Systematic review and meta‐analysis: Safety of vedolizumab during pregnancy in patients with inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND AND AIM: Vedolizumab is a novel monoclonal antibody used in patients with inflammatory bowel disease, often affecting women of child-bearing age. We aimed to compare maternal and fetal adverse outcomes in pregnancies of women with inflammatory bowel disease exposed to vedolizumab versus those on other treatment. METHODS: We performed a systematic literature search through December 2020 looking for studies including outcomes from pregnancies of female inflammatory bowel disease patients treated with vedolizumab. Our primary outcome was a composite of adverse pregnancy-related events in pregnancies of female patients on vedolizumab compared with those of disease-matched controls on other medication regimens. Events of interest included preterm births, early loss of pregnancy, late fetal death, elective termination of pregnancy, and congenital anomalies. RESULTS: Four studies were included in our review meeting criteria for our primary analysis. Compared with those with no vedolizumab exposure, pregnancies with vedolizumab exposure had an increase in overall adverse pregnancy-related outcomes (odds ratio [OR] 2.18, 95% confidence interval [CI], 1.52-3.13). The vedolizumab group also had increased preterm births (OR 2.16, 95% CI, 1.28-3.66), and early loss of pregnancies (OR 1.79, 95% CI, 1.06-3.01) but no difference in number of live births (OR 0.60, 95%CI, 0.36-1.00), or congenital malformations (OR 1.56, 95% CI, 0.56-4.37). CONCLUSIONS: Our systematic review highlights possible concern with the general safety of vedolizumab in pregnancy, as an increase in overall total unfavorable outcomes was observed. Premature births and early loss of pregnancy were also more prevalent in pregnant female patients on vedolizumab. It is possible these findings are confounded by disease activity, and further prospective cohort studies of vedolizumab and pregnancy outcomes 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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.004 | 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".