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Record W4411429142 · doi:10.1016/j.ard.2025.05.519

POS0131 DEVELOPMENT AND VALIDATION OF A TIME-EFFICIENT SIMPLIFIED SPARCC MRI SCORE IN AXIAL SPONDYLOARTHRITIS

2025· article· en· W4411429142 on OpenAlexaboutno aff
Fatma Abdelrahman, Mohamed Mortada, Hassan Abdelwahab, E.S.A.H.F. El-Sayyad, Mohammad Abd Alkhalik Basha

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAxial spondyloarthritisNuclear medicineInternal medicineAnkylosing spondylitisSacroiliitis

Abstract

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Background: Axial spondyloarthritis (axSpA) is a chronic inflammatory condition that primarily affects the spine and sacroiliac joints (SIJs), leading to significant suffering. It falls under the broader category of spondyloarthritis, which presents a considerable diagnostic challenge owing to nonspecific symptoms and a notable lack of radiographic evidence in the early stages of the disease [1]. Magnetic resonance imaging (MRI) has become an indispensable diagnostic tool for evaluating axSpA that can detect both active inflammation and structural changes in the SIJs and spine. It is the standard imaging modality recommended for the assessment of axSpA, as supported by the ankylosing spondylitis Working Group of the International Association for the Assessment of Spine Arthritis (ASAS) and Outcome Measures in Rheumatoid Arthritis Clinical Trials (OMERACT) [2]. Various MRI scoring systems are in use, with the Spondyloarthritis Research Consortium of Canada (SPARCC) score being notably sensitive in detecting subtle changes in inflammation and correlating positively with clinical measures of disease activity [3]. Despite advancements in the management of axSpA, the absence of standardized methods for routinely assessing disease activity remains a significant challenge. The current SPARCC MRI score, although valuable, is time-consuming and depending on individual conditions. Objectives: To develop and validate a simplified SPARCC score for detecting disease activity in patients with axSpA and compare its performance with the original SPARCC score. Methods: This prospective study included 60 patients with axSpA diagnosed according to the ASAS classification criteria who were naïve to biologic DMARDs. Disease activity and physical function in axSpA patients were assessed using several indices at baseline and six months after biological therapy. Improvement was defined as either an ASDAS-Clinically Important Improvement (ASDAS-CII) (a decrease of ≥ 1.1) or ASDAS-Major Improvement (ASDAS-MI) (a decrease of ≥ 2.0). MRI examinations of SIJs and spine were performed at baseline and six months after biological therapy. The simplified SPARCC score, focusing on the three most affected slices/vertebrae instead of six, was developed and compared with the original SPARCC score. Both original and simplified SPARCC scores were correlated with clinical indices and inflammatory markers (CRP and ESR). Inter-reader agreement and diagnostic performance were evaluated to assess reliability and clinical utility. Results: The study included 60 axSpA patients with mean age of 29.78 years, with male predominance (68.3%). Median disease duration was 4 years (0.5-10). 45% of the patients had peripheral arthritis, 15% had psoriasis, 5% had inflammatory bowel disease and 3.3% had uveitis. HLA-B27 was positive in 43.3%. By comparing various disease activity markers and clinical scales at baseline and 6 months post-therapy; all parameters showed significant improvement (p<0.001) (median ESR decreased from 35.0 to 12.0, CRP from 17.5 to 2.0, BASDAI from 7.0 to 2.0, BASFI from 6.0 to 2.0, and BASMI from 4.0 to 2.0). Mean ASDAS-CRP decreased from 4.02 to 1.91 with 38.3% achieved inactive status. Both the original and simplified SPARCC scores showed significant improvement after six months of biological therapy (p<0.001). The simplified SPARCC scores demonstrated strong correlations with disease activity markers, comparable to or stronger than the original scores. BASDAI and ASDAS showed strong correlations with both systems. BASDAI correlated with original (r=0.421) and simple (r=0.413) scores at baseline. ASDAS correlated with original (r=0.419) and total scores (r=0.425). Simplified SPARCC total scores had the best diagnostic accuracy for detecting the disease activity (AUC 0.810, sensitivity 76.3%, specificity 66.7% at cut-off 9.0). Original SPARCC SIJ and total scores performed similarly (AUC 0.794, sensitivity 75%, specificity 82.7% at cut-offs 12.0 and 24.0). Spine scores showed high sensitivities (87.5-89.5%) but lower specificities (67.3%). Inter-reader agreement was substantial to almost perfect for all scores, with simplified SPARCC spine score exhibiting highest agreement (κ= 0.817 at baseline and 0.784 at follow-up) followed by simple total scores (k=0.716 baseline, k=0.744 follow-up). The simplified scoring significantly reduced assessment time (Figure 1): SIJ scoring from 16.9 ± 2.5 to 6.2 ± 1.9 minutes, spine from 14.8 ± 2.4 to 5.6 ± 1.4 minutes, and total from 37.1 ± 9.2 to 10.7 ± 2.9 minutes (all p<0.001). Figure 1 Conclusion: This study supports the construct validity of the simplified SPARCC MRI score in assessing inflammation in patients with axSpA.The application of this new score has advantages of improved inter-reader reliability and significantly reduced assessment time. REFERENCES: [1] Bittar M, Khan MA, Magrey M (2023) Axial Spondyloarthritis and Diagnostic Challenges: Over-diagnosis, Misdiagnosis, and Under-diagnosis. Curr Rheumatol Rep 25:47-55. [2] Khmelinskii N, Regel A, Baraliakos X (2018) The Role of Imaging in Diagnosing Axial Spondyloarthritis. Front Med 5:106. [3] Maksymowych WP, Wichuk S, Dougados M et al (2017) MRI evidence of structural changes in the sacroiliac joints of patients with non-radiographic axial spondyloarthritis even in the absence of MRI inflammation. Arthritis Res Ther 19:126. Table 1VariablesBaseline (n=60)Follow-up (n=60)% of improvementP-valueOriginal SPARCC SIJ22 (21.3), 4 – 566 (10.5), 0 – 2373.8<0.001Simple SPARCC SIJ19 (20.5), 4 – 366 (9.25), 0 – 2171.4<0.001Original SPARCC spine13 (16.3), 0 – 511.5 (10), 0 – 2278.8<0.001Simple SPARCC spine11.5 (15.25), 0 – 471.5 (8), 0 – 1879<0.001Original SPARCC total36.5 (36.5), 5 – 1016 (17), 0 – 4575.5<0.001Simple SPARCC total21 (25), 0 – 693 (16), 0 – 3079.1<0.001 Acknowledgements: NIL . Disclosure of Interests: None declared . © 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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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.

Opus teacher head0.026
GPT teacher head0.294
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

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Citations0
Published2025
Admission routes1
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