{"paper_id":"0e66fd5c-38b5-47eb-8a2d-7cd0fa12d155","body_text":"Adverse Events During Pregnancy Associated with Third Generation Antiseizure Medications: A Real-World Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adverse Events During Pregnancy Associated with Third Generation Antiseizure Medications: A Real-World Analysis Lan-fang Li, Xiu-mei Wang, Xue-Yan CUI, Jing PENG, Xiaolei REN, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7930539/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The third generation antiseizure medications (ASMs) have been widely used in clinical practice due to their safety and effectiveness. Given the scarcity of research on the risks of prenatal exposure to third generation ASMs for maternal and fetal, certain challenges persist. This study investigates the frequency and rate of pregnancy-related adverse events (PRAEs) associated with third generation ASMs in the FDA Adverse Event Reporting System (FAERS) database. Methods The adverse events (AEs) of ASMs were selected from FAERS database. The reporting odds ratio (ROR), proportional reporting ratio (PRR) and bayesian confidence propagation neural network (BCPNN) were utilized to conduct disproportionality analysis. Additionally, subgroup analyses were conducted to compare the PRAEs between the newer ASMs and reference drugs. Results In total, 48,321 AEs were identified for third-generation ASMs, of which 1,162 reports (2.5%) were PRAEs. The most common PRAEs were abortion spontaneous (33.0%), premature baby (18.3%) and premature delivery (14.4%). Disproportionality analysis revealed ten signals for zonisamide​​, nine signals for lacosamide​​, four signals for brivaracetam, three signals for perampanel​​ or ​​rufinamide. The three most frequently reported HLT were “abortion spontaneous”, “gestational age and weight conditions” and “labour onset and length abnormalities”. The strongest PT signals were placental infarction for zonisamide (ROR = 115.4, 95% CI: 56.9–234.0), postmature baby for brivaracetam (ROR = 83.1, 95% CI: 26.5–261.1), and stillbirth for rufinamide (ROR = 83, 95% CI: 50.5–136.4). Notably, we revealed that zonisamide​​​​ was related with placental abnormalities, including placental disorder and infarction. These disproportionality signals varied across different subgroup analyses.​ Conclusions In comparison with​​ second-generation ASMs, newer ASMs demonstrate a comparable safety profile in pregnant women with epilepsy (PWWE). However, caution must be exercised in clinical medication, with close attention paid to the adverse reactions indicated by the risk signals of each drug. Additional prospective cohort studies should be conducted to validate these findings. antiseizure medications pregnant women with epilepsy PRAEs FAERS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Epilepsy, as one of the most common neurological disorders, affects the health of 65 million individuals worldwide. Women with epilepsy comprise almost half of the population with general epilepsy, account for approximately 0.5% to 1.0% of all pregnant women 1 . Studies indicate that 23–29% of PWWE had breakthrough or worsening seizures during their pregnancy 2 . In general, about 20–30% of patients with epilepsy require newer ASMs to achieve seizure control. Over the past decades, several ASMs have been introduced. While first- and second-generation ASMs demonstrate efficacy, they are limited by tolerability issues and pharmacokinetic disadvantages 3 – 6 . For PWWE, the safety profile of ASMs is critically important for both the mother and the developing fetus. Appropriate ASMs therapy improving maternal outcomes while minimizing seizure risk and reducing potential fetal/maternal harm to the greatest extent possible remains a therapeutic dilemma. With the approval of newer ASMs, PWWE exposure to various ASMs has become increasingly common 7 . Compared to pregnant women without epilepsy or those not exposed to ASMs, they may face higher risks of adverse pregnancy outcomes and neonatal complications, including preterm birth, C-section, and fetal loss (including stillbirth, miscarriage, spontaneous abortion, or induced abortion) 8 – 10 . Although newer ASMs offer advantages in tolerability and drug interactions,​ safety data remains limited, particularly regarding adverse pregnancy and neonatal risks, so they cannot be confidently recommended for PWWE at this time 11 . Safety studies of third-generation ASMs often prioritize fetal outcomes over maternal risks 12 . There remains a critical gap in research addressing maternal adverse effects, such as gestational hypertension, metabolic alterations, and long-term health impacts. Given the extensive application of third-generation ASMs, the lack of robust evidence linking these ASMs to pregnancy and obstetric outcomes highlights an urgent need for future research. Large-scale randomized clinical trials on the safety of ASMs in PWWE were not avaliable. There were existing six epilepsy-pregnancy registries, with West China Registry recently initiated. These registries are a primary source of ASMs safety information during pregnancy. However, they primarily assess the risk of major congenital malformations in offspring following in utero exposure to ASMs, and the Chinese registry is constrained by its short duration of operation. In contrast, pharmacovigilance data can serve as a valuable source of ASMs. Spontaneous reporting systems (SRSs) are a widely used pharmacovigilance method globally. Currently, the main SRSs include Vigibase, FAERS, and others. Due to its large data volume and easy accessibility, along with the fact that third-generation ASMs received market approval later in China with insufficient local data, we selected FAERS to mine and analyze the PRAEs caused by newer ASMs. This study investigated PRAEs associated with newer ASMs submitted to FAERS and compared the potential disproportionality signals with lamotrigine, levetiracetam and oxcarbazepine (reference drugs which were generally recognized as relatively safe during pregnancy). This study provides comprehensive data on the safety profiles of third-generation ASMs for both PWWE and fetuses, addressing the gap in existing research that predominantly focuses on fetal safety alone. MATERIALS AND METHODS Data Sources Data were obtained from the FAERS public dashboard from January 1, 2024 to June 31, 2024. The study included the following third-generation ASMs: brivaracetam (BRV), zonisamide (ZNS), lacosamide (LCM), rufinamide (RUF), perampanel (PER), eslicarbazepine (ESL), cannabidiol (CBD), tiagabine (TGB). Other third-generation ASMs were excluded for the following reasons: stiripentol, clobazam and fenfluramine are approved for specific epilepsy syndromes, such as Dravet and Lennox–Gastaut and therefore are not appropriate for our study. Vigabatrin has been warned permanent vision loss and indicated for the treatment of infantile spasms, ezogabine and felbamate have been withdrawn from the market, cenobamate and fenfluramine were not sufficient for a statistical analysis because of its recent market approval. The specific approval dates and mechanisms of action of the drugs are provided in supplementary table 1 . To reduce the risk of misleading relationship between drugs and AEs, only reported cases defined as AEs in which the reporter identified the target drug as the “primary suspect” and “secondary suspect”. AEs were standardized and classified using HLTs and System Organ Classes (SOCs) from the Medical Dictionary for Regulatory Activities (MedDRA), Version 26. The SOC “pregnancy, puerperium and perinatal conditions” was included, while cases pertaining to breastfeeding, male patients and normal conditions were excluded. Duplicate records were removed as previously described 13 . Data sources are publicly available and therefore do not require ethical approval. Data analysis Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and the Bayesian Confidence Propagation Neural Network (BCPNN) are methods commonly used in pharmacovigilance for detecting AE signals. ROR is a consistent estimator of the odds ratio (i.e., the risk ratio) and exhibits less bias than other methods. The PRR can estimate relative risk but is prone to false-positive signals. The BCPNN employs a supervised neural network learning approach, utilizing known ADRs as a training set for machine learning, which remains relatively stable even with a limited number of reports. Therefore, this study integrated the ROR, PRR, and BCPNN methods to mine the AE signals for the ASMs. All three approaches are based on the 2×2 contingency table. Their calculation formulas and threshold criteria for risk signals are detailed in supplementary table 2. Generally, higher ROR or information component (IC) values indicate stronger drug-AE associations.​​ To better investigate potential gestational risks of third-generation ASMs, subgroup analyses were stratified by comparator groups: First, comparing third-generation ASMs with all other drugs; second, intra-class comparisons within third-generation ASMs; finally, given the favorable gestational safety profiles of second-generation ASMs—levetiracetam (LEV), oxcarbazepine (OXC), and lamotrigine (LTG)—these agents were selected as reference comparators versus third-generation ASMs.​​ All data were processed using R software (version 4.0.5) and microsoft excel. Graphs were generated with GraphPad Prism 9.5. Heatmap was plotted by https://www.bioinformatics.com.cn (last accessed on 10 Dec 2024), an online platform for data analysis and visualization 14 . RESULTS 3.1 Demographics A total of 48,321 AEs of third-generation ASMs were submitted to the FAERS database from Q1 2004 to Q2 2024. Among these, only 1,162 AEs (2.4%) were classified under the SOC “Pregnancy, Puerperium and Perinatal Conditions”. A total of 507 reports contained age information: 254 reports (21.9%) were aged 18–35, and 121 reports (10.4%) were aged 35–50. There were 952 reports (81.9%) submitted by professions. The occurrence years of AEs were predominantly concentrated in 2014 to 2024 (accounting for 89.1%). The country distribution of reports is depicted in Fig. 1 A and the European ranked first in number, with 873 cases. The annual number of reported cases of PRAEs for ASMs is shown in Fig. 1 B. The primary indications for ASMs were epilepsy (749 cases, 65%), followed by reports with unknown indication information (320 cases, 27.5%). Our results indicated that the most common serious outcome was congenital anomaly (10.8%) and death (3.3%), while mild and moderate outcome including hospitalization, required intervention and others account for the majority (83.7%) of all cases. In terms of final outcome, ZNS had the highest mortality rate (4.8%) followed by LCM (3.8%). In the study cohort, TGB showed the highest incidence of congenital malformations (20%), with PER ranking second (17.2%). Further details were presented in Table 1 . Among all AEs associated with ASMs, the incidence rates of PRAEs ranged from 1.4% for ESL to 5.4% for RUF, and their distribution were shown in Fig. 1 C. Overall, the PRAEs accounted for a small proportion of the total AEs associated with third-generation ASMs. Table 1 Demographic information reported by PRAEs for ASMs in FAERS Characteristic BRV (n = 93) CBD (n = 4) ESL (n = 35) LCM (n = 646) PER (n = 67) RUF (n = 36) TGB (n = 11) ZNS (n = 270) Age, year 0–18 10 0 0 69 6 5 2 40 18–35 19 1 9 129 12 4 3 77 35–50 13 2 1 52 10 19 3 21 Unknown 51 1 25 396 39 8 3 132 Reporter health-professional 82 0 21 539 64 34 5 207 Consumer 11 4 14 103 3 2 4 40 Unknown 0 0 0 4 0 0 2 23 Reporting year 2002–2007 0 0 0 0 0 0 4 15 2008–2013 0 0 1 56 0 0 3 48 2014–2019 14 0 27 255 7 19 4 65 2020–2024 79 4 7 335 60 17 0 142 Reporting region Europe 74 0 24 492 54 36 0 193 North America 14 4 10 98 3 0 8 60 South America 5 0 1 9 0 0 0 0 Asia 0 0 0 41 10 0 0 6 Oceania 0 0 0 6 0 0 0 1 Unknown 0 0 0 0 0 0 4 10 Indication Epilepsy(Tuberous sclerosis complex, Sturge-Weber syndrome) 47 1 21 449 27 31 4 179 Foetal exposure during pregnancy 5 0 0 33 10 1 1 24 Psychiatric disorders(Affective disorder,Schizophrenia,Generalised anxiety disorder) 0 2 0 0 0 0 4 0 Migraine 0 0 0 0 0 0 0 4 Unknown 41 1 14 164 30 4 2 64 Reaction outcome Life-Threatening 6 0 0 11 6 0 0 4 Hospitalization 24 0 5 78 28 0 2 44 Disability 0 1 0 1 0 0 0 0 Death 1 0 1 27 1 0 0 14 Congenital Anomaly 15 0 5 78 17 1 3 23 Required Intervention 0 0 0 0 0 0 1 7 Other Serious 80 3 27 513 47 36 9 198 3.2 Signals associated with the third-generation ASMs compared with all the other drugs In this study, the PRAEs signals were detected by ROR, PRR, and BCPNN. The top 5 PTs were abortion spontaneous, premature baby, premature delivery, low birth weight baby, and stillbirth, involving 896 reports, which accounted for 77.5% of PRAEs. These 27 PTs could be categorized into 15 HLTs and 6 High-Level Group Terms (HLGTs), with the main SOC “pregnancy, puerperium and perinatal conditions” (Fig. 2 A). ​​When comparing the third-generation ASMs with all the other drugs in terms of HLT classification, a total of 10 AE signals were obtained. The top 5 frequently reported AEs associated with these ASMs were: abortions spontaneous, gestational age and weight conditions, labour onset and length abnormalities, stillbirth and foetal death, and foetal growth complications (Table 2 ). Among these, neonatal metabolic and endocrine disorders had the highest signal with 7 cases (ROR = 18.57; 95% CI, 8.66–39.81), followed by foetal complications (ROR = 5.99; 95% CI, 3.2–11.2) and maternal complications of delivery (ROR = 5.97; 95% CI, 2.46–14.46). Table 2 Signal strength of PRAEs for ASMs at the High Level Terms (HLTs) level HLT n ROR(95%CI) PRR x2 IC025 Adverse event Abortions spontaneous 384 3.42(3.09,3.78) 3.40 644.06 1.58 Yes Gestational age and weight conditions 347 2.84(2.55,3.15) 2.82 405.52 1.34 Yes Labour onset and length abnormalities 201 3.43(2.99,3.95) 3.42 341.58 1.54 Yes Stillbirth and foetal death 89 2.69(2.18,3.32) 2.69 93.59 1.15 Yes Foetal growth complications 33 1.77(1.26,2.49) 1.77 10.99 0.58 Yes Pregnancy complicated by maternal disorders 22 1.87(1.23,2.84) 1.87 8.79 0.59 Yes Hypertension associated disorders of pregnancy 19 1.47(0.94,2.31) 1.47 2.87 0.35 No Placental abnormalities (excl neoplasms) 17 4.98(3.08,8.04) 4.98 53.17 1.42 Yes Abortions not specified as induced or spontaneous 11 1.31(0.72,2.37) 1.31 0.80 0.21 No Foetal complications 10 5.99(3.2,11.2) 5.99 40.79 1.37 Yes Neonatal metabolic and endocrine disorders 7 18.57(8.66,39.81) 18.56 109.76 1.93 Yes Haemorrhagic complications of pregnancy 5 1.15(0.48,2.77) 1.15 0.10 0.08 No Maternal complications of delivery 5 5.97(2.46,14.46) 5.97 20.28 1.05 Yes Amniotic fluid and cavity disorders of pregnancy 3 1.06(0.34,3.28) 1.06 0.01 0.02 No Maternal complications of pregnancy 3 3.02(0.97,9.42) 3.02 4.02 0.51 No Given the different pharmacological mechanisms of third-generation ASMs, we conducted disproportionality analysis of PRAEs for each drug (Table 3 ). Among them, the most frequently reported PT were abortion spontaneous (381,33%), premature baby (212,18.3%), premature delivery (166,14.4%), low birth weight baby (70,6.1%) and stillbirth (67,5.8%). A total of 17 positive signals were obtained for ZNS, 16 for LCM and 8 for BRV. Hypothermia neonatal is the most prominent signals for LCM, as well as placental infarction for ZNS and stillbirth for RUF. The detailed information was shown in Table 3 and Fig. 2 B. We next analyzed all signals at the HLT level. There were 6 ASMs with strong signals for “abortions spontaneous” and “gestational age and weight conditions” (Fig. 3 ). LCM was the only one shown positive correlation with the “foetal complications” (ROR 16.46; 95% CI, 8.8-30.77), “maternal complications of delivery” (ROR 16.39; 95% CI, 6.76–39.71), and “neonatal metabolic and endocrine disorders” (ROR 50.99; 95% CI, 23.78-109.34). Besides, ZNS indicated disproportionality signals of “amniotic fluid and cavity disorders of pregnancy”, “haemorrhagic complications of pregnancy”, and “placental abnormalities (excl neoplasms)”. Moreover, RUF showed the strongest significant correlation with stillbirth and foetal death. Our findings revealed that ZNS and LCM both shown ten disproportionality signals compared with all the other drugs. Further details can be found in Table 4 . Table 3 The signals detection in terms of PTs of 8 ASMs PT n ROR(95%CI) LCM Abortion spontaneous 215 Premature baby 123 Premature delivery 110 Low birth weight baby 43 Stillbirth 35 Small for dates baby 25 Hydrops foetalis 10 Gestational diabetes 9 Abortion 8 Hypothermia neonatal 7 Premature rupture of membranes 6 Retained placenta or membranes 5 Postmature baby 4 Threatened labour 4 Hyperemesis gravidarum 3 Foetal macrosomia 3 ZNS Abortion spontaneous 65 Premature baby 42 Small for dates baby 22 Low birth weight baby 19 Foetal growth restriction 19 Premature delivery 18 Stillbirth 16 Foetal death 11 Gestational diabetes 10 Placental disorder 9 Pre-eclampsia 8 Placental infarction 8 Premature labour 7 Threatened labour 5 Premature separation of placenta 5 Abortion threatened 3 Polyhydramnios 3 BRV Abortion spontaneous 30 Premature baby 24 Premature delivery 22 Low birth weight baby 5 Gestational diabetes 3 Postmature baby 3 Prolonged pregnancy 3 Abortion 3 ESL Abortion spontaneous 22 Foetal growth restriction 4 PER Abortion spontaneous 28 Premature baby 18 Premature delivery 13 Small for dates baby 5 RUF Stillbirth 16 Abortion spontaneous 14 Small for dates baby 6 TGB Premature baby 5 Premature delivery 3 Table 4 The signals detection in terms of HLTs of ASMs in different subgroup 3.3 Disproportionality analysis of related AEs for specific ASMs compared with all the other third-generation ASMs To ​​compare the differences in safety during pregnancy among third-generation ASMs, we recalculated the RORs for PRAEs of all those newer ASMs. Upon conducting secondary analyses, only four ASMs including LCM, ZNS, BRV and RUF indicated PRAE signals. BRV, PER, and ESL showed no risk signals for spontaneous abortion in terms of ROR or PRR. Similarly, no risk signals were detected for “gestational age and weight conditions” with BRV, PER, RUF and TGB. Compared to the primary analysis, ZNS, PER and TGB showed no risk signals for “labour onset and length abnormalities”. However, LCM, ZNS, and RUF consistently demonstrated risks for “stillbirth and foetal death”. Further details regarding the secondary analysis can be found in Table 4 . Figure 4 presents the subgroup analysis results using the ROR and BCPNN method. 3.4 AEs during pregnancy for third-generation ASMs compared with the second-generation ASMs Among second-generation ASMs, LEV, LTG, and OXC were commonly used for the treatment of PWWE due to their favorable safety profiles 15 – 17 . The analysis uncovered significant signals when comparing LCM with LEV and LTG, including “abortions not specified as induced or spontaneous (ROR 0.88; 95% CI, 0.43–1.81)”, “abortions spontaneous (ROR 0.7; 95% CI, 0.61–0.81)”, “foetal complications (ROR 0.62 95% CI, 0.33–1.18), “gestational age and weight conditions (ROR 0.83; 95% CI, 0.72–0.96)”. ZNS did not show significance compared to LEV in “foetal growth complications” and “stillbirth and foetal death”. Moreover, ZNS demonstrates a superior safety profile compared to LEV in terms of pregnancy-related complications and prolonged labor. Notably, the BCPNN method revealed unique risk signals for LCM specifically associated with neonatal metabolic and endocrine disorders, and maternal complications of delivery (Fig. 5 ). Additionally, no disproportionality signals were identified for BRV, PER, RUF, ESL, or TGB, due to an insufficient number of AE cases. Details were given in supplementary table 3. DISCUSSION As first-choice of treatment for epilepsy, ASMs require a relatively long course of administration. Therefore, the safety of ASMs for mothers and developing fetuses during pregnancy has always been a focus of attention, and sufficient safety information are urgently needed, especially for the third-generation ASMs. This real-world study comprehensively evaluates the similarities and differences in the gestational safety profiles across various categories of third-generation ASMs. In this study, the two drugs with the highest number of PRAEs were LCM (n = 646) and ZNS (n = 270). This observation may be attributable to the higher usage rates of these two drugs during pregnancy.​​ A separate MONEAD study 7 reported the most frequently used monotherapies of third-generation ASM in pregnancy was ZNS (5.0%). The most common polytherapy regimens were LCM + LEV (6.5%), and LTG + ZNS (5.2%). LCM is partly eliminated by the kidneys (approximately 40%) and also partly metabolized by CYP2C19 and demonstrates a significant advantage in maintaining pharmacokinetic stability before and during pregnancy compared with LEV or LTG 18 – 19 . However, more consistent data come from MONEAD study 20 , reporting a decrease of dose-normalized concentrations during pregnancy of 39.9% for LCM. In this study, LCM demonstrated risks for miscarriage, hyperemesis gravidarum, threatened preterm labor, premature rupture or retention of membranes, and gestational diabetes in PWWE. Fetal risks included abnormalities in gestational age and birth weight, stillbirth, hydrops fetalis, and neonatal hypothermia. Although some studies 21 – 22 suggest no association between LCM and small for gestational age (SGA) or microcephaly, this conclusion remains premature due to limited sample sizes. In our study, 25 cases of SGA were reported with an ROR of 4.8 (95% CI: 3.24–7.11), indicating a significant risk signal. Two pharmacovigilance reports 23 – 24 of LCM align with these findings, showing fetal loss in 23–26% of exposed pregnancies.​​ Moreover, LCM showed a risk difference for preterm birth of 4.6% and for cesarean delivery of 9.7% 23 . Notably, preterm birth emerged as a risk signal for LCM in this study, warranting clinical vigilance. LCM also generated a unique risk signal for hypothermia neonatal among ASMs—unreported in prior human studies but consistent with dose-dependent hypothermia observed in rat models when LCM was administered at analgesic doses 25 . Consequently, vigilance regarding hypothermia risks to the fetus is warranted during LCM therapy. The UK and Ireland epilepsy and pregnancy register 26 have found among 112 patients exposed to ZNS during the first trimester of pregnancy, the major congenital malformation (MCM) rate was 13%, while spontaneous abortions and preterm births accounted for 21%. Our study revealed that congenital malformations accounted for 8% of ZNS in real world. Abortion spontaneous and premature baby were the most commonly reported PTs, with a total of 107 cases documented, representing 39.6% of all PRAEs. These findings highlight concerns about the potential teratogenicity of ZNS. However, only 130 cases across four available studies 10 to date noted severely limited pregnancy exposure data for ZNS- underscoring the need for further safety evaluations. ZNS was associated with SGA in a meta-analysis 8 . Hernández-Díaz S et al. 27 demonstrated that ZNS induces significant weight reduction, neonates born to ZNS-exposed group showed a mean birth weight reduction of 202g and a 12.2% incidence of height deficiency (> 1cm below standard). These findings corroborate the risk signal for SGA identified in our study with ZNS. Notably, ZNS was associated with placental disorders (n = 9 reports), representing a unique risk signal not previously documented in ​earlier findings. The mechanism remains unclear, may be associated with its high rate of transplacental transfer (reported up to 92%) 28 . BRV has risk signals for 8 PTs and it exhibits stable pharmacokinetics during pregnancy and a generally favorable safety profile. In vitro studies show no cytotoxicity in embryos after exposure to BRV 29 . Three pregnancies exposed to BRV, their newborns did not possess MCMs 30 . However, two infants presented with three minor congenital anomalies. Our study also found the teratogenic risks of BRV, which represented 18% of the outcomes. PER has risk signals for 4 PTs and the most significant ROR signal was spontaneous abortion. Alicino AM et al. 31 reported 96 pregnancies treated with PER, 28 pregnancies (29.2%) did not reach term, including induced abortion and spontaneous. Two infants exposed to PER monotherapy had low Apgar scores 32 , our study also detected risk signals for preterm birth and SGA. No controlled studies have investigated the pharmacokinetics of PER in pregnancy. Although a ​​3–4 fold reduction in PER exposure was documented in the third trimester​​ (weeks 28–40), pharmacokinetic analyses 33 demonstrated ​​minimal fluctuation in PER plasma concentrations across all trimesters. ESL generated two HLTs risk signals. Although ESL was associated with MCMs in case reports 34 , all documented cases involved concurrent risk factors or co-administered medications. Of 15 spontaneous abortion cases, 11 occurred with ESL exposure - a finding 34 consistent with our study. Despite detected risk signals for spontaneous abortion and premature delivery with RUF and TGB, no case reports substantiating these associations have been published to date. Similarly, for CBD, neither pharmacokinetic studies in pregnant individuals nor comprehensive data in healthy human cohorts are currently available. Studies of larger populations are needed to elucidate if there are any pharmacokinetic changes in the more recently approved medications during pregnancy. This study has several limitations: First, as we focused solely on newer ASMs, the low reporting numbers in databases may introduce selection bias. Given their recent market approval, potential adverse reactions might remain unreported or undetected, underscoring the risk signal. Second, substantial missing age data precluded stratified subgroup analysis by age demographics. Additionally, limited sample size prevented adjusting for confounders such as drug interactions and dosage effects. Third, the majority of AE reports originated from the US and Europe, lacking Asian population data; consequently, the impact of polymorphisms (e.g., CYP enzyme variants) on gestational drug exposure cannot be excluded. Finally, the AE risk signals only represent statistical associations- confirming the safety profiles of third-generation ASMs during pregnancy requires validation through large-scale, prospective clinical studies. CONCLUSIONS In summary, given the widespread use of third-generation ASMs, this study provides a comprehensive safety assessment of their gestational, perinatal, and puerperium profiles. The findings offer critical reference guidance for physicians choosing ASMs during pregnancy, particularly highlighting risk signals identified in contrast to second-generation ASMs. Further studies are required to validate and confirm the hypotheses generated from this study. Declarations Ethics approval and consent to participate Patient records in the FAERS database are anonymized and de-identified; therefore, Consent to Participate declaration: not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated and analysed during the current study are available in the FAERS database ,[https://www.fda.gov/drugs/surveillance/fdas-adverse-event-reporting-system-faers]. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Science and Technology Development Program of Medical and Health from Shandong Province (202102040505). Author Contributions Lan-Fang Li: Conceptualization (Lead); Formal Analysis (Lead); Writing –Original Draft Preparation (Equal). Xiu-Mei Wang: Conceptualization (Supporting); Data Curation (Lead). Xue-Yan CUI: Writing – Review and Editing (Supporting); Jing PENG: Methodology (Equal). Xiao-Lei REN: Methodology (Equal); Software (Lead). Mei-Xia WANG: Resources (Equal). Qing-Xia Kong: Conceptualization (Equal); Writing – Original Draft (Supporting); Validation (Equal). Hui-Xian Zhang: Conceptualization (Lead); Funding Acquisition (Lead); Supervision (Lead); Visualization (Lead); Writing – Review & Editing (Lead). All authors contributed to the article and approved the submitted version. Acknowledgements We thank Wenzhe Chen (Information Center, Affiliated Hospital of Jining Medical University.) for providing computation support. References Razaz N, Igland J, Bjørk MH, Joseph KS, Dreier JW, Gilhus NE, Gissler M, Leinonen MK, Zoega H, Alvestad S, Christensen J, Tomson T. 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Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplmentlyTable.docx Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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A Distribution of PRAES across continents and countries​; B Annual PRAEs reports for 8 drugs; C Proportions of PRAEsand their distribution in ASMs at HLTs level.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7930539/v1/602c4766600afafaaa9d12b5.png\"},{\"id\":96243728,\"identity\":\"35dd0a12-5d7e-4ba2-9928-82cd397af405\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:16:55\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":350265,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eScanning PRAEs associated with ASMs on the FAERS database. A The sankey diagram illustrates the hierarchical relationship of PTs for 27 categories of PRAEs caused by ASMs based on MedDRA. B This network diagram presents PT-positive signals for 8 ASMs, with larger circles indicating a greater number of reports.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7930539/v1/1791fe6563dd1175d755f92a.png\"},{\"id\":96243537,\"identity\":\"3f43acfa-7504-4f07-8b09-8ebd569a92d6\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:16:37\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":217488,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSignal detection results of PRAEs in terms of HLT and ROR (95%CI).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7930539/v1/caa7f2bc7cdee366988fbaed.png\"},{\"id\":96243098,\"identity\":\"3718fd67-cc90-4c9a-b7b9-39a17621da86\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:15:31\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":165350,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDisproportionality analysis of PRAEs for specific ASMs compared with all the other third-generation ASMs at HLTs level based on ROR and BCPNN method.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7930539/v1/0065ced886edb4582fa258c3.png\"},{\"id\":95914554,\"identity\":\"e2d2b5ed-81a2-4bf4-865d-e74866c7eb2f\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 11:16:35\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":173768,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSignal detection results of third-generation ASMs compared to LEV, LTG and OXC using BCPNN method.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7930539/v1/abb79b1a466e42d23c83ddd7.png\"},{\"id\":98622006,\"identity\":\"a0c37b5a-a698-4d20-9a83-19af998da89e\",\"added_by\":\"auto\",\"created_at\":\"2025-12-19 16:40:26\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2262305,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7930539/v1/7226d061-3014-48dd-89ea-eb3945cad8eb.pdf\"},{\"id\":95914547,\"identity\":\"b0e7030f-5122-47ce-85ad-4e6fe6cb171e\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 11:16:35\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":35184,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplmentlyTable.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7930539/v1/532a96078bb9cd3d52573f40.docx\"},{\"id\":95914544,\"identity\":\"eec8508b-a808-45cf-ac9c-7b4c24c56f0c\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 11:16:35\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":73153,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Tables.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7930539/v1/258872dce594ea26f2eb1a25.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Adverse Events During Pregnancy Associated with Third Generation Antiseizure Medications: A Real-World Analysis\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eEpilepsy, as one of the most common neurological disorders, affects the health of 65\\u0026nbsp;million individuals worldwide. Women with epilepsy comprise almost half of the population with general epilepsy, account for approximately 0.5% to 1.0% of all pregnant women\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e. Studies indicate that 23\\u0026ndash;29% of PWWE had breakthrough or worsening seizures during their pregnancy\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e. In general, about 20\\u0026ndash;30% of patients with epilepsy require newer ASMs to achieve seizure control. Over the past decades, several ASMs have been introduced. While first- and second-generation ASMs demonstrate efficacy, they are limited by tolerability issues and pharmacokinetic disadvantages\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR4 CR5\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e. For PWWE, the safety profile of ASMs is critically important for both the mother and the developing fetus. Appropriate ASMs therapy improving maternal outcomes while minimizing seizure risk and reducing potential fetal/maternal harm to the greatest extent possible remains a therapeutic dilemma. With the approval of newer ASMs, PWWE exposure to various ASMs has become increasingly common\\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e. Compared to pregnant women without epilepsy or those not exposed to ASMs, they may face higher risks of adverse pregnancy outcomes and neonatal complications, including preterm birth, C-section, and fetal loss (including stillbirth, miscarriage, spontaneous abortion, or induced abortion)\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR9\\\" citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e. Although newer ASMs offer advantages in tolerability and drug interactions,​ safety data remains limited, particularly regarding adverse pregnancy and neonatal risks, so they cannot be confidently recommended for PWWE at this time\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e. Safety studies of third-generation ASMs often prioritize fetal outcomes over maternal risks\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. There remains a critical gap in research addressing maternal adverse effects, such as gestational hypertension, metabolic alterations, and long-term health impacts. Given the extensive application of third-generation ASMs, the lack of robust evidence linking these ASMs to pregnancy and obstetric outcomes highlights an urgent need for future research.\\u003c/p\\u003e\\u003cp\\u003eLarge-scale randomized clinical trials on the safety of ASMs in PWWE were not avaliable. There were existing six epilepsy-pregnancy registries, with West China Registry recently initiated. These registries are a primary source of ASMs safety information during pregnancy. However, they primarily assess the risk of major congenital malformations in offspring following in utero exposure to ASMs, and the Chinese registry is constrained by its short duration of operation. In contrast, pharmacovigilance data can serve as a valuable source of ASMs. Spontaneous reporting systems (SRSs) are a widely used pharmacovigilance method globally. Currently, the main SRSs include Vigibase, FAERS, and others. Due to its large data volume and easy accessibility, along with the fact that third-generation ASMs received market approval later in China with insufficient local data, we selected FAERS to mine and analyze the PRAEs caused by newer ASMs. This study investigated PRAEs associated with newer ASMs submitted to FAERS and compared the potential disproportionality signals with lamotrigine, levetiracetam and oxcarbazepine (reference drugs which were generally recognized as relatively safe during pregnancy). This study provides comprehensive data on the safety profiles of third-generation ASMs for both PWWE and fetuses, addressing the gap in existing research that predominantly focuses on fetal safety alone.\\u003c/p\\u003e\"},{\"header\":\"MATERIALS AND METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eData Sources\\u003c/h2\\u003e\\u003cp\\u003eData were obtained from the FAERS public dashboard from January 1, 2024 to June 31, 2024. The study included the following third-generation ASMs: brivaracetam (BRV), zonisamide (ZNS), lacosamide (LCM), rufinamide (RUF), perampanel (PER), eslicarbazepine (ESL), cannabidiol (CBD), tiagabine (TGB). Other third-generation ASMs were excluded for the following reasons: stiripentol, clobazam and fenfluramine are approved for specific epilepsy syndromes, such as Dravet and Lennox\\u0026ndash;Gastaut and therefore are not appropriate for our study. Vigabatrin has been warned permanent vision loss and indicated for the treatment of infantile spasms, ezogabine and felbamate have been withdrawn from the market, cenobamate and fenfluramine were not sufficient for a statistical analysis because of its recent market approval. The specific approval dates and mechanisms of action of the drugs are provided in supplementary table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. To reduce the risk of misleading relationship between drugs and AEs, only reported cases defined as AEs in which the reporter identified the target drug as the \\u0026ldquo;primary suspect\\u0026rdquo; and \\u0026ldquo;secondary suspect\\u0026rdquo;. AEs were standardized and classified using HLTs and System Organ Classes (SOCs) from the Medical Dictionary for Regulatory Activities (MedDRA), Version 26. The SOC \\u0026ldquo;pregnancy, puerperium and perinatal conditions\\u0026rdquo; was included, while cases pertaining to breastfeeding, male patients and normal conditions were excluded. Duplicate records were removed as previously described\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e. Data sources are publicly available and therefore do not require ethical approval.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eData analysis\\u003c/h2\\u003e\\u003cp\\u003eReporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and the Bayesian Confidence Propagation Neural Network (BCPNN) are methods commonly used in pharmacovigilance for detecting AE signals. ROR is a consistent estimator of the odds ratio (i.e., the risk ratio) and exhibits less bias than other methods. The PRR can estimate relative risk but is prone to false-positive signals. The BCPNN employs a supervised neural network learning approach, utilizing known ADRs as a training set for machine learning, which remains relatively stable even with a limited number of reports. Therefore, this study integrated the ROR, PRR, and BCPNN methods to mine the AE signals for the ASMs. All three approaches are based on the 2\\u0026times;2 contingency table. Their calculation formulas and threshold criteria for risk signals are detailed in supplementary table 2. Generally, higher ROR or information component (IC) values indicate stronger drug-AE associations.​​ To better investigate potential gestational risks of third-generation ASMs, subgroup analyses were stratified by comparator groups: First, comparing third-generation ASMs with all other drugs; second, intra-class comparisons within third-generation ASMs; finally, given the favorable gestational safety profiles of second-generation ASMs\\u0026mdash;levetiracetam (LEV), oxcarbazepine (OXC), and lamotrigine (LTG)\\u0026mdash;these agents were selected as reference comparators versus third-generation ASMs.​​ All data were processed using R software (version 4.0.5) and microsoft excel. Graphs were generated with GraphPad Prism 9.5. Heatmap was plotted by \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.bioinformatics.com.cn\\u003c/span\\u003e\\u003cspan address=\\\"https://www.bioinformatics.com.cn\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (last accessed on 10 Dec 2024), an online platform for data analysis and visualization\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003e3.1 Demographics\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eA total of 48,321 AEs of third-generation ASMs were submitted to the FAERS database from Q1 2004 to Q2 2024. Among these, only 1,162 AEs (2.4%) were classified under the SOC \\u0026ldquo;Pregnancy, Puerperium and Perinatal Conditions\\u0026rdquo;. A total of 507 reports contained age information: 254 reports (21.9%) were aged 18\\u0026ndash;35, and 121 reports (10.4%) were aged 35\\u0026ndash;50. There were 952 reports (81.9%) submitted by professions. The occurrence years of AEs were predominantly concentrated in 2014 to 2024 (accounting for 89.1%). The country distribution of reports is depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA and the European ranked first in number, with 873 cases. The annual number of reported cases of PRAEs for ASMs is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB. The primary indications for ASMs were epilepsy (749 cases, 65%), followed by reports with unknown indication information (320 cases, 27.5%). Our results indicated that the most common serious outcome was congenital anomaly (10.8%) and death (3.3%), while mild and moderate outcome including hospitalization, required intervention and others account for the majority (83.7%) of all cases. In terms of final outcome, ZNS had the highest mortality rate (4.8%) followed by LCM (3.8%). In the study cohort, TGB showed the highest incidence of congenital malformations (20%), with PER ranking second (17.2%). Further details were presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Among all AEs associated with ASMs, the incidence rates of PRAEs ranged from 1.4% for ESL to 5.4% for RUF, and their distribution were shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC. Overall, the PRAEs accounted for a small proportion of the total AEs associated with third-generation ASMs.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDemographic information reported by PRAEs for ASMs in FAERS\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"9\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" 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colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEurope\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e74\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e492\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e193\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNorth America\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e60\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSouth America\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAsia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e41\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOceania\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUnknown\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e10\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eIndication\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEpilepsy(Tuberous sclerosis complex, Sturge-Weber syndrome)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e449\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e31\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e179\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFoetal exposure during pregnancy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e24\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePsychiatric disorders(Affective disorder,Schizophrenia,Generalised anxiety disorder)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMigraine\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUnknown\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e41\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e164\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e30\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e64\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eReaction outcome\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLife-Threatening\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHospitalization\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e24\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e28\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e44\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDisability\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDeath\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e14\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCongenital Anomaly\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e23\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRequired Intervention\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOther Serious\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e80\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e513\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e198\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3.2 Signals associated with the third-generation ASMs compared with all the other drugs\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn this study, the PRAEs signals were detected by ROR, PRR, and BCPNN. The top 5 PTs were abortion spontaneous, premature baby, premature delivery, low birth weight baby, and stillbirth, involving 896 reports, which accounted for 77.5% of PRAEs. These 27 PTs could be categorized into 15 HLTs and 6 High-Level Group Terms (HLGTs), with the main SOC \\u0026ldquo;pregnancy, puerperium and perinatal conditions\\u0026rdquo; (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). ​​When comparing the third-generation ASMs with all the other drugs in terms of HLT classification, a total of 10 AE signals were obtained. The top 5 frequently reported AEs associated with these ASMs were: abortions spontaneous, gestational age and weight conditions, labour onset and length abnormalities, stillbirth and foetal death, and foetal growth complications (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Among these, neonatal metabolic and endocrine disorders had the highest signal with 7 cases (ROR\\u0026thinsp;=\\u0026thinsp;18.57; 95% CI, 8.66\\u0026ndash;39.81), followed by foetal complications (ROR\\u0026thinsp;=\\u0026thinsp;5.99; 95% CI, 3.2\\u0026ndash;11.2) and maternal complications of delivery (ROR\\u0026thinsp;=\\u0026thinsp;5.97; 95% CI, 2.46\\u0026ndash;14.46).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eSignal strength of PRAEs for ASMs at the High Level Terms (HLTs) level\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHLT\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003en\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eROR(95%CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ePRR\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ex2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eIC025\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eAdverse event\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortions spontaneous\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e384\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.42(3.09,3.78)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.40\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e644.06\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGestational age and weight conditions\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e347\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.84(2.55,3.15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.82\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e405.52\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLabour onset and length abnormalities\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e201\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.43(2.99,3.95)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.42\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e341.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStillbirth and foetal death\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e89\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.69(2.18,3.32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.69\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e93.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFoetal growth complications\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.77(1.26,2.49)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.77\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e10.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePregnancy complicated by maternal disorders\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.87(1.23,2.84)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.87\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8.79\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypertension associated disorders of pregnancy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.47(0.94,2.31)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.87\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.35\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePlacental abnormalities (excl neoplasms)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.98(3.08,8.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e53.17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.42\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortions not specified as induced or spontaneous\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.31(0.72,2.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.31\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.80\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFoetal complications\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.99(3.2,11.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.99\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e40.79\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNeonatal metabolic and endocrine disorders\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18.57(8.66,39.81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e18.56\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e109.76\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.93\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHaemorrhagic complications of pregnancy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.15(0.48,2.77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.08\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMaternal complications of delivery\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.97(2.46,14.46)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.97\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e20.28\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eYes\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAmniotic fluid and cavity disorders of pregnancy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.06(0.34,3.28)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.06\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMaternal complications of pregnancy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.02(0.97,9.42)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.51\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eNo\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eGiven the different pharmacological mechanisms of third-generation ASMs, we conducted disproportionality analysis of PRAEs for each drug (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Among them, the most frequently reported PT were abortion spontaneous (381,33%), premature baby (212,18.3%), premature delivery (166,14.4%), low birth weight baby (70,6.1%) and stillbirth (67,5.8%). A total of 17 positive signals were obtained for ZNS, 16 for LCM and 8 for BRV. Hypothermia neonatal is the most prominent signals for LCM, as well as placental infarction for ZNS and stillbirth for RUF. The detailed information was shown in Table\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB. We next analyzed all signals at the HLT level. There were 6 ASMs with strong signals for \\u0026ldquo;abortions spontaneous\\u0026rdquo; and \\u0026ldquo;gestational age and weight conditions\\u0026rdquo; (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). LCM was the only one shown positive correlation with the \\u0026ldquo;foetal complications\\u0026rdquo; (ROR 16.46; 95% CI, 8.8-30.77), \\u0026ldquo;maternal complications of delivery\\u0026rdquo; (ROR 16.39; 95% CI, 6.76\\u0026ndash;39.71), and \\u0026ldquo;neonatal metabolic and endocrine disorders\\u0026rdquo; (ROR 50.99; 95% CI, 23.78-109.34). Besides, ZNS indicated disproportionality signals of \\u0026ldquo;amniotic fluid and cavity disorders of pregnancy\\u0026rdquo;, \\u0026ldquo;haemorrhagic complications of pregnancy\\u0026rdquo;, and \\u0026ldquo;placental abnormalities (excl neoplasms)\\u0026rdquo;. Moreover, RUF showed the strongest significant correlation with stillbirth and foetal death. Our findings revealed that ZNS and LCM both shown ten disproportionality signals compared with all the other drugs. Further details can be found in Table\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eThe signals detection in terms of PTs of 8 ASMs\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePT\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003en\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c7\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eROR(95%CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eLCM\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" morerows=\\\"34\\\" nameend=\\\"c7\\\" namest=\\\"c3\\\" rowspan=\\\"35\\\"\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortion spontaneous\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e215\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e123\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature delivery\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e110\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLow birth weight baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e43\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStillbirth\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e35\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmall for dates baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e25\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHydrops foetalis\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGestational diabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortion\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypothermia neonatal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature rupture of membranes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRetained placenta or membranes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePostmature baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eThreatened labour\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHyperemesis gravidarum\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFoetal macrosomia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eZNS\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortion spontaneous\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e65\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e42\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmall for dates baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e22\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLow birth weight baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e19\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFoetal growth restriction\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e19\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature delivery\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e18\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStillbirth\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e16\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFoetal death\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGestational diabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePlacental disorder\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePre-eclampsia\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePlacental infarction\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature labour\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eThreatened labour\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature separation of placenta\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortion threatened\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePolyhydramnios\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eBRV\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortion spontaneous\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e30\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" morerows=\\\"22\\\" nameend=\\\"c7\\\" namest=\\\"c3\\\" rowspan=\\\"23\\\"\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e24\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature delivery\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e22\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLow birth weight baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGestational diabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePostmature baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eProlonged pregnancy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortion\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eESL\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortion spontaneous\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e22\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFoetal growth restriction\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003ePER\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortion spontaneous\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e28\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e18\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature delivery\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e13\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmall for dates baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eRUF\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStillbirth\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e16\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbortion spontaneous\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmall for dates baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eTGB\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature baby\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePremature delivery\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eThe signals detection in terms of HLTs of ASMs in different subgroup\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3.3 Disproportionality analysis of related AEs for specific ASMs compared with all the other third-generation ASMs\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo ​​compare the differences in safety during pregnancy among third-generation ASMs, we recalculated the RORs for PRAEs of all those newer ASMs. Upon conducting secondary analyses, only four ASMs including LCM, ZNS, BRV and RUF indicated PRAE signals. BRV, PER, and ESL showed no risk signals for spontaneous abortion in terms of ROR or PRR. Similarly, no risk signals were detected for \\u0026ldquo;gestational age and weight conditions\\u0026rdquo; with BRV, PER, RUF and TGB. Compared to the primary analysis, ZNS, PER and TGB showed no risk signals for \\u0026ldquo;labour onset and length abnormalities\\u0026rdquo;. However, LCM, ZNS, and RUF consistently demonstrated risks for \\u0026ldquo;stillbirth and foetal death\\u0026rdquo;. Further details regarding the secondary analysis can be found in Table \\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e presents the subgroup analysis results using the ROR and BCPNN method.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003e3.4 AEs during pregnancy for third-generation ASMs compared with the second-generation ASMs\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eAmong second-generation ASMs, LEV, LTG, and OXC were commonly used for the treatment of PWWE due to their favorable safety profiles\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR16\\\" citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e. The analysis uncovered significant signals when comparing LCM with LEV and LTG, including \\u0026ldquo;abortions not specified as induced or spontaneous (ROR 0.88; 95% CI, 0.43\\u0026ndash;1.81)\\u0026rdquo;, \\u0026ldquo;abortions spontaneous (ROR 0.7; 95% CI, 0.61\\u0026ndash;0.81)\\u0026rdquo;, \\u0026ldquo;foetal complications (ROR 0.62 95% CI, 0.33\\u0026ndash;1.18), \\u0026ldquo;gestational age and weight conditions (ROR 0.83; 95% CI, 0.72\\u0026ndash;0.96)\\u0026rdquo;. ZNS did not show significance compared to LEV in \\u0026ldquo;foetal growth complications\\u0026rdquo; and \\u0026ldquo;stillbirth and foetal death\\u0026rdquo;. Moreover, ZNS demonstrates a superior safety profile compared to LEV in terms of pregnancy-related complications and prolonged labor. Notably, the BCPNN method revealed unique risk signals for LCM specifically associated with neonatal metabolic and endocrine disorders, and maternal complications of delivery (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Additionally, no disproportionality signals were identified for BRV, PER, RUF, ESL, or TGB, due to an insufficient number of AE cases. Details were given in supplementary table 3.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eAs first-choice of treatment for epilepsy, ASMs require a relatively long course of administration. Therefore, the safety of ASMs for mothers and developing fetuses during pregnancy has always been a focus of attention, and sufficient safety information are urgently needed, especially for the third-generation ASMs. This real-world study comprehensively evaluates the similarities and differences in the gestational safety profiles across various categories of third-generation ASMs.\\u003c/p\\u003e\\u003cp\\u003eIn this study, the two drugs with the highest number of PRAEs were LCM (n\\u0026thinsp;=\\u0026thinsp;646) and ZNS (n\\u0026thinsp;=\\u0026thinsp;270). This observation may be attributable to the higher usage rates of these two drugs during pregnancy.​​ A separate MONEAD study \\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003ereported the most frequently used monotherapies of third-generation ASM in pregnancy was ZNS (5.0%). The most common polytherapy regimens were LCM\\u0026thinsp;+\\u0026thinsp;LEV (6.5%), and LTG\\u0026thinsp;+\\u0026thinsp;ZNS (5.2%). LCM is partly eliminated by the kidneys (approximately 40%) and also partly metabolized by CYP2C19 and demonstrates a significant advantage in maintaining pharmacokinetic stability before and during pregnancy compared with LEV or LTG\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u003c/sup\\u003e. However, more consistent data come from MONEAD study\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e, reporting a decrease of dose-normalized concentrations during pregnancy of 39.9% for LCM. In this study, LCM demonstrated risks for miscarriage, hyperemesis gravidarum, threatened preterm labor, premature rupture or retention of membranes, and gestational diabetes in PWWE. Fetal risks included abnormalities in gestational age and birth weight, stillbirth, hydrops fetalis, and neonatal hypothermia. Although some studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e suggest no association between LCM and small for gestational age (SGA) or microcephaly, this conclusion remains premature due to limited sample sizes. In our study, 25 cases of SGA were reported with an ROR of 4.8 (95% CI: 3.24\\u0026ndash;7.11), indicating a significant risk signal. Two pharmacovigilance reports \\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003eof LCM align with these findings, showing fetal loss in 23\\u0026ndash;26% of exposed pregnancies.​​ Moreover, LCM showed a risk difference for preterm birth of 4.6% and for cesarean delivery of 9.7%\\u003csup\\u003e23\\u003c/sup\\u003e. Notably, preterm birth emerged as a risk signal for LCM in this study, warranting clinical vigilance. LCM also generated a unique risk signal for hypothermia neonatal among ASMs\\u0026mdash;unreported in prior human studies but consistent with dose-dependent hypothermia observed in rat models when LCM was administered at analgesic doses\\u003csup\\u003e\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e. Consequently, vigilance regarding hypothermia risks to the fetus is warranted during LCM therapy.\\u003c/p\\u003e\\u003cp\\u003eThe UK and Ireland epilepsy and pregnancy register\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e have found among 112 patients exposed to ZNS during the first trimester of pregnancy, the major congenital malformation (MCM) rate was 13%, while spontaneous abortions and preterm births accounted for 21%. Our study revealed that congenital malformations accounted for 8% of ZNS in real world. Abortion spontaneous and premature baby were the most commonly reported PTs, with a total of 107 cases documented, representing 39.6% of all PRAEs. These findings highlight concerns about the potential teratogenicity of ZNS. However, only 130 cases across four available studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e to date noted severely limited pregnancy exposure data for ZNS- underscoring the need for further safety evaluations. ZNS was associated with SGA in a meta-analysis\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e. Hern\\u0026aacute;ndez-D\\u0026iacute;az S et al.\\u003csup\\u003e27\\u003c/sup\\u003e demonstrated that ZNS induces significant weight reduction, neonates born to ZNS-exposed group showed a mean birth weight reduction of 202g and a 12.2% incidence of height deficiency (\\u0026gt;\\u0026thinsp;1cm below standard). These findings corroborate the risk signal for SGA identified in our study with ZNS. Notably, ZNS was associated with placental disorders (n\\u0026thinsp;=\\u0026thinsp;9 reports), representing a unique risk signal not previously documented in ​earlier findings. The mechanism remains unclear, may be associated with its high rate of transplacental transfer (reported up to 92%)\\u003csup\\u003e28\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eBRV has risk signals for 8 PTs and it exhibits stable pharmacokinetics during pregnancy and a generally favorable safety profile. In vitro studies show no cytotoxicity in embryos after exposure to BRV\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e. Three pregnancies exposed to BRV, their newborns did not possess MCMs\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e. However, two infants presented with three minor congenital anomalies. Our study also found the teratogenic risks of BRV, which represented 18% of the outcomes. PER has risk signals for 4 PTs and the most significant ROR signal was spontaneous abortion. Alicino AM et al.\\u003csup\\u003e31\\u003c/sup\\u003e reported 96 pregnancies treated with PER, 28 pregnancies (29.2%) did not reach term, including induced abortion and spontaneous. Two infants exposed to PER monotherapy had low Apgar scores\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e, our study also detected risk signals for preterm birth and SGA. No controlled studies have investigated the pharmacokinetics of PER in pregnancy. Although a ​​3\\u0026ndash;4 fold reduction in PER exposure was documented in the third trimester​​ (weeks 28\\u0026ndash;40), pharmacokinetic analyses\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e demonstrated ​​minimal fluctuation in PER plasma concentrations across all trimesters. ESL generated two HLTs risk signals. Although ESL was associated with MCMs in case reports\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e, all documented cases involved concurrent risk factors or co-administered medications. Of 15 spontaneous abortion cases, 11 occurred with ESL exposure - a finding\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e consistent with our study. Despite detected risk signals for spontaneous abortion and premature delivery with RUF and TGB, no case reports substantiating these associations have been published to date. Similarly, for CBD, neither pharmacokinetic studies in pregnant individuals nor comprehensive data in healthy human cohorts are currently available. Studies of larger populations are needed to elucidate if there are any pharmacokinetic changes in the more recently approved medications during pregnancy.\\u003c/p\\u003e\\u003cp\\u003eThis study has several limitations: First, as we focused solely on newer ASMs, the low reporting numbers in databases may introduce selection bias. Given their recent market approval, potential adverse reactions might remain unreported or undetected, underscoring the risk signal. Second, substantial missing age data precluded stratified subgroup analysis by age demographics. Additionally, limited sample size prevented adjusting for confounders such as drug interactions and dosage effects. Third, the majority of AE reports originated from the US and Europe, lacking Asian population data; consequently, the impact of polymorphisms (e.g., CYP enzyme variants) on gestational drug exposure cannot be excluded. Finally, the AE risk signals only represent statistical associations- confirming the safety profiles of third-generation ASMs during pregnancy requires validation through large-scale, prospective clinical studies.\\u003c/p\\u003e\"},{\"header\":\"CONCLUSIONS\",\"content\":\"\\u003cp\\u003eIn summary, given the widespread use of third-generation ASMs, this study provides a comprehensive safety assessment of their gestational, perinatal, and puerperium profiles. The findings offer critical reference guidance for physicians choosing ASMs during pregnancy, particularly highlighting risk signals identified in contrast to second-generation ASMs. Further studies are required to validate and confirm the hypotheses generated from this study.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePatient records in the FAERS database are anonymized and de-identified; therefore, Consent to Participate declaration: not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and analysed during the current study are available in the FAERS database ,[https://www.fda.gov/drugs/surveillance/fdas-adverse-event-reporting-system-faers].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the Science and Technology Development Program of Medical and Health from Shandong Province (202102040505).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLan-Fang Li: Conceptualization (Lead);\\u0026nbsp;Formal Analysis (Lead);\\u0026nbsp;Writing \\u0026ndash;Original Draft Preparation (Equal). Xiu-Mei Wang: Conceptualization (Supporting); Data Curation (Lead). Xue-Yan CUI: Writing \\u0026ndash; Review and Editing (Supporting); Jing PENG: Methodology (Equal). Xiao-Lei REN: Methodology (Equal);\\u0026nbsp;Software (Lead). Mei-Xia WANG:\\u0026nbsp;Resources (Equal). Qing-Xia Kong: Conceptualization (Equal); Writing \\u0026ndash; Original Draft (Supporting);\\u0026nbsp;Validation (Equal). Hui-Xian Zhang: Conceptualization (Lead); Funding Acquisition (Lead); Supervision (Lead); Visualization (Lead); Writing \\u0026ndash; Review \\u0026amp; Editing (Lead). All authors contributed to the article and approved the submitted version.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank Wenzhe Chen (Information Center, Affiliated Hospital of Jining Medical University.) for providing computation support.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eRazaz N, Igland J, Bj\\u0026oslash;rk MH, Joseph KS, Dreier JW, Gilhus NE, Gissler M, Leinonen MK, Zoega H, Alvestad S, Christensen J, Tomson T. Risk of Perinatal and Maternal Morbidity and Mortality Among Pregnant Women With Epilepsy. JAMA Neurol. 2024;81(9):985\\u0026ndash;95.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFreund B, Chen B, Kaplan PW, Johnson EL. Managing Antiseizure Medications in Pregnancy: Is Earlier and More Frequent Monitoring Better? 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Use of Antiseizure Medications Early in Pregnancy and the Risk of Major Malformations in the Newborn. Neurology. 2025;105(3):e213786.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKawada K, Itoh S, Kusaka T, Isobe K, Ishii M. Pharmacokinetics of zonisamide in perinatal period. Brain Dev. 2002;24(2):95\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWitherspoon E, Zuczek N, Williams G, Bernstein B, Ghosh A, Culjat M, Kaushal S, Forcelli PA. A single exposure to brivaracetam or perampanel does not cause cell death in neonatal rats. Front Pediatr. 2024;12:1441891.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePaolini SL, Pilato M, Rajasekaran V, Waters JFR, Bagic A, Urban A. Outcomes in three cases after brivaracetam treatment during pregnancy. Acta Neurol Scand. 2020;141(5):438\\u0026ndash;41.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAlicino AM, Falcicchio G, Boero G, Santarcangelo G, Francavilla T, Trojano M, La Neve A. Perampanel during pregnancy: Description of four cases. Epilepsy Behav Rep. 2021;16:100490.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eVazquez B, Tomson T, Dobrinsky C, Schuck E, O'Brien TJ. Perampanel and pregnancy. Epilepsia. 2021;62(3):698\\u0026ndash;708.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eYamamoto Y, Akita N, Nogimoto H, Suzuki W, Imai K, Takahashi Y, Kagawa Y. Changes in Perampanel Pharmacokinetics and Cytochrome P450 3A4 Activity Before, During, and After Pregnancy. Ther Drug Monit. 2024;46(4):548\\u0026ndash;51.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eCosta R, Magalh\\u0026atilde;es LM, Gra\\u0026ccedil;a J, Vieira M, Gama H, Moreira J, Rocha JF. Soares-da-Silva P. Eslicarbazepine acetate exposure in pregnant women with epilepsy. Seizure. 2018;58:72\\u0026ndash;4.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003eTables 1 to 4 are available in the Supplementary Files section.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"antiseizure medications, pregnant women with epilepsy, PRAEs, FAERS\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7930539/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7930539/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003eThe third generation antiseizure medications (ASMs) have been widely used in clinical practice due to their safety and effectiveness. Given the scarcity of research on the risks of prenatal exposure to third generation ASMs for maternal and fetal, certain challenges persist. This study investigates the frequency and rate of pregnancy-related adverse events (PRAEs) associated with third generation ASMs in the FDA Adverse Event Reporting System (FAERS) database.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eThe adverse events (AEs) of ASMs were selected from FAERS database. The reporting odds ratio (ROR), proportional reporting ratio (PRR) and bayesian confidence propagation neural network (BCPNN) were utilized to conduct disproportionality analysis. Additionally, subgroup analyses were conducted to compare the PRAEs between the newer ASMs and reference drugs.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eIn total, 48,321 AEs were identified for third-generation ASMs, of which 1,162 reports (2.5%) were PRAEs. The most common PRAEs were abortion spontaneous (33.0%), premature baby (18.3%) and premature delivery (14.4%). Disproportionality analysis revealed ten signals for zonisamide​​, nine signals for lacosamide​​, four signals for brivaracetam, three signals for perampanel​​ or ​​rufinamide. The three most frequently reported HLT were \\u0026ldquo;abortion spontaneous\\u0026rdquo;, \\u0026ldquo;gestational age and weight conditions\\u0026rdquo; and \\u0026ldquo;labour onset and length abnormalities\\u0026rdquo;. The strongest PT signals were placental infarction for zonisamide (ROR\\u0026thinsp;=\\u0026thinsp;115.4, 95% CI: 56.9\\u0026ndash;234.0), postmature baby for brivaracetam (ROR\\u0026thinsp;=\\u0026thinsp;83.1, 95% CI: 26.5\\u0026ndash;261.1), and stillbirth for rufinamide (ROR\\u0026thinsp;=\\u0026thinsp;83, 95% CI: 50.5\\u0026ndash;136.4). Notably, we revealed that zonisamide​​​​ was related with placental abnormalities, including placental disorder and infarction. These disproportionality signals varied across different subgroup analyses.​\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e\\u003cp\\u003eIn comparison with​​ second-generation ASMs, newer ASMs demonstrate a comparable safety profile in pregnant women with epilepsy (PWWE). However, caution must be exercised in clinical medication, with close attention paid to the adverse reactions indicated by the risk signals of each drug. Additional prospective cohort studies should be conducted to validate these findings.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Adverse Events During Pregnancy Associated with Third Generation Antiseizure Medications: A Real-World Analysis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-14 11:16:30\",\"doi\":\"10.21203/rs.3.rs-7930539/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"42f7010a-279b-41a1-bc60-1cbb5a9ec65c\",\"owner\":[],\"postedDate\":\"November 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-12-11T05:38:58+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-14 11:16:30\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7930539\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7930539\",\"identity\":\"rs-7930539\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}