A247 THE IMPACT OF BIOLOGICS ON MATERNAL AND NEONATAL OUTCOMES IN PREGNANT PATIENTS WITH INFLAMMATORY BOWEL DISEASE AND THEIR ASSOCIATION WITH THERAPEUTIC DRUG LEVELS
Bibliographic record
Abstract
Abstract Background Biologic therapies are at the backbone of managing moderate to severe IBD and maintaining clinical remission. The data on the effects of biologic therapy on maternal and neonatal outcomes continues to be limited. To our knowledge, there have been no previous studies specifically investigating whether biologic therapeutic drug levels in pregnant patients are related to maternal and neonatal outcomes. Aims The objective of this study is to determine whether biologic use in pregnancy is associated with adverse maternal and fetal outcomes, and if those outcomes correlate to specific therapeutic drug levels. Methods In this study, we retrospectively evaluate patients at a high-volume pregnancy and inflammatory bowel disease centre at Mount Sinai Hospital in Toronto between January 1st, 2017 to December 31st, 2022. Primary outcomes include maternal complications, such as induction of labour, cesarean section, peripartum infections, placental abruption, preterm labour and pre-eclampsia. Secondary outcomes include neonatal complications, such as preterm birth, low birth weight (ampersand:003C2500g), congenital anomalies and the presence of neonatal infection. These outcomes will be compared to therapeutic drug levels measured in preconception, during pregnancy and post-partum. Results A total of 312 patients were included in our study, of which 152 were on biologic treatment for IBD. Of the biologics population, the majority of patients are on Remicade, followed by Humira and Stelara (Table 1). Preliminary data analysis reveals that there was no significant difference in the rate of miscarriages (10% vs. 6%, P= 0.1), cesarean section (44% vs. 38%, P=0.3) and preterm deliveries (12% vs. 14%, P=0.7) between the biologics vs. non-biologics patient groups. Next round of analysis will include secondary outcomes and therapeutic drug levels. Conclusions Through this study, we hope to add to the literature on the possible safety of maintaining biologic therapy during pregnancy in those with inflammatory bowel disease. The data on proactive therapeutic drug monitoring in pregnant patients may bring to light the importance of its inclusion in clinical decision-making algorithms. Breakdown of therapies in the biologic patient group Funding Agencies None
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".