Pos0423 Change Over 2 Years in Different Phenotypes of Chronic Back Pain Suspicious of Axial Spondyloarthritis: A Latent Transition Analysis of the Spondyloarthritis Caught Early (Space) Cohort

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.