Predictors of Secukinumab Treatment Response and Continuation in Axial Spondyloarthritis: Results From the EuroSpA Research Collaboration Network
Bibliographic record
Abstract
OBJECTIVE: In patients with axial spondyloarthritis (axSpA) initiating secukinumab (SEC), we aimed to identify baseline (treatment start) predictors of achieving low disease activity (LDA) after 6 months, as measured by the Axial Spondyloarthritis Disease Activity Score using C-reactive protein (ASDAS-CRP) and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), as well as treatment continuation after 12 months. METHODS: From 11 European registries, patients with axSpA who initiated SEC treatment in routine care, with available data on 6-month ASDAS-CRP and BASDAI assessments were included. Logistic regression analyses on multiply imputed baseline data were performed; potential baseline predictors included demographic, diagnosis, lifestyle, clinical, and patient-reported variables. RESULTS: In a pooled cohort of 1174 patients with axSpA, 5 of 19 potential assessed variables were mutually predictive for achieving LDA by ASDAS-CRP and BASDAI: higher physician global assessment score, noncurrent smoking, lack of prior exposure to biologic/targeted synthetic disease-modifying antirheumatic drugs, and lower Health Assessment Questionnaire scores and BASDAI scores. Moreover, radiographic axSpA and CRP ≤ 10 mg/L were associated with achieving ASDAS-CRP LDA, and HLA-B27 positivity and history of psoriasis with achieving BASDAI LDA, whereas earlier time of secukinumab initiation (2015-2017) was associated with treatment continuation. CONCLUSION: In this European real-world study of patients with axSpA initiating SEC, predictors of achieving LDA by ASDAS-CRP and BASDAI at 6 months and remaining on treatment at 12 months included both clinical, patient-reported, and lifestyle factors, underscoring the complex mechanisms of real-world drug effectiveness.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".