Individual-level and country-level socio-economic factors and health outcomes in spondyloarthritis: analysis of the ASAS-perSpA study.

OBJECTIVES: The aim of this study was to investigate the association between individual-level and country-level socio-economic (SE) factors and health outcomes across SpA phenotypes. METHODS: Patients with axial SpA (axSpA), peripheral SpA (pSpA) or PsA from the ASAS-perSpA study (in 23 countries) were included. The effect of individual-level (age, gender, education and marital status) and country-level [e.g. Gross Domestic Product (GDP)] SE factors on health outcomes [Ankylosing Spondylitis Disease Activity Score (ASDAS) ≥ 2.1, ASDAS, BASFI, fatigue and the Assessment of SpondyloArthritis international Society Health Index (ASAS-HI)] was assessed in mixed-effects models adjusted for potential confounders. Interactions between SE factors and disease phenotype were tested. A mediation analysis was conducted to explore whether the impact of country-level SE factors on ASDAS was mediated through biologic/targeted synthetic (b/ts) DMARD uptake. RESULTS: In total, 4185 patients (61% males, mean age 45) were included (65% axSpA, 25% PsA, 10% pSpA). Female gender [ß= 0.14 (95% CI: 0.06, 0.23)], lower educational level [ß = 0.35 (0.25, 0.45)) and single marital status [ß = 0.09 (0.01, 0.17)] were associated with higher ASDAS. Living in lower GDP countries was also associated with higher ASDAS [ß = 0.39 (0.16, 0.63)], and 7% of this association was mediated by b/tsDMARD uptake. Higher BASFI was similarly associated with female gender, lower education and living alone, without the effect of country-level SE factors. Female gender and lower educational level were associated with worse ASAS-HI, while more fatigue was associated with female gender and higher country-level SE factors [lower GDP, ß = -0.46 (-0.89 to -0.04)]. No differences across disease phenotypes were found. CONCLUSIONS: Our study shows country-driven variations in health outcomes in SpA, independently influenced by individual-level and country-level SE factors and without differences across disease phenotypes.

Clinical characteristics of patients with spondyloarthritis and inflammatory bowel disease versus inflammatory bowel disease-related arthritis.

The purpose of this study was to clarify the clinical characteristics of spondyloarthritis (SpA) patients with inflammatory bowel disease (IBD) compared to those without IBD. Furthermore, among patients with SpA and IBD, we aimed to clarify what clinical characteristics lead rheumatologists to diagnose "IBD-related arthritis." Utilizing SpA and psoriatic arthritis (PsA) patients' data from an international, cross-sectional, observational study, we analyzed information on demographics and disease characteristics, dichotomizing patients by IBD status. The presence or absence of IBD was determined based on data collection of treating rheumatologists. Patients with SpA (including PsA) and IBD were also categorized based on treating rheumatologists' definitive diagnosis in regard to SpA type, and compared by whether the patients had IBD-related arthritis or not. Among 4465 SpA patients, 287 (6.4%, 95%CI 5.7-7.2%) were identified with IBD. Compared to SpA patients without IBD, patients with SpA and IBD had a longer diagnostic delay (5.1 vs. 2.9 years, p < 0.001). In patients with SpA and IBD, 111 (38.7%, 95%CI 33.0-44.6%) were diagnosed with IBD-related arthritis. Multivariable analyses showed that HLA-B27 positivity [OR = 0.35, (95%CI 0.15-0.80)], psoriasis [OR = 0.14, (95%CI 0.04-0.50)], IBD as first symptom of SpA [OR = 3.32, (95%CI 1.84-6.01)], and need for IBD-specific treatment [OR = 5.41, (95%CI 2.02-14.50)] were independently associated with the definitive diagnosis of IBD-related arthritis. Collaboration with gastroenterologists is needed to shorten the diagnostic delay in patients with SpA and IBD. The recognition of the factors for the diagnosis of "IBD-related arthritis" may lead to the elucidation of the pathogenesis.

A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging.

PURPOSE: The purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predict the MRI Assessment of SpondyloArthritis International Society (ASAS) definition of active sacroiliitis in patients with chronic inflammatory back pain. MATERIALS AND METHODS: MRI examinations of patients from the French prospective multicenter DESIR cohort (DEvenir des Spondyloarthropathies Indifférenciées Récentes) were used for training, validation and testing. Patients with inflammatory back pain lasting three months to three years were recruited. Test datasets were from MRI follow-ups at five years and ten years. The model was evaluated using an external test dataset from the ASAS cohort. A neuronal network classifier (mask-RCNN) was trained and evaluated for sacroiliac joints detection and BME classification. Diagnostic capabilities of the model to predict ASAS MRI active sacroiliitis (BME in at least two half-slices) were assessed using Matthews correlation coefficient (MCC), sensitivity, specificity, accuracy and AUC. The gold standard was experts' majority decision. RESULTS: A total of 256 patients with 362 MRI examinations from the DESIR cohort were included, with 27% meeting the ASAS definition for experts. A total of 178 MRI examinations were used for the training set, 25 for the validation set and 159 for the evaluation set. MCCs for DESIR baseline, 5-years, and 10-years follow-up were 0.90 (n = 53), 0.64 (n = 70), and 0.61 (n = 36), respectively. AUCs for predicting ASAS MRI were 0.98 (95% CI: 0.93-1), 0.90 (95% CI: 0.79-1), and 0.80 (95% CI: 0.62-1), respectively. The ASAS external validation cohort included 47 patients (mean age 36 ± 10 [SD] years; women, 51%) with 19% meeting the ASAS definition. MCC was 0.62, sensitivity 56% (95% CI: 42-70), specificity 100% (95% CI: 100-100) and AUC 0.76 (95% CI: 0.57-0.95). CONCLUSION: The deep learning model achieves performance close to those of experts for BME detection in sacroiliac joints and determination of active sacroiliitis according to the ASAS definition.