Safety of Vedolizumab in Pregnancy
Bibliographic record
Abstract
BACKGROUND: Vedolizumab is increasingly used as an advanced therapy for inflammatory bowel disease (IBD), with increasing use among reproductive-aged individuals. We aimed to evaluate the safety of vedolizumab during pregnancy by synthesizing recent evidence on maternal, fetal, and neonatal outcomes. METHODS: A systematic review and meta-analysis was conducted following PRISMA guidelines. 5 databases were searched from inception to June 26, 2025 for cohort studies and randomized control trials evaluating vedolizumab exposure during pregnancy in individuals with IBD. Outcomes of interest included live birth, preterm birth, early pregnancy loss, cesarean delivery, congenital malformations, small for gestational age (SGA), and composite perinatal complications. Random-effects meta-analyses were performed using the restricted maximum likelihood estimator. RESULTS: Eight cohort studies (5 prospective, 3 retrospective) met inclusion criteria. Vedolizumab exposure was associated with increased odds of preterm birth (pooled OR=1.33; 95% CI: 1.12-1.59; I²=74.1%) and cesarean delivery (OR=1.27; 95% CI: 1.03-1.57; I²=0%), but not with early pregnancy loss (OR=0.96; 95% CI: 0.54-1.71), congenital malformations (OR=1.42; 95% CI: 0.86-2.34), SGA (OR=1.10; 95% CI: 0.70-1.73), or composite perinatal complications (OR=2.00; 95% CI: 0.98-4.10). CONCLUSIONS: Vedolizumab exposure during pregnancy in individuals with IBD was not associated with increased odds of early pregnancy loss, congenital malformations, or composite perinatal complications. However, increased odds of preterm birth and cesarean delivery were observed, potentially reflecting underlying disease severity.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".