Maternal, Fetal, and Labour Outcomes of Dupilumab Use for Atopic Dermatitis During Pregnancy: A Systematic Review
Bibliographic record
Abstract
Atopic dermatitis is a chronic complex inflammatory disease that significantly impacts maternal well-being and quality of life during pregnancy, warranting effective therapeutic interventions that prioritize maternal health and fetal safety. Dupilumab is approved for moderate-to-severe atopic dermatitis, but limited data exist regarding its safety during pregnancy. We conducted a systematic review to review and analyze maternal, fetal, and labour outcomes in patients receiving dupilumab for atopic dermatitis during pregnancy. Comprehensive searches were conducted using databases including OVID, Scopus, and Web of Science, covering studies published until May 2024. Our search yielded 285 studies, of which 13 met the eligibility criteria. These studies included 68 patients with 69 pregnancies, revealing 58 live births and 11 spontaneous abortions. Dupilumab therapy was administered continuously throughout pregnancy in 22.2% of cases, while 77.8% received intermittent treatment. Maternal atopic dermatitis outcomes showed significant improvement in disease severity. Most pregnancies (86.3%) progressed without complications. Labour-associated outcomes varied, with 82.4% of women undergoing vaginal deliveries. The majority of births occurred at full term (82.5%), with a mean gestational age of 38.4 weeks. Fetal outcomes demonstrated a normal birth weight in 92.3% of cases, with no reported congenital defects. Our review suggests that dupilumab use during pregnancy is associated with improvement of atopic dermatitis and low or minimal risk of major adverse outcomes in treated patients or their newborns. Prospective studies with long-term follow-up are warranted to confirm the safety of dupilumab in this population.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".