Incidence rates of inflammatory bowel disease in patients with psoriasis, psoriatic arthritis and ankylosing spondylitis treated with secukinumab: a retrospective analysis of pooled data from 21 clinical trials
Bibliographic record
Abstract
OBJECTIVES: Here, we present the reported incidence rates of inflammatory bowel disease (IBD) in patients receiving treatment with secukinumab for psoriasis (PsO), psoriatic arthritis (PsA) or ankylosing spondylitis (AS), in a pooled analysis of 21 clinical trials. METHODS: Data from all patients who had received at least one dose of secukinumab were included. Safety analyses were conducted to evaluate cumulative IBD rates as well as per-year rates, by indication. Crohn's disease (CD), ulcerative colitis (UC) and IBD unclassified (IBDU) events were analysed using exposure-adjusted incidence rates (patient incidence rates per 100 patient-years (PY)). RESULTS: A total of 7355 patients with a cumulative exposure of 16 226.9 PY were included in the pooled analysis. Among 5181 patients with PsO, there were 14 cases of UC, 5 cases of CD and 1 case of IBDU, with exposure adjusted incidence rates (EAIRs) of 0.13, 0.05 and 0.01, respectively. Of these 20 cases, 14 were new-onset. In 1380 patients with PsA, there were 3 cases of UC, 3 cases of CD and 2 cases of IBDU (EAIRs 0.08, 0.08 and 0.05); 7 of these represented new-onset cases. Among 794 patients with AS, there were 4 cases of UC, 8 cases of CD and 1 case of IBDU (EAIRs 0.2, 0.4 and 0.1); 9 were new-onset cases. In the per year analysis, the EAIRs for each indication did not increase over time with secukinumab treatment. CONCLUSIONS: In this pooled secukinumab safety analysis of 7355 patients across 21 clinical trials, cases of IBD events (including CD, UC and IBDU) were uncommon.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".