Systematic review and network meta-analysis of the risk of Herpes zoster with biological therapies and selective Janus kinase-1 inhibitors in atopic dermatitis
Bibliographic record
Abstract
Introduction: Atopic dermatitis (AD) patients have an increased risk of herpes zoster (HZ). The relationship of dupilumab, tralokinumab, upadacitinib, and abrocitinib to HZ incidence in AD patients remains unclear. Aim: To evaluate and compare the incidence and risk of HZ among patients with moderate to severe atopic dermatitis treated with advanced systemic therapies. Material and methods: Systematic searches were conducted in Ovid Medline and Embase. The primary outcome was incidence of HZ in patients with moderate to severe AD receiving placebo or the aforementioned treatments. A frequentist random-effects NMA was conducted with odds ratio. Results: Our search identified 16 trials comprising 10,689 patients. Upadacitinib was associated with a dose-dependent increase in the incidence of HZ compared to placebo (OR = 2.55 [1.09, 5.95] and (OR = 4.29 [1.89, 9.74], respectively) and compared to various dupilumab doses (OR = 4.48 [1.29, 15.57], 3.61 [1.28, 10.18] and 7.54 [2.21, 25.68], 6.09 [2.24, 16.52], respectively). Upadacitinib 30 mg was associated with a higher incidence of HZ when compared to upadacitinib 15 mg (OR = 1.68 [1.19, 2.38]). Abrocitinib 200 mg was associated with a higher increase in HZ compared to placebo (OR = 3.34 [1.34, 8.31]). According to SUCRA ranks, both JAK-1 inhibitors had a higher cumulative incidence of HZ compared to dupilumab. Conclusions: JAK-1 inhibitors are associated with a significantly higher incidence of HZ compared to dupilumab and placebo. Our results suggest that recombinant HZ vaccination should be highly considered for all adult patients prior to starting oral JAK-1 inhibitors.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".