ABS0655 EVALUATION OF CONCOMITANT PERIPHERAL ARTHRITIS IN EARLY AXIAL SPONDYLOARTHRITIS: RESULTS FROM A 72-MONTH FOLLOW-UP ITALIAN ARM OF SPONDYLOARTHRITIS CAUGHT EARLY (SPACE) COHORT
Bibliographic record
Abstract
Background: Peripheral arthritis (PA) is common in early axial-spondyloarthritis (axSpA), but the influence of PA on spinal and pelvic structural damage and on disease-activity has not been thoroughly investigated. Objectives: We aimed to assess the association between PA and clinical and disease-activity indices, and X-rays and magnetic-resonance-imaging (MRI) features in early axSpA. Methods: Baseline data analysis of the Italian SPondyloArthritis-Caught-Early (SPACE)-cohort, including patients (age <45y) with chronic-back-pain of recent onset (≥3 months, ≤2y) and unknown origin. Patients underwent MRI and X-rays of the sacroiliac-joints (SIJ) to establish diagnosis of axSpA (according to ASAS-criteria and physician's judgement). The full diagnostic work-up included all clinical SpA-features, acute phase reactants, human-leukocyte-antigen (HLA)-B27, radiographs and MRI of the sacroiliac joints (SIJ) and spine. Clinical assessements, disease-activity and functional indices were collected at baseline (T0) and yearly during 72-months. Spinal and SIJ X-rays and MRIs were performed every 2-years and scored independently by 2 readers following Stoke Ankylosing Spondylitis Spinal Score System modified by Creemers (mSASSS) (score 0-72), modified New York criteria grading system (mNY-criteria) (score 0-4 per each joint) and Spondyloarthritis Research Consortium of Canada (SPARCC) (score of 0–40 for SIJ and of 0-92 for the spine). Characteristics of axSpA patients according to PA were compared over-time with descriptive-statistics; logistic-regression model was constructed to assess the association between baseline axSpA features and PA. Results: Ninety-one patients had axSpA (83.5% non-radiographic;16.5% radiographic); 44% had PA. AT T0 axSpA without PA had shorter axial symptoms duration (p=0.04), more frequently HLA-B27+ (p=0.02) and uveitis (p=0.03), radiographic sacroiliitis with bilateral/symmetric pattern (p=0.03) and less signs of spondylitis (p=0.04). AxSpA with PA was more frequently associated with dactylitis/entheseal involvement (p<0.02) and higher intake of combined therapy (p<0.01) (Table 1). There was overall improvement — slightly less in axSpA with PA — in functional and disease-activity indices ( data not shown ). All patients showed slight spinal/pelvic radiographic progression. Patients without PA showed increased sacroiliitis progression and low-grade spinal progression (Figure 1 A-C). At T0, axSpA without PA had more frequently active sacroiliitis on MRI than those with PA [70.6% vs. 65%]. Instead, we observed a slightly higher prevalence of inflammatory corner lesions in axSpA patients with PA than those without PA [68.8% vs. 65%], especially in cervical and thoracic district [(30% vs. 21.6%) and (42.5% vs. 39.2%), respectively] ( data not shown ). We noted a significant downtrend of SPARCC SIJ/spine scores in all patients, regardless the concomitant presence of PA. More fat lesions were observed on MRI-spine in axSpA with PA, more fat lesions on MRI-SIJ in axSpA without PA (Figure 1 D-E). Multivariate analysis showed that PA was independently associated with higher CRP values at baseline (≥ 6 mg/dl) [OR 5.79 (95%CI 1.76–19.02); p=0.004] and dactylitis [OR 7.91 (95%CI 1.38–45.34; p=0.020)]. Conclusion: PA was associated with distinct axSpA features. AxSpA with PA showed increased spinal radiographic progression and low-grade radiographic sacroiliitis, higher prevalence of cervical and thoracic spine-MRI signs, higher disease-activity and more functional impairment. REFERENCES: NIL . Acknowledgements: NIL . Disclosure of Interests: Mariagrazia Lorenzin: None declared , Giacomo Cozzi: None declared , Laura Scagnellato: None declared , Stefania Vio: None declared , Vanna Scapin: None declared , Giorgio De Conti: None declared , Amelia Carmela Damasco: None declared , Andrea Doria GSK, Astrazeneka, Roberta Ramonda Lilly, UCB, Novartis. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".