Real-World Effectiveness and Safety of Dupilumab, Tralokinumab, and Upadacitinib in Patients with Atopic Dermatitis: A 52-Week International, Multicenter Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Evaluating the real-world effectiveness, safety, and tolerability of targeted biologic and non-biologic therapies in patients with atopic dermatitis (AD) treated in routine clinical practice remains crucial. In this international, multicenter, retrospective, comparative study we aimed to evaluate the 52-week effectiveness, safety, and tolerability of dupilumab, tralokinumab, and upadacitinib in patients with AD aged ≥ 12 years. METHODS: Effectiveness was assessed at weeks 16, 24, and 52 using Eczema Area and Severity Index (EASI) and itch Numerical Rating Scale (NRS) scores. Safety was measured via adverse events (AEs). RESULTS: A total of 1286 treatment courses were included: 62.5% received dupilumab, 24.3% received upadacitinib, and 13.1% received tralokinumab. Upadacitinib demonstrated higher effectiveness than dupilumab and tralokinumab across all time points and most evaluated outcomes both on the overall population and the biologic-/JAKi-naïve population, including stringent treatment targets such as EASI 90 response and combined EASI 90 + itch NRS 0/1 response. While upadacitinib demonstrated superior effectiveness, it was associated with a higher incidence of AEs, both leading to and not leading to treatment discontinuation, including thromboembolic events, lipid abnormalities, and hematologic abnormalities. In contrast, conjunctivitis was the most frequently observed AE among patients receiving biologics. CONCLUSION: This study provides a comprehensive real-world comparison of dupilumab, tralokinumab, and upadacitinib in AD, highlighting upadacitinib's superior effectiveness in achieving stringent treatment targets, both in the short and long term, but also a higher incidence of AEs. However, the considerable heterogeneity of the study population, an inherent limitation of real-world studies, must be acknowledged when interpreting these findings.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".