Patient-reported outcomes in axial spondyloarthritis and psoriatic arthritis patients treated with secukinumab for 24 months in daily clinical practice
Bibliographic record
Abstract
OBJECTIVES: In patients with axial spondyloarthritis (axSpA) or psoriatic arthritis (PsA) initiating secukinumab, we aimed to assess and compare the proportion of patients achieving 6-, 12- and 24-month patient-reported outcomes (PRO) remission and the 24-month retention rates. PATIENTS AND METHODS: Patients with axSpA or PsA from 16 European registries, who initiated secukinumab in routine care were included. PRO remission rates were defined as pain, fatigue, Patient Global Assessment (PGA) ≤2 (Numeric Rating Scale (NRS) 0-10) and Health Assessment Questionnaire (HAQ) ≤0.5, for both axSpA and PsA, and were calculated as crude values and adjusted for drug adherence (LUNDEX). Comparisons of axSpA and PsA remission rates were performed using logistic regression analyses (unadjusted and adjusted for multiple confounders). Kaplan-Meier plots with log-rank test and Cox regression analyses were conducted to assess and compare secukinumab retention rates. RESULTS: We included 3087 axSpA and 3246 PsA patients initiating secukinumab. Crude pain, fatigue, PGA and HAQ remission rates were higher in axSpA than in PsA patients, whereas LUNDEX-adjusted remission rates were similar. No differences were found between the patient groups after adjustment for confounders. The 24-month retention rates were similar in axSpA vs. PsA in fully adjusted analyses (HR [95 %CI] = 0.92 [0.84-1.02]). CONCLUSION: In this large European real-world study of axSpA and PsA patients treated with secukinumab, we demonstrate for the first time a comparable effectiveness in PRO remission and treatment retention rates between these two conditions when adjusted for confounders.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".