Risk of Serious Infections in Patients with Psoriasis on Biologic Therapies: An Updated Systematic Review and Meta-Analysis
Bibliographic record
Abstract
ABSTRACT Background The increase in the number of available biologic agents for psoriasis has led to an urgent need to evaluate their associated risk of serious infections to help inform clinical decisions. Methodology We systematically searched PubMed, Medline, Embase, and Cochrane databases for biologics targeting TNF (adalimumab, etanercept, infliximab, certolizumab pegol), interleukin (IL)-12/23 (ustekinumab), IL-17A (secukinumab, ixekizumab), IL-17RA (brodalumab) and IL-23p19 (guselkumab, risankizumab, and tildrakizumab) for the primary outcome serious infections, at 10– 16 weeks, 1 year and 3 years. Peto’s method (fixed effect model) was used to estimate the pooled odds ratio (OR) in meta-analyses comparing biologics with one another, methotrexate, or placebo. Results Forty-three publications (49 trials) consisting of 29,724 participants met our inclusion criteria. Serious infections across all studies were low (n=97) at 10–16 weeks and did not show an increased risk with biologic therapies compared with placebo. Seven head-to-head RCTs were identified, most of which showed no significant difference in the risk of serious infections at 10–16 weeks and at 1 year. Adalimumab was not associated with a significant increased risk of serious infections compared with methotrexate in children at 10–16 weeks. Conclusions Biologics for psoriasis were not associated with an increased risk of serious infections compared with placebo or one another at 10–16 weeks. Longer-term, real-world data with larger sample sizes are warranted.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".