Biologics During Pregnancy in Women With Inflammatory Bowel Disease and Risk of Infantile Infections: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Biologics, such as tumor necrosis factor inhibitors, anti-integrins and anticytokines, are therapies for inflammatory bowel disease (IBD) that may increase the risk of infection. Most biologics undergo placental transfer during pregnancy and persist at detectable concentrations in exposed infants. Whether this is associated with an increased risk of infantile infections is controversial. We performed a systematic review and meta-analysis evaluating the risk of infantile infections after in utero exposure to biologics used to treat IBD. METHODS: We searched PubMed, Embase, Scopus, Web of Science, and CENTRAL from inception to June 2020 to evaluate the association of biologic therapy during pregnancy in women with IBD and risk of infantile infections. Odds ratios of outcomes were pooled and analyzed using a random effects model. RESULTS: Nine studies met the inclusion criteria comprising 8,013 women with IBD (5,212 Crohn's disease, 2,801 ulcerative colitis) who gave birth to 8,490 infants. Biologic use during pregnancy was not associated with an increased risk of all infantile infections (odds ratio [OR] 0.91, 95% confidence interval [CI] 0.73-1.14, I2 = 30%). In a subgroup analysis for the type of infection, biologic use was associated with increased infantile upper respiratory infections (OR 1.57, 95% CI 1.02-2.40, I2 = 4%). Biologic use during pregnancy was not associated with infantile antibiotic use (OR 0.91, 95% CI 0.73-1.14, I2 = 30%) or infection-related hospitalizations (OR 1.33, 95% CI 0.95-1.86, I2 = 26%). DISCUSSION: Biologics use during pregnancy in women with IBD is not associated with the overall risk of infantile infections or serious infections requiring antibiotics or hospitalizations but is associated with an increased risk of upper respiratory infections.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".