one year recorded by phone. Qualified health professional assistants (HPA) conducted the phone call. Upon screening, all pts filled in a tablet based questionnaire to be developed for future online use about their symptoms. Subsequently, 53 pts (group 1) were seen first by an HPA who analysed the questionnaire and performed the joint count. Based on these findings, the HPA made a suspected diagnosis. Afterwards, also a rheumatologist saw these pts and made a suspected diagnosis. In comparison, the rheumatologist directly examined 45 pts without an HPA (group 2). In both groups, typical clinical findings led to a new appointment, or in acute cases, treatment was started immediately. If an RMD was suspected or laboratory parameters were abnormal, pts received a second appointment as well. The documentation of the real-life data was carried out by the Medpath © system based on medical and flexible adaptable paths. Results Mean duration between phone call and screening was 19.6±12.8 days. Pts had an age of 18–80 years (mean 51.1±15.5 years), and 73 pts (74.5%) were female. Although all pts had reported symptoms of less than one year over the phone, only 70 pts (71.4%) fulfilled this criterion when asked in the questionnaire. In group 1, according to the HPA 22 of 53 (41.5%) pts had an RMD upon screening. The suspected diagnoses of HPA and rheumatologist corresponded in 43 of 53 (81.1%) pts in the screening. In group 2, the rheumatologist suspected 14 of 45 (31.1%) pts having an RMD in the first visit. 59 (60.2%) pts had no RMD. 23 (23.5%) pts had RA that was seropositive in 14 (14.3%) pts. All RA pts were recommended a therapy with conventional disease modifying antirheumatic drugs (cDMARDs) and glucocorticoids (GC), which 21 (21.4%) pts started 17.2±27.6 days after screening. 3 (3%) pts initially had a preclinical seropositive RA, which changed into definite RA in need for treatment with cDMARDs after 191 days in one patient. 3 (3%) pts received inpatient treatment and systemic lupus erythematosus was diagnosed in one case. Conclusions The structured screening system efficiently selects pts with RMDs and leads to treating RMD at an early stage in times of limited resources. Acknowledgement Abbvie supports the project within the T2T Initiative Germany. Disclosure of Interest None declared