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A case of a 32 year old Caucasian, female patient referred to an outpatient rheumatology clinic with a 3 month history of low back pain and a 2 week duration severe left heel pain is presented. Disease presentation, including findings on physical examination, laboratory tests and imaging are discussed, as well as their potential impact on treatment decisions.Disclosure of InterestNone declared

Background:We have previously identified three phenotypes of axial spondyloarthritis (axSpA) by latent class analysis.[1] One phenotype, that we labelled as ‘Axial’, was characterized by HLA-B27 positivity plus typical abnormalities on axial imaging. The second included patients with inflammatory back pain (IBP) and peripheral features (‘IBP+Peripheral’) and the third had patients with risk factors for axSpA (HLA-B27 and family history) but a low likelihood of other SpA features (‘At risk’). Prognostic information can help clinicians in managing axSpA. However, whether there are prognostic dissimilarities across these phenotypes of axSpA is yet unclear.Objectives:To compare the long-term outcomes of three phenotypes of axSpA.Methods:Patients with axSpA from the DESIR cohort were grouped according to the three previously identified phenotypes at baseline. Clinical data was collected at baseline, every 6 months up to 2 years and then yearly up to 5 years. Radiographs of the spine and sacroiliac joints (SIJ) and magnetic resonance imaging (MRI) of the spine and SIJ were obtained at baseline, 2 and 5 years. Each image was scored by three central readers. Clinical outcomes, measured in each visit, included disability (BASFI), mobility (BASMI) and quality of life (QoL; short form 36 physical (SF36 PCS) and mental (SF36 MCS) component scores). Imaging outcomes included inflammatory (SPARCC score) and structural lesions (number of fatty lesions) on MRI of the spine and SIJ and structural lesions on radiographs of spine (mSASSS) and SIJ (mNY grade). The association between phenotype membership at baseline, operationalized as a categorical variable with the ‘Axial’ class as reference, and each outcome over time was tested in multivariable GEE models. An interaction between class and treatment with bDMARDs at 1 year was tested, and if significant (p<0.15) the effect of class on each outcome was tested separately in treated and untreated patients.Results:In total, 576 patients (‘Axial’: 110; ‘IBP+Peripheral’: 137; ‘At risk’: 329) were included. Patients from the ‘Axial’ class were more often male (80%) than those from the ‘IBP+Peripheral’ (43%) and ‘At risk’ (40%) classes. At baseline, inflammatory and structural lesions on axial imaging were more common in the ‘Axial’ than in the other classes (e.g., mean SPARCC on spine MRI: 7.6 vs 0.7-0.9; and mSASSS 0.9 vs 0.3 in both). Nonetheless, disability at baseline was lower in the ‘Axial’ class (mean BASFI: 2.7) than in ‘IBP+Peripheral’ (BASFI: 3.4) and ‘At risk’ class (BASFI: 2.9). Impairment in spinal mobility was limited in all classes and quality of life was similarly impaired across classes at baseline (SF36 PCS range: 37-41; SF36 MCS: 39-43). The multivariable analysis showed that the ‘At risk’ patients had worse disability and QoL than ‘Axial’ patients over time (Table 1). For instance, ‘At risk’ patients had on average 0.4 more points in BASFI over time than ‘Axial’ patients [β (95% CI): 0.4 (0.2; 0.7)]. Of note, the difference in BASFI between the ‘At risk’ and ‘Axial’ phenotypes was higher in patients receiving bDMARDs than in those not (β=0.6 vs 0.5, interaction p-value: 0.076), since BASFI improved more in ‘Axial’ (ΔBASFI: -1.3) than in ‘At risk’ (ΔBASFI: -0.9) treated patients. A similar interaction between class and bDMARDs was found for the SF-36 PCS but not for the other outcomes. There were no differences in disability, mobility and QoL between ‘Axial’ and ‘IBP+Peripheral’ patients. All imaging outcomes over time were worse in the ‘Axial’ phenotype than in the others (Table 2).Conclusion:Patients with ‘axSpA at risk’ show worse self-reported outcomes over time and are less likely to benefit from anti-inflammatory treatment than those with a classical axSpA phenotype. Rheumatologists might need to tailor treatment strategies according to the phenotype of axSpA and should be particularly vigilant in their indication of targeted DMARDs in patients without objective signs of inflammation.REFERENCES:[1] Sepriano et al. Ann Rheum Dis. 2020.Acknowledgements:NIL.Disclosure of Interests:Alexandre Sepriano Abbvie, Lilly, Abbvie, Novartis, UCB, Sofia Ramiro AbbVie, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Pfizer, UCB, Sanofi, Désirée van der Heijde AbbVie, Argenx, Bayer, BMS, Galapagos, Gilead, Glaxo-Smith-Kline, Janssen, Lilly, Novartis, Pfizer, Takeda, UCB Pharma and is Director of Imaging Rheumatology bv., Anna Moltó AbbVie, Biogen, BMS, Gilead, Galapagos, Janssen, Lilly, Novartis, MSD, Pfizer, UCB Pharma, Cécile Gaujoux-Viala AbbVie; Amgen; Biogen;Boehringer Ingelheim, Bristol-Myers Squibb; Celgene; Eli Lilly; Galapagos; Gilead Sciences, Inc.; Janssen; Medac; Merck-Serono; Mylan; Nordic Pharma; Novartis; Pfizer; Roche; Sandoz; Sanofi; and UCB Pharma, Maxime Dougados AbbVie, BMS, Galapagos, Janssen, Lilly, Novartis, Pfizer, UCB Pharma, Merck, Robert Landewé AbbVie, BMS, Celgene, Eli-Lilly, Galapagos, Gilead, Glaxo-Smith-Kline, Janssen, Merck, Novartis, Pfizer, Roche, UCB and is Director of Rheumatology Consultancy bv.

The Emerging EULAR NETwork (EMEUNET), founded in 2009, aims to promote education, foster research collaborations and facilitate the integration of young researchers and rheumatologists within EULAR. After 10 years, we aimed to interview our members to assess how EMEUNET is fulfilling its goals, understand if there are unmet needs and explore new ideas. In July 2019 a survey was circulated for 3 months among EMEUNET members via its social media channels. The survey contained 18 closed and 2 open items, in addition to general questions on age, job and country of origin and work. Out of 124 total respondents, most completed all items (120/124) and only a minority had some missing items (4/124). A little over half of the respondents were female (n=69, 54.8%) and median age was 33y (range 25y-42y). Most respondents were born (n=109, 88.6%) and/or worked in Europe (n=114, 92.7%) (fig. 1), although there was also a representation from extra-European countries, which is in line with the general EMEUNET membership (https://emeunet.eular.org/map.cfm). 11 (9.1%) had been EMEUNET members for less than 2 years, 81 (66.9%) for 2-5years and 29 (24.0%) for more than 5 years. Figure 2 shows a clear link between the respondents' perception of EMEUNET aim(s) and the actual aims of EMEUNET set out in our mission statement (https://emeunet.eular.org/mission_statement.cfm). Most of the respondents got to know about EMEUNET through a friend/colleague in Rheumatology (n=67, 54.0%), at EULAR/ACR annual conferences (n=32, 25.8%), via social media (n=9, 7.3%), other international meeting (n=6, 4.8%), national Rheumatology meeting (n=6, 4.8%) or via the EMEUNETs country liaisons (n=4, 3.2%). 29 respondents (24%) were part of the EMEUNET working group (WG), 43% (n=53) applied before to be part of the WG, 24% (n=29) never applied and 15% (n=19) did not know what the WG was. Most of the respondents who never applied to the WG thought it to be too time consuming. Only 21 (16.9%) felt there was a subgroup in the WG missing and most of those (n=8, 38.1%) found this should fully focus on research collaborations, which is actually part of the general aims of EMEUNET and a focus of all Working Groups. Only a relatively small portion of EMEUNET of respondents (<5%) stated to have approached EMEUNET with their ideas. Although 90.2% (n=110) felt that the opportunity to submit new ideas is a good initiative, only 52.5% (n=..) knew that such possibilities exists. This suggests that EMEUNET could do more to make members aware of this possibility. Additionally, while the main reasons for not contacting EMEUNET with an idea were ‘not having one’ (41.9%) or having ‘no time’ (34.9%), 20.9% feels their ideas would not be received well, which is another aspect offering room for improvement. Twice a year, EMEUNET organises networking events (NE) for their members to discuss their work in an informal setting. We found that 56/120 (46.7%) of the respondents prefer a low-budget (<€20) event, including an activity and the possibility to network, accompanied by drinks and bites. This is much in line with EMEUNETs past NE. In addition, past NEs were often mentioned as an example of ideal events. Awareness on EMEUNET often comes from colleagues and international conferences in rheumatology, which coincides with one of the focuses of EMEUNET to increase visibility in the last 10 years. Expectations about NE are in line with the previously organised NE. Areas for improvement are dissemination of information on the EMEUNET WG, the possibility to submit ideas -which is open to all members- and the increased focus on research collaboration, an aspect on which we are actively working as EMEUNET is fully engaged with the newly launched EULAR Virtual Research Centre. None declared [Display omitted] [Display omitted]

Background:Sepriano et al have identified four phenotypes of chronic back pain suspicious of axial spondyloarthritis (axSpA) in the Spondyloarthritis Caught Early (SPACE) cohort, reflecting an expert-free judgement of the construct of axSpA.[1] They were labelled as: “pure axSpA” (“axial”) representing individuals with positive axial imaging and a familial/genetic predisposition for axSpA; “axSpA with peripheral signs” (“IBP+peripheral”) with participants characterized by peripheral involvement and inflammatory back pain (IBP), “axial Spondyloarthritis at risk” (“at risk”), a phenotype with a familial/genetic predisposition to axSpA but an otherwise low probability of other features and a “no Spondyloarthritis” (“no SpA”) phenotype depicting participants with a very low probability for any SpA associated feature. However, these phenotypes have not been investigated over time.Objectives:To follow-up these four phenotypes over two years (2Y) and assess a potential switch between them over time.Methods:The SPACE cohort consists of participants with chronic back pain suspicious of axSpA. Data was collected at baseline, three months, one- and 2Y on demographics and all SpA features, including clinical, laboratory and imaging features (see Figure 1). All SPACE participants were included in the analysis. Missing data at baseline and 2Y follow-up were imputed using data from the adjacent visits or, if not possible, were assumed to be absent. In a sensitivity analysis only participants with complete data, both at baseline and 2Y, were included. Latent class analysis (LCA) was performed on baseline data to reassess model fit and clinical recognizability of the four-phenotype model. Latent transition analysis (LTA) models were constructed using data from baseline and 2Y, extracting marginal probabilities, displaying the probability of a participant being in one of the phenotypes, conditional probabilities, which reflect the probability of a SpA feature being present in one of the phenotypes, and transitional probabilities, depicting the probability of switch between the phenotypes from baseline to 2Y follow up.Results:In total, 702 participants were included. Two-hundred-seventy-one (39%) were male and mean (SD) age and duration of back pain were 30 (8) years and 13 (7) months, respectively.Both LCA and LTA models revealed the previously described four-phenotype model as the best fitting model. The highest marginal probabilities were observed in the “no SpA” (37%) and “at-risk” (29%) phenotypes, with comparatively lower probabilities in the “axial” (18%) and “IBP+peripheral” (16%) phenotype. Conditional probabilities (Figure 1) showed a high probability of sacroiliitis on MRI (97%), and elevated CRP values (50%) in the “axial” phenotype, the highest probability of IBP (96%) and peripheral/cutaneous SpA features (including peripheral arthritis, dactylitis, heel pain and psoriasis) in the “IBP+peripheral” phenotype, the highest probability of family history of SpA (100%), but otherwise a lack of distinct SpA features in the “at risk” phenotype and negative family history of SpA and overall the lowest probability for any SpA feature in the “no SpA” phenotype. The LTA revealed a 3% transition probability from the “no SpA“ to the “at risk” class between baseline and 2Y with all other participants remaining in their initially assigned class (Figure 2). Sensitivity analysis on 384 participants with complete data at both baseline and 2Y showed similar results, demonstrating the robustness of the model.Conclusion:Transitions between the four classes over two years were basically non-existent, reflecting the low probability of participants in the cohort to develop relevant new SpA features after an initial clinical work-up.REFERENCES:[1] Sepriano A, Ramiro S, van der Heijde D, van Gaalen F, Hoonhout P, Molto A, et al. What is axial spondyloarthritis? A latent class and transition analysis in the SPACE and DESIR cohorts. Ann Rheum Dis. 2020;79(3):324-31.Acknowledgements:NIL.Disclosure of Interests:Philipp Bosch Janssen, AbbVie, Pfizer, Alexandre Sepriano Abbvie, UCB and Lilly, Mary Lucy Marques: None declared, Désirée van der Heijde AbbVie, Bayer, BMS, Cyxone, Eisai, Galapagos, Gilead, Glaxo-Smith-Kline, Janssen, Novartis, Pfizer, UCB, Lilly, Takeda, Imaging Rheumatology bv, Robert Landewé Abbott, Amgen, AstraZeneca, BMS, GSK, Novartis, Merck, Pfizer, Schering-Plough, UCB Pharma, Imaging Rheumatology bv, Miranda van Lunteren: None declared, Liese J.E. de Bruin: None declared, Manouk de Hooge UCB, Rosalinde Stal, Caroline Bastiaenen: None declared, F. A. van Gaalen Stichting vrienden van Sole Mio, Stichting ASAS, Jacobus stichting, Novartis, UCB, MSD, AbbVie, Bristol Myers Squibb, Eli Lilly, Sofia Ramiro AbbVie/Abbott, Eli Lilly, Galapagos, Merck/MSD, Novartis, Pfizer, UCB, Sanofi.

Due to its multisystemic involvement, systemic lupus erythematosus (SLE) can have a significant impact on patients' quality of life (QoL). Sexual (dis)function is a key component of QoL but is often underappreciated. Little is known about sexual disfunction in SLE patients, a condition that primarily affects women during their fertile age. We aimed at determining the prevalence of sexual dysfunction among women with SLE and predictors thereof. We performed a cross-sectional multicenter study in which women (18-70 years-old) with a clinical diagnosis of SLE (according to their treating rheumatologist) were included. An anonymous online questionnaire was perfomed where data on demographics (e.g., age), symptoms of depression and anxiety [Hospital Anxiety and Depression Scale (HADS)), health-related QoL (Short Form Health Survey Index 36 Item (SF36)]) and sexual function [Female Sexual Function Index (FSFI) - a 19-item patient-report outcome that assesses female sexual function]) were collected. Data on clinical features [disease activity according to the Safety of Estrogens in Lupus National Assessment-SLE Disease Activity Index (SELENA-SLEDAI), organ involvement and evaluation of comorbidity (Charlson Comorbidity Index)] and on treatment status were collected from medical records. The main outcome was sexual dysfunction, defined as FSFI<26.5 (validated cut-off). A multivariable logistic regression was perfomed to test the association of clinical and demographic characteristics with sexual dysfunction (present vs absent). In total, 194 female patients with SLE were included (mean age 44 years-old [standard deviation (SD) 11]). The mean SELENA-SLEDAI score was 1.7 (SD: 2.2), corresponding to low disease activity, and 94% of patients were on classical disease-modifying antirheumatic drugs. The mean value of HADS was 9 (0-21), for both depression and anxiety scores. Regarding SF36, the mental component had a mean value of 61 (0-100) and the physical one of 70 (0-100). Sexual disfunction was present in 128 (66%) patients. In the multivariable analysis (Table 1), older age (OR: 1.04; 95%CI: 1.01; 1.07), higher SELENA-SLEDAI (OR: 1.18; 95%CI: 1.01; 1.40), higher HADS depression score (OR: 1.20; 95%CI: 1.01; 1.43), as well as a lower (that is, worse) SF36 mental component score (OR: 0.97; 95%CI: 0.95; 0.98) were independently associated with sexual dysfunction. Sexual disfunction is common in women with SLE and is influenced by both physical and mental health components. Clinicians should consider both for the optimal management of their patients in order to improve their sexual QoL. NIL. NIL. None Declared. Table 1Factors associated with sexual dysfunction in female patients with SLEUnivariable analysisOR (95% CI)(N=192-194)p-valueMultivariable analysisOR (95% CI)(N=193)Age1.04 (1.01; 1.07)0.011.04 (1.01; 1.07)Menopause (yes vs no)2.38 (1.11; 5.61)0.03†SELENA-SLEDAI1.09 (0.94; 1.28)0.241.18 (1.01; 1.40)CCI1.39 (1.00; 2.02)0.06†bDMARDs (yes vs no)0.85 (0.30; 2.59)0.76*cDMARDs (yes vs no)0.16 (0.01; 0.87)0.09†Glucocorticoids (yes vs no)1.15 (0.63; 2.09)0.66*HADS Depression1.20 (1.03; 1.42)0.021.20 (1.01; 1.43)HADS Anxiety1.19 (1.04; 1.38)0.02†SF36 Mental0.97 (0.95; 0.98)< 0.010.97 (0.95; 0.98)SF36 Physical0.99 (0.98;1.00)0.03†* Not selected in the univariable model (p>0.25)† Not selected in the multivariable model (p>0.05)CCI, Charlson Comorbidity IndexbDMARDs, biologic disease-modifying anti-rheumatic drugscDMARDs, classic disease-modifying anti-rheumatic drugs

BackgroundThere is still little known on posterior element (PE) inflammation and ankylosis in the facet joints (FJ) of patients with axial spondyloarthritis (axSpA).ObjectivesTo assess whether PE inflammation, in particular in the FJ, is associated with new FJ ankylosis on MRI one year later, in patients with radiographic axSpA (r-axSpA).MethodsPatients with a diagnosis of r-axSpA recruited from Germany (Herne) and the Netherlands (Leiden) were included in the Sensitive Imaging in Ankylosing Spondylitis (SIAS) observational cohort when ≥1 inflammatory lesion on MRI of the spine and 1-18 syndesmophytes were present. Spinal MRIs were performed at baseline, 1 and 2 years. PE inflammatory lesions and FJ ankylosis were both assessed on MRI, per vertebral unit (VU) level by 3 readers independently. FJ inflammation in the cervical spine was not assessed. The probability of developing FJ ankylosis after one year was described conditional on the presence or absence of PE/FJ inflammation at baseline. Multilevel time-lagged auto-regressive GEE was used for the association between PE (or FJ) inflammation and the development of FJ ankylosis one year later, taking the reader and VU levels into account.ResultsIn 58 patients MRI scores of at least 2 readers were available. Patients’ average age was 49±10) years and 85% was male. Inflammation in PE or FJ at baseline was seen in 34 (59%) and 14 (24%) patients respectively. PE inflammation was distributed throughout the whole spine but most prevalent in the lower part of the thoracic spine (9%-16%). FJ inflammation was infrequently present and was more often reported in the upper thoracic spine (2%-5%) (Figure 1). FJ ankylosis was reported in 15 patients (26%) at baseline and 17 patients (29%) with follow up visits. FJ ankylosis was mainly present in the upper half of the spine (Figure 1). In 19 patients (33%) the development of new FJ ankylosis over 1 or 2 years was seen by at least 1 reader.Of the VU levels with PE or FJ inflammation only very few showed new FJ ankylosis after one year: 2 and 1 VU levels, respectively (Table 1). There was no association between PE inflammation and the development of new FJ ankylosis in the same level after one year (OR=1.15, 95%CI 0.55-2.42). However, FJ inflammation was associated with new facet joint ankylosis one year later (OR=3.79, 95%CI 1.47-9.75).Table 1.Probability of developing facet joint ankylosis with and without posterior element inflammation present one year before, in r-axSpA patients from the SIAS cohortInflammation in any part of the posterior elementsPE inflammationNew facet joint ankylosis after one yearN*P (FJ ankylosis | PE inflammation)007195P (FJ ankylosis | 0) = 43/7238 =0.0059014310511P (FJ ankylosis | 1) =2/513 =0.0039112Inflammation only in the facet jointFacet joint inflammationNew facet joint ankylosis after one yearN#P (FJ ankylosis | FJ inflammation)005934P (FJ ankylosis | 0) = 38/5972 =0.006401381093P (FJ ankylosis | 1) =1/94 =0.0106111PE; posterior elements, *; number of VU levels with inflammation in at least 1 part of the posterior elements (pedicle, facet joint, processes spinosi, soft tissue), #; number of facet joints, FJ; facet joint, P; probabilityConclusionPE inflammation and FJ ankylosis on MRI were infrequently observed in patients with r-axSpA. No association was found between inflammation in the PE and the development of FJ ankylosis. However, when inflammation in the FJ is present the likelihood of developing FJ ankylosis after 1 year is over 3 times higher compared to FJ without inflammation. This finding adds to the pathophysiological relationship between inflammation and ankylosis at the same anatomical location of the axial skeleton in patients with axSpA.REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsManouk de Hooge Consultant of: UCB, Rosalinde Stal: None declared, Alexandre Sepriano Consultant of: Abbvie, UCB and Lilly, Xenofon Baraliakos Consultant of: AbbVie, BMS, Celltrion, Eli Lilly, Galapagos, MSD, Novartis, Pfizer, UCB, Grant/research support from: AbbVie, MSD, Novartis, UCB, Monique Reijnierse: None declared, Juergen Braun: None declared, Désirée van der Heijde Consultant of: AbbVie, Bayer, BMS, Cyxone, Eisai, Galapagos, Gilead, Glaxo-Smith-Kline, Janssen, Lilly, Novartis, Pfizer, UCB Pharma, Floris A. van Gaalen Consultant of: Novartis, MSD, Abb Vie, Bristol Myers Squibb, Grant/research support from: Stichting vrienden van Sole Mio, Stichting ASAS, acobus Stichting, Novartis, UCB, Sofia Ramiro Consultant of: AbbVie, Eli Lilly, MSD, Novartis, Pfizer, UCB, Sanofi, Grant/research support from: AbbVie, Galapagos, MSD, Novartis, Pfizer, UCB.

Patients (pts) with early arthritis (EA) may present with more or less classical inflammatory disease phenotypes. It is difficult to discern at presentation those who will evolve to a well-defined phenotype (e.g., rheumatoid arthritis; RA) from those who will remain undifferentiated or even will have non-inflammatory disease. The 2010 RA classification criteria were developed to promote early identification of RA and were validated against the external standard of ‘expert diagnosis’, a well-known approach that -however- may lead to circularity. Obtain a more unbiased insight into the ‘Gestalt’ of EA, by circumventing expert opinion, and investigating the latent phenotypes underlying EA and whether there are differences in prognosis across these phenotypes over time. Three cohorts of pts with EA (Reade, ESPOIR and EAC) were analyzed separately. Clinical data were collected up to 12 (ESPOIR), 13 (Reade) or 24 (EAC) years. Hands and feet radiographs were scored, according to the Sharp van der Heijde method (SvdH) up to 10 (ESPOIR), 13 (Reade) and 14 (EAC) years. Latent class analysis was used to estimate the latent (i.e., unobserved) classes of EA. Each class was labelled by us, according to its most prominent features. Outcomes were functional disability, quantified by the health assessment questionnaire (HAQ), quality of life (QoL) quantified by the short form 36 physical (SF36 PC) and mental (SF36 MC) components (in ESPOIR) and radiographic damage (SvdH score). The association between class-membership and each outcome over time was tested in multivariable GEE models. In total, 390 (Reade), 798 (ESPOIR) and 1878 (EAC) pts were included. In ESPOIR, 4 latent classes could be distinguished (Figure 1); Two classes had a high likelihood of symmetrical polyarthritis. One of these, labelled as autoimmune inflammatory polyarthritis (AIPA), had a high likelihood of acute phase reactants (APR)-elevation and autoantibody (AB)-positivity, while the other (mild inflammatory polyarthritis; MIPA) had not. The third class had fewer joints involved (oligoarthritis of upper limbs; OAUL) and the fourth included pts with oligoarthritis of the lower limbs (OALL). All classes, except the latter, were also identified in Reade. In the EAC, OAUL could further be divided into autoimmune OAUL (AIOAUL) and mild-inflammatory OAUL (MIOAUL). In all cohorts, SvdH-scores were consistently worse in classes with AB and APR present (AIPA) than in those without. An example from the EAC cohort shows pts in the MIPA-class had on average 18.5 SvdH-units less than patients in the AIPA-class (Table 1). However, the mean HAQ- and SF36-scores were remarkably similar over time across all classes. The few statistically significant effect-sizes were small and of no clinical relevance. EA pts presenting with elevated (APR and autoantibodies) markers, the phenotype most consistent with the ‘classic RA-construct’, develop more radiographic damage than those without these markers. However, pts with more damage do not necessarily have worse physical function or QoL (measured up to 24 years). These results may justify immediate DMARD-treatment for preventing damage for the proportion of EA-patients that present with an AIPA-phenotype, but not necessarily for all others. [Display omitted] AS and BvD contributed equally to this work. None Declared. Table 1Prognosis of EA phenotypesHAQ (0-3)β (95% CI)SF36 PC (0-100)β (95% CI)SF36 MC (0-100)β (95% CI)SvdH (0-448)β (95% CI)ReadeMIPA vs AIPA0.2 (0.1; 0.3)**-7.0 (-9.4; -4.5)OAUL vs AIPA0.0 (-0.2; 0.1)**-2.7 (-5.9; 0.5)ESPOIRMIPA vs AIPA0.0 (-0.1; 0.1)-0.2 (-1.5; 1.1)-0.2 (-1.8; 1.3)-4.3 (-6.5; -2.2)OAUL vs AIPA-0.1 (-0.1; 0.0)0.5 (-0.5; 1.4)-0.7 (-2.0; 0.5)-0.8 (-3.1; 1.5)OALL vs AIPA0.0 (-0.2; 0.1)-1.0 (-3.4; 1.4)-1.7 (-5.1; 1.7)-4.5 (-6.8; -2.2)EACMIPA vs AIPA-0.1 (-0.2; 0.0)**-18.5 (-25.2; -11.9)AIOAUL vs AIPA-0.1 (-0.2; 0.0)**-6.3 (-15.1; 2.6)MIOAUL vs AIPA0.0 (-0.1; 0.1)**0.7 (-5.3; 6.7)OALL vs AIPA-0.1 (-0.2; 0.1)**-6.8 (-19.2; 5.5)ESPOIR: models adjusted for age, gender, DAS28, NSAIDs, GCs, csDMARDs, bDMARDs. Reade and EAC: age, gender and DAS28. * Not available.

BackgroundThe Berlin algorithm for axial spondyloarthritis (axSpA) diagnosis was developed more than 15 years ago. New evidence suggests that the diagnostic performance of some SpA features might not be as good as initially thought.ObjectivesTo review the evidence on the performance of SpA features for axSpA diagnosis.MethodsSystematic literature review and meta-analysis of studies (2004-2021) reporting data on ≥1 SpA feature. The population was defined as adults (≥16 years) with a suspicion or definite clinical diagnosis of axSpA. The diagnostic performance of each SpA feature was tested against the physician diagnosis of axSpA. For each feature, if ≥2 studies analysed the same cohort, the one with the highest number of patients was included. Pooled sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-) were calculated. Sensitivity analyses were performed to assess the effect of covariates, like feature prevalence in the studies, on the diagnostic performance of SpA features.ResultsIn total, 21 studies (19 cohorts) were included, comprising 8574 patients (4089 [48%] with axSpA). Table 1 shows the results of the meta-analysis. Inflammatory back pain (IBP) and good response to nonsteroidal anti-inflammatory drugs (NSAIDs) have low LR- (0.3), meaning that the diagnosis of axSpA is less likely when absent; however, if present they are not helpful in making a diagnosis (LR+ ~1). Peripheral features, extra-musculoskeletal manifestations (EMM) and preceding infection may help in the diagnosis of axSpA when present (LR+ 1.7-4.2), but have little value in its exclusion when absent (LR- 0.8-1.0). Imaging features, elevated C-reactive protein (CRP) and HLA-B27 have good diagnostic performances, reflected by their high LR+ (2.7-17.7) and (relatively) low LR- (0.5-0.7). In a sensitivity analysis the specificity of IBP was lower in studies with high prevalence of this feature (24% versus 78% in studies with low prevalence). The same was observed for buttock pain, peripheral features, uveitis, good response to NSAIDs, elevated CRP and HLA-B27.ConclusionImaging features, HLA-B27 and elevated CRP have high diagnostic value, even though circularity cannot be ruled out. However, HLA-B27, with a LR+ of 2.7, has a worse diagnostic performance than earlier considered. When present, peripheral features and EMM importantly increase the probability of axSpA, while IBP and good response to NSAIDs do not, but are helpful in ruling out the disease when absent. The diagnostic value of most SpA features is somewhat lower compared to previous data.Table 1.Performance of SpA features for axSpA diagnosisSpA FeatureSensitivity % (95% CI)Specificity % (95% CI)LR+ (95% CI)LR-(95% CI)IBP84 (73; 91)51 (31; 71)1.7 (1.2; 2.5)0.3 (0.2; 0.5)Buttock pain49 (32; 66)71 (56; 82)1.7 (1.2; 2.4)0.7 (0.57; 0.9)Heel enthesitis21 (15; 28)91 (85; 95)2.3 (1.3; 3.9)0.9 (0.8; 0.9)Peripheral arthritis21 (15; 29)92 (84; 96)2.7 (1.4; 5.2)0.9 (0.8; 0.9)Dactylitis5 (3; 7)99 (98; 99)3.9 (2.2; 6.9)1.0 (0.9; 1.0)Uveitis9 (7; 11)98 (96; 99)3.8 (2.1; 6.9)0.9 (0.9; 1.0)Psoriasis8 (4; 13)96 (93; 97)1.7 (1.1; 2.8)1.0 (0.9; 1.0)Inflammatory bowel disease4 (2; 6)99 (97; 100)4.2 (1.2; 14.9)1.0 (1.0; 1.0)Any EMM29 (21; 38)87 (81; 91)2.2 (1.5; 3.4)0.8 (0.7; 1.0)Family history20 (14; 27)91 (84; 96)2.3 (1.2; 4.3)0.9 (0.8; 0.9)Good response to NSAIDs80 (57; 92)60 (46; 72)2.0 (1.2; 3.2)0.3 (0.1; 0.9)Elevated CRP37 (35; 40)90 (81; 96)3.9 (1.6; 5.1)0.7 (0.6; 0.8)HLA-B2761 (52; 69)78 (68; 85)2.7 (2.0; 3.8)0.5 (0.4; 0.6)BME on MRI (ASAS definition)47 (35; 60)97 (94; 98)13.6 (7.9; 23.3)0.6 (0.4; 0.7)mNY+ on X-SIJ31 (23; 40)98 (95; 99)17.7 (6.7; 46.9)0.7 (0.6; 0.8)Any enthesitis21 (13; 32)94 (88; 97)3.3 (1.8; 6.2)0.9 (0.8; 0.9)Preceding infection3 (2; 4)99 (98; 99)3.0 (1.8; 5.1)1.0 (1.0; 1.0)Age at symptom onset <4587 (80; 92)26 (15; 41)1.2 (0.9; 1.5)0.5 (0.3; 1.0)BME on MRI: bone marrow edema on magnetic resonance imaging of the sacroiliac joints (SIJ); mNY+ on X-SIJ: positive modified New York criteria on SIJ radiography.REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Background:The use of an improvement in the axial spondyloarthritis disease activity score (ASDAS) ≥ 1.1 at 12 weeks is recommended by ASAS to guide the decision of whether treatment with a biological disease-modifying antirheumatic drug (bDMARD) should be continued or not.[1] Many, however, still think that improvements may occur after this timepoint and we do not know whether the (time to) response is influenced by patients’ characteristics.Objectives:We aimed to assess the likelihood of fulfilling the ASAS criteria for treatment continuation at 3 and 6 months after the start of a bDMARD and whether there are patient characteristics that influence this response.Methods:Patients with a diagnosis of axSpA, according to their treating rheumatologist, from the Portuguese national registry of rheumatic diseases (Reuma.PT) who started the first bDMARD from 1st of January 2011 to 1st of June 2022 were included. To be included, patients were required to have complete data on ASDAS in the following 3 visits: T0 (baseline visit at the start of the bDMARD), T1 (3 months) and T2 (6 months). We determined how many patients fulfilled the ASAS criteria of treatment continuation (Δ ASDAS ≥ 1.1 compared with baseline) at T1 and T2. Patient characteristics (e.g., age, sex, ethnicity, BMI, smoking status) were compared across four groups of patients based on their response: no response in both visits, response only at T1, response only at T2 and response in both visits. If one or more relevant differences were found, the response at T1 and T2 was then evaluated in the subgroups defined by these characteristics.Results:In total, 336 patients with axSpA [male: 56%; mean (standard deviation) age: 43 (SD 12)] were included. After 3 months, 199 (58%) patients fulfilled the ASAS criteria of treatment continuation (Table 1). This number was similar at 6 months (N=207; 60%). One hundred and eleven (36%) patients had no ASDAS response in both visits, 28 (8%) responded only at T1, 36 (10%) only at T2, and half of the patients responded in both visits (N=171; 50%). The four groups were mostly comparable except for sex and age. Patients who responded at both visits were more often male (65% vs 45%) and somewhat younger (mean age: 42 vs 45) compared with those who never responded. Similar to the entire population, there were no meaningful differences in the likelihood of response at T1 and T2 across subgroups stratified by age and sex (Table 2). The analysis of response across subgroups also revealed that young male patients had the highest likelihood of response to bDMARDs (T1:73%; T2: 75%), while female patients were less likely to respond irrespective of their age (Table 2).Conclusion:The likelihood of a delayed response (beyond 3 months) is very low, which should prompt questioning whether is justified to wait for 6 months to decide on the continuation of the bDMARD treatment. Young male patients are more likely to respond than female patients. Clinicians should keep in mind that diagnostic re-evaluations and addressing comorbidities are as important in the management of axSpA, as choosing the appropriate anti-inflammatory drug.REFERENCES:[1] S. Ramiro et al. Ann Rheum Dis. 2023.Table 1.ASDAS response between T0 and each follow-up visit among patients with ASDAS available in all 3 visitsASDAS response T0 → T2YesNoTOTALASDAS response T0 → T1Yes171 (50)28 (8)199 (58)No36 (10)111 (32)147 (42)TOTAL207 (60)139 (40)346 (100)Values are n (%). The denominator is the total number of patients (N=346) in all cells.Table 2.ASDAS response between T0 and each follow-up visit, stratified on age and gender.All patients(n=346)Male <43 yo(n=104)Male ≥43 yo(n=88)Female <43 yo(n=76)Female ≥43 yo(n=78)T0 → T1, n (%)199 (58)73 (70)49 (56)39 (51)38 (49)T0 → T2, n (%)207 (60)80 (77)51 (58)41 (54)35 (45)Age is a binary variable categorized according to the mean at baseline - age <43 yo (years old) vs age ≥ 43 yo.Acknowledgements:Mariana Diz Lopes; Inês Santos; Bárbara Lobão; Joana Borges; Alice Castro; Sandra Sousa; Cláudia Oliveira; Susana P Silva; Ana Rita Fonseca; Diana Gonçalves; Ana Margarida Correia; Paulo Pereira; Marcelo Neto; Mariana Luís; Maria Pontes Ferreira; Anita Cunha; Maura Couto; Vítor Teixeira; Ana Lúcia Fernandes; Patrícia Nero; Filipe Barcelos; Daniel Melim; João Madruga Dias; Jorge Cantante Garcia; Cristina Catita; Tiago Meirinhos; Marlene Sousa; Susana Fernandes; Filipe Cunha Santos; Sara Dinis; Filipe C. Araújo; Tiago Costa; Sandra Falcão; Filipe Barcelos; Cândida Silva; José Tavares-Costa; Margarida Cruz; Filipa Ramos; Ana Valido; Marília Rodrigues; Margarida Mateus; Carlos Vaz; José Marona; Sandra Sousa; Filipa Farinha; Graça Sequeira; Joana Dinis; Elsa Sousa; Pedro Ávila Ribeiro.Disclosure of Interests:None declared.

Background:Axial spondyloarthritis (axSpA) typically starts before the fourth decade of life. Consistent with that, the Assessment of SpondyloArthritis international Society (ASAS) classification criteria for axSpA should be applied only in patients with chronic back pain starting before 45 years of age. It has, however, been suggested that axSpA can sometimes start later in life with a distinctive phenotype, the so-called ‘late onset axSpA’ (lo-axSpA). There is, nevertheless, only limited data in support of the existence of such phenotype.Objectives:We aimed to evaluate the occurrence of lo-axSpA and whether these patients differ from those with early onset axSpA (eo-axSpA).Methods:We performed a cross-sectional, multicentre, nationwide study using data from Reuma.pt, the Portuguese registry of patients with rheumatic diseases. Adult patients with the clinical diagnosis of axSpA, according to their treating rheumatologist, and with available information on the age of symptom onset were included. Lo-axSpA was defined as axSpA with a symptom onset ≥45 years of age. Demographic characteristics (e.g., age, gender, smoking status, and employment), SpA features [12 features (see Table 1) recorded as ever present, i.e., any time in the past or at the current study visit], measures of disease activity (ASDAS and BASDAI), disability (BASFI) and treatment with NSAIDs, csDMARDs and bDMARDs were compared between patients with lo-axSpA and eo-axSpA at the last available visit at the time of data extraction (13/12/2022). The independent association between these patient and disease characteristics with lo-axSpA was tested in a multivariable logistic regression model. The final multivariable model included all variables with a p-value <0.05 and was adjusted for gender.Results:In total, 2165 patients with axSpA were included. The mean (standard deviation; SD) age at symptom onset was 32 (10) years and the mean symptom duration was 17 (12) years. The majority of the patients were male (56%), most had definite damage on pelvic radiographs according to the modified New York criteria (85%) and were treated with bDMARDs (77%). Out of the total 2165 patients, 273 (13%) had symptom onset ≥45 years and were therefore labelled as lo-axSpA. There were no differences in disease activity, disability or treatment between patients with lo-axSpA and eo-axSpA (Table 1). There were, however, some notable differences between the two groups. Patients with lo-axSpA were less often positive for HLA-B27 (51% vs 65%), less likely to have family history of SpA (8% vs 14%), acute anterior uveitis (AAU, 13% vs 20%) and inflammatory back pain (IBP, 81% vs 88%) than patients with eo-axSpA. On the contrary, patients with lo-axSpA had more peripheral arthritis (36% vs 28%) than patients with eo-axSpA. The multivariable analysis was consistent with these findings (Table 2). Patients positive for HLA-B27 (OR 0.6, 95%: 0.4-0.7), family history of SpA (OR 0.6, 95%: 0.4-0.9), IBP (OR 0.5, 95%: 0.4-0.8), AAU (OR 0.6, 95%: 0.4-0.9) and without peripheral arthritis (OR 1.5, 95%: 1.1-1.9) were less likely to have lo-axSpA.Conclusion:This study shows that axSpA indeed starts before 45 years of age in the vast majority of the patients. Even though recall bias cannot be entirely ruled out, clinicians should however be aware that late-onset disease, though infrequent, may in some cases exist. This minority phenotype has a weaker association with HLA-B27, a lower probability of family history, IBP and uveitis but more peripheral involvement.REFERENCES:[1] Rudwaleit M, et al. Ann Rheum Dis. 2009.[2] Olivieri I, et al. Clin Exp Rheumatol. 2009.Table 1. Comparison of patients and disease characteristics between patients with late and early onset axSpATable 2. Association between patient and disease characteristics with late onset axial SpA (multivariable analysis)Acknowledgements:NIL.Disclosure of Interests:Margarida Lucas Rocha: None declared, Rita Pinheiro Torres: None declared, Sofia Ramiro AbbVie (Consultant, Grant/Research Support); Eli Lilly (Consultant); MSD (Consultant, Grant/Research Support); Novartis (Consultant, Grant/Research Support); Pfizer (Consultant, Grant/Research Support); Sanofi (Consultant); UCB Pharma (Consultant, Grant/Research Support), AbbVie (Consultant, Grant/Research Support); Galapagos (Grant/Research Support); MSD (Consultant, Grant/Research Support); Novartis (Consultant, Grant/Research Support); Pfizer (Consultant, Grant/Research Support); UCB Pharma (Consultant, Grant/Research Support), Alice Castro: None declared, Alice Neves: None declared, Ana Martins: None declared, Ana Teodósio Chícharo: None declared, Beatriz Mendes: None declared, Carolina Ochôa Matos: None declared, Catarina Dantas Soares: None declared, Claudia Miguel: None declared, Cláudia Pinto Oliveira: None declared, Hugo Parente: None declared, José Melo Gomes: None declared, Mariana Luis: None declared, Mariana Emília Santos: None declared, Maura Couto: None declared, Miguel Bernardes: None declared, Paula Valente: None declared, Roberto Pereira da Costa: None declared, Sandra Sousa: None declared, Jaime C. Branco: None declared, Fernando Pimentel-Santos: None declared, Alexandre Sepriano: None declared.