Use of antiseizure medications during pregnancy and adverse neonatal outcomes
Bibliographic record
Abstract
Background: Epilepsy during pregnancy can lead to various adverse health outcomes in both pregnant people and infants. Furthermore, in-utero exposure to antiseizure medications (ASMs), employed in the management of epilepsy, is also associated with an increased risk of adverse birth outcomes. With the exponential increase in the use of ASMs over the last few decades, it is crucial to understand the safety of in-utero exposure to ASMs. In this thesis we aimed to evaluate the safety of ASMs in all pregnant people, pregnant people with epilepsy (PPWE) and pregnant people without epilepsy (PPWOE), using both evidence synthesis methods and real-world data. Methods: First, we conducted a systematic review and meta-analysis to evaluate the risk of adverse birth weight outcomes due to in-utero exposure to ASMs in the published literature from inception to March 23rd, 2022. (Chapter 2: Protocol & Chapter 3: Report). Second, we conducted two population-based cohort studies utilizing the administrative databases in Manitoba to study the safety of in-utero exposure to ASMs and gabapentin in pregnancy. We included all pregnant people in Manitoba between 1998-2021. In study 1 (Chapter 4: ASMs) we evaluated the ASMs safety and in study 2 (Chapter 5: gabapentin) we evaluated gabapentin safety among all pregnant people, PPWE and PPWOE. Results: In the systematic review, we found a significant association between in-utero exposure to all ASMs in pregnant people and small for gestational age (SGA), with relative risk (RR) 1.33 (95% CI 1.18 to 1.50, I2 74%), low birth weight (LBW) RR 1.54 (95% CI 1.33 to 1.77, I2 67%), and decreased birth weight with a mean difference (MD) of -118.87 (95% CI -161.03 to -76.71, I2 42%) g compared to unexposed pregnant people. We found similar results among PPWE when compared to unexposed pregnant people, but they did not reach statistical significance. In our cohort study, among all pregnant people exposed to ASMs we found a significant increased risk of SGA (adjusted odds ratio [aOR] 1.16, 95% CI 1.04-1.30), LBW (aOR 1.66, 95% CI 1.47-1.88), preterm birth (aOR 1.56, 95% CI 1.41-1.73), neonatal intensive care unit (NICU) admissions (aOR 1.91, 95% CI 1.74-2.10), and length of hospital stay (LOS) infant (aOR 1.68, 95% CI 1.56-1.82) when compared with unexposed pregnant people. We found similar results among PPWOE when compared with unexposed PPWOE. For gabapentin exposure, among all pregnant people we found a significant increased risk of LBW (aOR 1.95, 95% CI 1.58-2.41), preterm birth (aOR 1.68, 95% CI 1.39-2.03), NICU admissions (aOR 2.04, 95% CI 1.71-2.42), pregnant peoples LOS (aOR 1.33, 95% CI 1.15-1.54), infant LOS (aOR 2.09, 95% CI 1.81-2.41) compared to unexposed pregnant people. We found similar results among exposed PPWOE when compared with unexposed PPWOE. Conclusions: Both the choice of ASM use and the underlying condition (epilepsy) contribute to an elevated adverse risk during pregnancy. With the increased risk of adverse neonatal outcomes associated with in-utero exposure to ASMs, including gabapentin, clinicians should carefully assess the risk-benefit ratio before prescribing these medications. Moreover, the use of gabapentin requires caution among pregnant people.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".