Vedolizumab clearance in neonates, susceptibility to infections and developmental milestones: a prospective multicentre population‐based cohort study
Bibliographic record
Abstract
BACKGROUND: Little is known about the consequences of intrauterine exposure to, and the post-natal clearance of, vedolizumab. AIMS: To investigate the levels of vedolizumab in umbilical cord blood of newborns and rates of clearance after birth, as well as how these correlated with maternal drug levels, risk of infection and developmental milestones during the first year of life METHODS: Vedolizumab-treated pregnant women with inflammatory bowel disease were prospectively recruited from 12 hospitals in Denmark and Canada in 2016-2020. Demographics were collected from medical records. Infant developmental milestones were evaluated by the Ages and Stages Questionnaire (ASQ-3). Vedolizumab levels were measured at delivery and, in infants, every third month until clearance. Non-linear regression analysis was applied to estimate clearance. RESULTS: In 50 vedolizumab-exposed pregnancies, we observed 43 (86%) live births, seven (14%) miscarriages, no congenital malformations and low risk of adverse pregnancy outcomes. Median infant:mother vedolizumab ratio at birth was 0.44 (95% confidence interval [CI], 0.32-0.56). The mean time to vedolizumab clearance in infants was 3.8 months (95% CI, 3.1-4.4). No infant had detectable levels of vedolizumab at 6 months of age. Developmental milestones at 12 months were normal or above average. Neither vedolizumab exposure in the third trimester (RR 0.54, 95% CI, 0.28-1.03) nor combination therapy with thiopurines (RR 1.29, 95% CI, 0.60-2.77) seemed to increase the risk of infections in the offspring. CONCLUSIONS: Neonatal vedolizumab clearance following intrauterine exposure is rapid. Infant vedolizumab levels did not correlate with the risk of infections during the first year of life. Continuation of vedolizumab throughout pregnancy is safe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".