Use of antiseizure medications during pregnancy and adverse neonatal outcomes
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,005 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».