Risk of Inflammatory Bowel Disease in Psoriasis Patients Treated with Anti-Interleukin-17 Agents: A Bayesian Metaanalysis
Bibliographic record
Abstract
Abstract Background Use of interleukin-17 inhibitors (IL-17i) in psoriasis has been associated with an increased risk of inflammatory bowel disease (IBD). However, the clinical significance of this association is not understood. Objectives To quantify the absolute risk of IBD in patients with psoriasis treated with IL-17i, stratified by known IBD risk factors. Methods Literature searches were performed to identify known IBD risk factors and the prevalences were quantified by a meta-analysis of proportions. The Bayesian model was used to estimate the probability of a new-onset or a flare of IBD in patients with psoriasis. Results The prevalence of Crohn’s disease (CD) or ulcerative colitis (UC) in the general psoriasis population was 0.0010. Use of IL-17i increased the risk of CD to 0.0037 and UC to 0.0028, translating to a number needed to harm (NNH) of 373 for CD and 564 for UC. In patients who had concomitant hidradenitis suppurativa, the use of IL-17i was associated with a decrease in NNH for CD and UC to 18 and 76, respectively, whereas for patients with a family history of IBD, the NNH values were 6 (for CD) and 10 (for UC). Conclusions In patients with no risk factors, the probability of IBD flare or onset during IL-17i treatment is negligible and additional IBD screening procedures are not indicated. In contrast, the patients with psoriasis who have hidradenitis suppurativa or first-degree family history of IBD as risk factors should be monitored for signs and symptoms of CD and UC during IL-17i therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.044 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".