Investigation into Safety Profiles of Antiepileptic Drugs and Identification of Predictors for Serious Adverse Events: Insights from National Pharmacovigilance Data
by
and
Soo Hyeon Lee
1,2,†, 
Dae Hyeon Sung
3,†,
Euna Cho
3,
Jeongah Min
3,
Sooyoung Shin
4,5,* and
Yeo Jin Choi
1,2,3,* 1
Department of Regulatory Science, Graduate School, Kyung Hee University, Seoul 02447, Republic of Korea
2
Institute of Regulatory Innovation through Science (IRIS), Kyung Hee University, Seoul 02447, Republic of Korea
3
Department of Pharmacy, School of Pharmacy, Kyung Hee University, Seoul 02447, Republic of Korea
4
Research Institute of Pharmaceutical Science and Technology (RIPST), Ajou University, Suwon 16499, Republic of Korea
5
Department of Pharmacy, College of Pharmacy, Ajou University, Suwon 16499, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Pharmaceuticals 2025, 18(7), 1013; https://doi.org/10.3390/ph18071013
Submission received: 3 June 2025
/
Revised: 25 June 2025
/
Accepted: 1 July 2025
/
Published: 7 July 2025
(This article belongs to the Special Issue Pharmacoepidemiology and Pharmacovigilance in Drug Therapy: New Issues, Challenges, and Applications)
Abstract
Backgrounds/Objectives: This study aims to comprehensively characterize the prevalence and severity of antiepileptic drug (AED)-induced adverse drug events (ADEs) and to identify predictors strongly associated with serious adverse events (SAEs) in both general and geriatric populations. Methods: This cross-sectional study investigated AED-related ADEs reported to the KIDS KAERS DB from January 2014 to December 2023. Disproportionality analysis was performed to detect the association between reported SAEs, and multiple logistic regression was conducted to identify predictors associated with SAEs. Cox’s proportional hazard model was utilized to assess ADE duration in elderly patients aged 60 years and older. Results: More than 50% of 36,809 AED-related ADEs were reported in elderly patients aged 60 years and older, and the prevalence of SAEs was 3.78%. ADEs associated with endocrine disorders had the highest likelihood of SAEs being reported (ROR 15.30), followed by hematological disorders. The predictors associated with elevated SAE risks in the elderly were male sex (OR 1.91; 95% CI 1.62–2.27), aging (OR 1.17; 95% CI 1.04–1.31), and certain AEDs. However, the concomitant administration of acid-suppressive therapy (AST) and opioids was associated with a lower risk of SAEs in the elderly population. Elderly patients not receiving concomitant AST were less likely to experience prolonged ADE duration (HR 0.28, 95% CI 0.07–1.15); however, no substantial differences in ADE duration were observed with the concomitant use of opioids. Conclusions: This study implies significant variability in the frequency, severity, and duration of ADEs depending on the type of AEDs, patient demographics, and concomitant medication use.
1. Introduction
Epilepsy is a brain disorder characterized by an increased predisposition to epileptic seizures, leading to transient occurrences of signs and/or symptoms associated with abnormal or excessive neuronal activity in the brain [1]. Epilepsy is associated with numerous neurological, cognitive, psychological, and social consequences. The global prevalence of epilepsy is increasing, affecting 51.7 million patients, with an age-standardized prevalence rate of 657 per 100,000 patients in 2021 [1,2]. The management of epilepsy remains a significant clinical challenge despite the numerous antiepileptic drugs (AEDs) available on the market, and a recent study indeed reported a prevalence of drug-refractory epilepsy of 21.3% [3,4]. Moreover, the frequent occurrence of numerous adverse drug events (ADEs) associated with AEDs complicates epilepsy management as well [3]. These ADEs may range in severity from mild to severe, or even life-threatening, which substantially affects patient adherence to the treatment and overall quality of life [5].
One of the most significant challenges in epilepsy management is the risk of drug–drug interactions (DDIs) associated with AEDs, particularly the first generation of AEDs, such as carbamazepine and phenytoin [5]. Many AEDs either induce or inhibit the cytochrome P450 (CYP450) enzyme, subsequently increasing the risk of potential drug–drug interactions and ADEs [5]. Furthermore, AEDs themselves may interact with one another, and this is especially concerning for patients with refractory epilepsy, who often require multiple AEDs concurrently to manage epilepsy. Moreover, these patients are predisposed to an elevated ADE risk due to the altered pharmacokinetic properties and reduced therapeutic efficacy associated with potential drug–drug interactions.
Geriatric populations, defined as the population aged 65 years and older, are at an elevated risk of severe adverse events (SAEs) due to altered pharmacokinetic/pharmacodynamic characteristics, polypharmacy, and multimorbidity [6]. They also have an elevated risk of cognitive impairment, falls, frailty, and comorbidities such as cardiovascular and endocrinology disorders, all of which may be exacerbated by AED-induced ADEs, further complicating epilepsy management [7]. Furthermore, age-related physiological changes may further exacerbate the pharmacokinetic/pharmacodynamic impact of AEDs, increasing the vulnerability of geriatric patients to ADEs [8]. Nonetheless, the impact of AED-induced ADEs in geriatric patients is limited despite their heightened risk. Moreover, considering the substantial increase in the aging population worldwide, a comprehensive investigation into the safety of AEDs as well as the identification of predictors associated with SAEs in the geriatric population is essential. Thus, the aim of this study was to comprehensively characterize the prevalence and severity of AED-induced ADEs and to identify predictors strongly associated with SAEs in both general and geriatric populations by utilizing a spontaneous adverse event system to promote drug safety in patients with epilepsy.
2. Results
2.1. Patient Demographics
Among 134,393 ADE cases extracted from the Korean Adverse Event Reporting System database (KIDS KAERS DB), a total of 36,809 AED-related ADEs reported from 1 January 2014, to 31 December 2023, were included in the analysis (Figure 1). The highest number of ADEs was reported in elderly patients aged 60 years and older (N = 19,155; 52.04%) (Table 1). Approximately 65.62% of cases were reported in women (N = 24,153). The prevalence of SAEs was 3.78% (N = 1392). The most etiologic AEDs were pregabalin (N = 12,318; 33.46%) and gabapentin (N = 11,062; 30.05%). The highest likelihood of SAEs being reported was observed for valproic acid (reporting odds ratio (ROR) 6.18, 95% confidence interval (CI) 4.79–7.98), followed by phenytoin (ROR 4.03, 95% CI 3.09–5.25) and phenobarbital (ROR 3.86, 95% CI 2.43–6.13) (Table 2). The likelihood of SAEs being reported was substantially lower for gabapentin (ROR 0.20, 95% CI 0.16–0.24) and pregabalin (ROR 0.36, 95% CI 0.31–0.41).
2.2. ADE Types and Risk of Reporting SAEs
The most frequent types of AED-related ADEs were associated with central and peripheral nervous system disorders (N = 8270; 22.47%) and respiratory system disorders (N = 7976; 21.67%) (Table 3). However, reported ADEs associated with endocrine disorders had the highest likelihood of being SAEs (ROR 15.30; 95% CI 3.65–64.07), followed by hematological disorders, including red blood cell disorders (ROR 10.01; 95% CI 4.63–21.68) and white cell and reticuloendothelial system (RES) disorders (ROR 9.00; 95% CI 6.90–11.72). The odds of reporting SAEs were substantially lower among ADEs associated with central and peripheral nervous system disorders (ROR 0.41; 95% CI 0.35–0.49), psychiatric disorders (ROR 0.20; 95% CI 0.15–0.28), and gastrointestinal system disorders (ROR 0.06; 95% CI 0.03–0.10). The results of the disproportionality analysis of SOC-based ADEs for individual AEDs are summarized in Table 4. The highest likelihood of reporting both serious and nonserious skin and appendages disorder was observed for lamotrigine (ROR 12.46, 95% CI 11.15–13.92 for non-SAEs, and ROR 3.30 95% CI 2.40–4.55 for SAEs), followed by phenytoin (ROR 5.18, 95% CI 4.26–6.29 for non-SAEs and ROR 2.73, 95% CI 1.66–4.50 for SAEs) and carbamazepine (ROR 5.10, 95% CI 4.61–5.64 for non-SAEs and ROR 2.17, 95% CI 1.64–2.87 for SAEs). Interestingly, levetiracetam was more likely to lead to reports of nonserious skin and appendages disorder (ROR 2.42, 95% CI 2.19–2.68) than serious cases, where the signal was inversely associated with this risk (ROR 0.40, 95% CI 0.27–0.61). Valproic acids and clonazepam are more likely to be associated with serious central and peripheral nervous system disorders than nonserious cases. Topiramate was the only agent with a substantially high likelihood of serious vision-related ADEs being reported (ROR 54.45 (95% CI 13.23–224.12). Clonazepam and topiramate had a markedly high likelihood of both serious and nonserious ADEs related to psychiatric disorders being reported, whereas levetiracetam and phenytoin were more likely to lead to reports of both nonserious and serious liver and biliary disorders. Oxcarbamazepine had a significantly high likelihood of both serious and nonserious ADEs related to metabolic disorders and hematologic disorders being reported, including red blood cell disorders and white cell and reticuloendothelial system (RES) disorders. The odds of reporting SAEs were substantially higher with concomitant chemotherapy administration (ROR 1.93, 95% CI 1.02–3.69) (Table 5). However, the concomitant administration of acid-suppression therapy (AST), acetaminophen, non-steroidal anti-inflammatory drugs (NSAIDs), opioids, antidepressants, and antihyperglycemic agents was associated with a lower likelihood of SAEs being reported.
2.3. ADE Types and Risk of SAE Reporting in Elderly Patients
The highest likelihood of AED-related SAEs reported in elderly patients was observed for white cell and RES disorders (ROR 10. 78; 95% CI 7.08–16.41), followed by liver and biliary system disorders (ROR 6.25; 95% CI 4.25–9.21) and cardiovascular disorders (ROR 3.07; 95% CI 1.32–7.13) (Table 6). In contrast, the likelihood of reporting SAEs was lower with central and peripheral nervous system disorders (ROR 0.45; 95% CI 0.35–0.57), psychiatric disorders (ROR 0.23; 95% CI 0.15–0.37), and gastrointestinal disorders (ROR 0.03; 95% CI 0.00–0.09). The results of the disproportionality analysis of SOC-based ADEs for individual AEDs in elderly patients are summarized in Table S1. The concomitant use of AST, acetaminophen, NSAIDs, opioids, and antidepressants was associated with a lower likelihood of SAEs being reported (Table 7).
2.4. Identification of Predictors Associated with SAE Risks
Univariate analysis identified patient sex, causality, the number and types of concomitant medications, and AED types as predictors associated with SAEs (Table 8). Multivariate logistic regression revealed a significantly increased risk of SAEs with male sex (OR 1.36; 95% CI 1.21–1.52), concomitant immunosuppressant (OR 3.68; 95% CI 1.39–9.74) and chemotherapy (OR 2.22; 95% CI 1.11–4.45) use, and certain AEDs, including divalproex, lamotrigine, levetiracetam, valproic acid, oxcarbazepine, zonisamide, carbamazepine, phenobarbital, and phenytoin. The predictors associated with elevated SAE risks in the elderly were male sex (OR 1.91; 95% CI 1.62–2.27), aging (OR 1.17; 95% CI 1.04–1.31), and certain AEDs, including lamotrigine (OR 2.26; 95% CI 1.43–3.56), levetiracetam (OR 1.83, 1.25–2.69), valproic acid (OR 3.70; 95% CI 1.96–7.00), carbamazepine (OR 2.78, 95% CI 1.94–3.98), and phenytoin (OR 3.33; 95% CI 1.98–5.58) (Table 9). However, the concomitant administration of AST, opioids, and antidepressants was associated with a lower risk of SAEs in elderly patients.
2.5. Impact of SOC on ADE Duration in the Elderly
The duration of ADE was significantly longer in female patients (HR 1.42, 95% CI 1.02–1.99) (Figure 2). Patients experiencing ADEs unrelated to the central and peripheral nervous system (HR 0.39, 95% CI 0.22–0.70) and non-psychiatric disorders (HR 0.36, 95% CI 0.15–0.89) were less likely to have prolonged ADE duration, whereas those patients experiencing ADEs related to non-respiratory disorders were more likely to experience a longer duration of ADEs (HR 1.65, 95% CI 1.18–2.32) (Figure 3). Among elderly patients, those not receiving concomitant AST were less likely to experience prolonged ADE duration (HR 0.28, 95% CI 0.07–1.15) (Figure 4). No substantial differences in ADE duration were observed with the concomitant use of opioids, acetaminophen, and NSASIDs.
3. Discussion
This study comprehensively evaluated AED-induced ADE records that were spontaneously reported to a nationwide pharmacovigilance reporting system, KIDS KAERS DB, from 1 January 2014, to 31 December 2023. This study demonstrated that the majority of the ADEs were reported in the geriatric population aged 60 years and older, implying the increased vulnerability of older adults to drug-related ADEs and complications (Table 1). Among numerous AEDs, gabapentin and pregabalin were the most frequently reported agents; however, both agents were associated with a substantially lower risk of SAEs being reported, suggesting a favorable safety profile compared to older-generation AEDs, such as phenytoin and phenobarbital (Table 2). The most frequently reported types of ADEs were those associated with central and peripheral nervous system disorders and respiratory system disorders, whereas the highest likelihood of SAEs being reported was observed for ADEs related to endocrine disorders, hematologic disorders, and liver and biliary disorders (Table 3). The predictors associated with elevated AED-induced SAEs were male sex, the type of AEDs, and the concomitant use of immunosuppressants and chemotherapy (Table 8). However, increasing age was an essential predictor for elevated SAE risk in the elderly population (Table 9).
The risk of SAEs is substantially higher for skin and appendages disorder, liver and biliary disorders, respiratory system disorders, and white cell RES disorders in patients from both the general and geriatric population (Table 3 and Table 6). Severe skin and appendages ADEs, such as Stevens–Johnson syndrome (SJS), drug reactions with eosinophilia and systemic symptoms (DRESS), and toxic epidermal necrolysis (TEN), are well-recognized as being rare, but there are serious complications for certain AEDs, particularly carbamazepine, lamotrigine, and valproic acids [9,10]. Consistent with previous findings, this study demonstrated the substantially elevated likelihood of both nonserious and serious skin and appendages disorder being reported in relation to the ADEs of lamotrigine and carbamazepine (Table 4). These reactions often require intensive medical interventions and substantially increase morbidity, especially in older adults [11]. Moreover, evidence suggests a higher risk of drug-induced skin-related ADEs in Asian populations, which may be partially explained by genetic susceptibility, such as the HLA–B*15:02 alleles [12]. Furthermore, the rapid titration of an AED dose also contributes to an elevated risk of skin-related reactions in epilepsy patients, implying the importance of pharmacogenetic screening and optimal dose titration in Asian elderly populations [13].
Drug-induced liver and biliary injury is also a well-recognized complication of AEDs, primarily due to their hepatic metabolism and hepatotoxic potentials. Several AEDs, including valproic acids, phenytoin, and carbamazepine, are extensively metabolized by the liver and have been associated with both idiosyncratic and dose-dependent liver injuries, ranging from an asymptomatic elevation in liver enzymes, such as aspartate transaminase (AST) and alanine transaminase (ALT), to fulminant hepatic failure [14]. Furthermore, these agents exhibit potent modulatory activity, functioning either as an inducer or inhibitor, on the CYP450 enzyme system, subsequently contributing to clinically significant DDIs [15]. These pharmacokinetic interactions are particularly critical in geriatric patients, who often experience an age-related reduction in hepatic clearance and polypharmacy, increasing their susceptibility to liver injury and ADEs resulting from significant DDIs. Hence, a comprehensive medication review and liver function monitoring are critical to mitigate the risk of severe liver-related ADEs.
ADEs involving the central and peripheral nervous system were among the most frequently reported ADEs in this study (Table 3). These ADEs typically include symptoms such as somnolence, tremor, peripheral neuropathy, and cognitive impairments, many of which are dose-dependent and may significantly affect daily functioning [16]. Despite their lower likelihood of being reported as SAEs, elderly patients experiencing central and peripheral nervous systems-related ADEs are more likely to have prolonged event duration (Figure 3), implying that the burden of central and peripheral nervous system-related ADEs is not necessarily reflected by severity alone but also by their persistence and functional impact over time. The chronic or slowly resolving nature of many neurologic side effects, in addition to delayed recognition or insufficient dose adjustment, may contribute to extended symptom duration. Moreover, elderly patients are particularly vulnerable to AED-induced cognitive impairment due to age-related declines in neuroplasticity, and these patients often experience slower recovery from neurologic toxicity [17,18]. Moreover, in this study, ADEs related to white cell and RES system disorders were associated with a significantly increased likelihood of SAEs being reported (Table 3). Several AEDs, including carbamazepine, phenytoin, valproic acid, and phenobarbital, have been implicated in a range of hematologic toxicities, such as leukopenia, aplastic anemia, and thrombocytopenia [19], and this study provided consistent findings (Table 4). Again, these ADEs can either be idiosyncratic or dose-related, especially in the case of prolonged exposure or high plasma concentrations [20]. This risk is more pronounced in elderly patients, who may have reduced bone marrow reserves or altered drug metabolism [21,22]. Hence, this study emphasizes the need for cautious neurologic and hematologic monitoring, individualized dosing strategies, and early intervention to minimize neurologic ADEs in aging populations.
In this study, gabapentin and pregabalin emerged as the most frequently reported etiologic AEDs (Table 2), and this may reflect their widespread use as adjunctive treatments, not only in refractory epilepsy but also in neuropathic pain, fibromyalgia, and anxiety disorders, especially in older adults [23,24]. Although their frequent use contributed to the high number of reported ADEs, both gabapentin and pregabalin were associated with a substantially lower likelihood of SAEs being reported, implying a generally favorable safety profile compared to older-generation AEDs. However, these agents may induce central nervous system (CNS)-related ADEs, including somnolence, dizziness, ataxia, and cognitive impairment, particularly among elderly patients or those receiving polypharmacy. Elderly patients are at a higher risk of pain from multiple comorbidities, including osteoarthritis and diabetic neuropathy, which may lead to the concomitant use of other analgesics, such as acetaminophen, NSAIDs, and opioids [25]. Interestingly, this study found that the concomitant use of opioids, NSAIDs, and acetaminophen was associated with a lower likelihood of SAEs being reported (Table 5 and Table 7) and an insignificantly prolonged duration of ADEs (Figure 4). This may result from cautions for prescription practices: short-term or low-dose use in high-risk patients. Nonetheless, caution is warranted, particularly with gabapentinoid–opioid combinations, as previous research has revealed a markedly increased risk of respiratory depression and overdose [26]. These findings accentuate the importance of individualized treatment plans and close monitoring for ADEs related to disorders of the central nervous system and respiratory system in elderly patients.
Another interesting finding was prolonged ADE recovery among patients receiving concomitant AST despite its association with a lower risk of SAEs (Figure 4). This may be associated with altered pharmacokinetic characteristics by histamine-2 receptor antagonists (H2RAs) and proton pump inhibitors (PPIs). These agents alter gastric pH, which can contribute to altered absorption and bioavailability of certain AEDs, especially those with pH-dependent solubility [27]. Interestingly, a study revealed that the long-term use of PPIs has been linked to a substantially increased risk of developing epilepsy [28]. While the mechanism behind this remains unclear, disruption of the gut–brain axis, micronutrient deficiencies, and chronic system inflammation induced by altered gastrointestinal microbiota may have a role in inducing epilepsy [28]. These findings suggest that prolonged AST use may have neurological implications other than pharmacokinetic interactions, implying the importance of judicious AST prescription, especially in patients with epilepsy or those receiving long-term AED therapy. Moreover, further controlled studies are warranted to elucidate the clinical significance of AST-related neurologic risks and their impact on central nervous system outcomes in epilepsy management.
This study provided a comprehensive pharmacovigilance investigation of AEDs, including detailed insights into the types and duration of AED-induced ADEs as well as their prevalence and severity over a 10-year period. This study also provides differentiated analyses by age group, particularly focusing on elderly patients, who are often underrepresented in clinical trials but are predisposed to an elevated risk of drug-related problems and complications. Furthermore, this study investigated the impact of concurrent medication therapy with AEDs on ADE severity and types. Nonetheless, this study possesses several limitations. First, KIDS KAERS DB is a spontaneous voluntary ADE reporting system, which provides nationwide and longitudinal insights into drug-induced ADEs, but it is subject to potential issues with reporting bias, underreporting, and variability in data completeness and quality. The absence of comprehensive patient histories, including comorbidities, concurrent medications, treatment duration, and exact medication dosages, may hinder the accurate determination of the causality between AED exposure and reported ADEs, thereby leading to an underestimation or misinterpretation of the risk factors contributing to AED-induced ADEs. Moreover, potentially important clinical and demographic factors—including medication adherence, body mass index (BMI), education level, and socioeconomic status—were not available in the KIDS KAERS DB, limiting the analysis to evaluating potential correlations between the variables and the occurrence of ADEs with different severities. Additionally, certain drugs, such as oxycodone and other opioids, were excluded from the subset used for the disproportionality analysis due to a low number of SAE reports, not meeting the minimum number of ADE cases required for statistical evaluations. Consequentially, key risk factors may have been underestimate, and potential safety signals for these drugs may have been underrepresented or missed in our findings. Moreover, there may be long-term skeletal risks associated with AEDs that were not captured in this study. For example, some AEDs are known to reduce bone mineral density and increase fracture risk, particularly with prolonged use. However, in this study, only two cases of osteoporosis-related ADEs were identified, which may reflect underreporting of the delayed onset of such outcomes in the spontaneous reporting system. Furthermore, the observational nature of this study’s design may result in unclear causality, as patient-specific factors, including unmeasured variables, may act as confounding factors that may influence the outcomes. Nonetheless, minimal bias from the ADE cases was expected in this study because the Korea Institute of Drug Safety and Risk Management (Ministry of Food and Drug Safety) performs in-depth investigations of ADE reports by reviewing patients’ medical charts, collecting scientific pharmacovigilance data from manufacturers, and consulting with healthcare professionals appointed by the institution. Despite these limitations, this study contributes valuable real-world evidence on the safety of AEDs, highlighting population-specific vulnerabilities, and accentuates the importance of integrating pharmacovigilance data into clinical decision-making to promote safe AED use. Furthermore, this study provides evidence for tailored guidance on the prevention of SAEs in elderly patients that can be applied in clinical practice.
4. Materials and Methods
4.1. Study Design and Data Collection
This cross-sectional study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [29]. This study analyzed AED-related adverse event (AE) cases spontaneously reported to the KIDS KAERS DB, constructed by the Korean Institute of Drug Safety and Risk Management (KIDS, Ministry of Food and Drug Safety) from 1 January 2014, to 31 December 2023 [30,31,32]. The prespecified medications included 16 AEDs: carbamazepine, clonazepam divalproex, ethosuximide, gabapentin, lacosamide, lamotrigine, levetiracetam, oxcarbazepine, phenobarbital, phenytoin, pregabalin, primidone, topiramate, valproic acid, and zonisamide. The causality of drug-induced AEs was assessed by the World Health Organization–Uppsala Monitoring Centre (WHO–UMC) criteria, and all AEs with “certain”, “probable/likely”, and “possible” causality were included in the analysis. Any AEs with “unlikely”, “conditional/unclassified”, or “unassessable/unclassifiable” causality [33], and those with masked (MSK-coded) etiologic medications, were excluded from the analysis. MSK codes are assigned to medication products that are marketed by fewer than 2 pharmaceutical companies. Both monotherapy and polytherapy ADE cases were included in the analysis, with no exclusion applied based on concomitant medication use. All ADEs were reported in accordance with Medical Dictionary for Regulatory Activities (MedDRA) terminology and were further classified into system organ classes (SOCs). Serious adverse events (SAEs) were classified in accordance with the International Conference on Harmonization (ICH) E2D guidelines and included any ADEs involving death, life-threatening conditions, a persistent or significant disability or incapacity, hospitalization or prolonged existing hospitalizations, congenital abnormalities or birth defects, and other medically significant events [34]. The following data were extracted from KIDS KAERS DB: (1) patient demographic information, including sex and age, (2) medical histories and concurrent medication lists, (3) ADE information, including etiologic medications, causality assessment, occurrence date, resolution date, and seriousness, and (4) information on reporters. The protocol for utilizing KIDS KAERS DB was approved by KIDS (Ministry of Food and Drug Safety) (KIDS KAERS DB 2405A0010) and was reviewed and approved for exemption by the Kyung Hee University institutional board (IRB) (No. KHSIRB–24–418(EA), approved 14 August 2024) as the study involved de-identified, anonymized data.
4.2. Statistical Analysis
Descriptive statistics were calculated to summarize patient demographics and ADE types related to depression treatment. Age was expressed as the median and interquartile range (IQR) based on the Kolmogorov–Smirnov normality test. The disproportionality test was performed to determine the likelihood of SAEs being reported for ADEs with at least 4 reported cases of both nonserious ADEs and SAEs to ensure the validity and reliability of the results [31,32]. The disproportionality test was conducted, and the effect size was estimated as the reporting odds ratio (ROR) with corresponding 95% confidence interval (CIs) and Mantel–Haenszel-adjusted P-values for the following assessments: (1) the association between AEDs and SAEs, (2) the association of SOC-based ADEs with seriousness, (3) the association of SOC-based ADEs with seriousness for each AED agent, (4) the association of the type of concomitantly used medications with SAEs, and (5) the association of SOC-based ADEs and individual AED agents in both nonserious and serious ADE cases. The ROR was calculated based on Table 10.
A univariate logistic regression was performed to identify risk factors associated with AED-induced SAEs, and these factors included sex, age, causality, number of concurrently used medications, and types of AEDs or concomitantly used medications. A multiple logistic regression with the forward selection method was conducted to estimate the effect size of the predictors that were substantially associated with the seriousness of AED-induced ADEs based on the univariate logistic regression. The effect size was estimated using the odds ratio (OR) with a 95% CI. A sensitivity analysis of the AED-induced ADEs in elderly patients who were aged 60 years and older was carried out to determine the significance of ADEs in the elderly [35]. A Kaplan–Meier survival analysis and Cox’s proportional hazard modeling were performed to evaluate the differences in the duration of ADEs across SOC categories and in relation to the type of concomitant drug therapy used in elderly patients. Any ADEs with at least 100 reported cases were included in the analysis. Only ADE records with the reported occurrence and resolution date were analyzed, and the effect size was estimated using the hazard ratio (HR) with a 95% CI. All statistical analyses were conducted with SPSS Statistic 26.0 (IBM SPSS Statistics for Windows, Armonk, NY, USA) and R (version 4.3.1). Statistical significance was determined by any p-value < 0.05.
5. Conclusions
This study provides a comprehensive evaluation of AED-induced ADEs using a nationwide pharmacovigilance database. The study highlights significant variability in the frequency, severity, and duration of ADEs depending on the type of AEDs, patient demographics, and concomitant medication use. Elderly patients were more likely to report SAEs related to white cell and RES disorders and liver and biliary disorders. The multivariate analysis identified male sex, the use of immunosuppressants and chemotherapy agents, and specific AEDs as key predictors associated with an increased risk of SAEs. Among elderly patients, advanced aged and certain AEDs were important predictors of elevated SAE risk. The likelihood of SAEs being reported was lower with central and peripheral nervous system disorders and psychiatric disorders; however, patients experienced prolonged ADE durations related to these disorders. Interestingly, the concomitant use of analgesics, such as NSAIDs, and opioids was identified as a predictor associated with a lower SAE risk, despite insignificant durations of ADEs in the elderly. Conversely, concomitant AST was associated with prolonged ADE recovery. These findings strongly emphasize the importance of individualized AED selection along with cautious monitoring in elderly patients. Further controlled studies are warranted to explore the mechanism underlying prolonged ADE recovery to optimize epilepsy management across diverse patient populations.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ph18071013/s1, Table S1: Disproportionality analysis on SOC-based ADEs by each AEDs in Elderly Patients.
Author Contributions
Conceptualization: S.H.L., D.H.S., Y.J.C.; data curation: S.H.L., D.H.S., E.C., S.S.; formal analysis: S.H.L., D.H.S., J.M.; funding acquisition: Y.J.C.; investigation: S.H.L., D.H.S., Y.J.C., S.S.; methodology: S.H.L., D.H.S., Y.J.C., S.S.; project administration: Y.J.C., S.S.; resources: Y.J.C., S.S.; software: S.H.L., E.C., J.M., Y.J.C.; supervision: Y.J.C., S.S.; validation: E.C., J.M.; visualization: S.H.L., E.C., J.M.; original draft: S.H.L., D.H.S., Y.J.C.; writing—review and editing: Y.J.C., S.S. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by a grant from Kyung Hee University in 2024 (KHU–20241064). Y.J. Choi received funds from Kyung Hee University.
Institutional Review Board Statement
The protocol for utilizing the KIDS KAERS DB was approved by KIDS (Ministry of Food and Drug Safety) (KIDS KAERS DB 2405A0010) and was reviewed and approved for exemption by the Kyung Hee University institutional board (IRB) (No. KHSIRB–24–418(EA), approved 14 August 2024) as the study involved de-identified, anonymized data.
Informed Consent Statement
Patient consent was waived due to the utilization of anonymized data. Informed consent was waived by the board.
Data Availability Statement
The data presented in this study are available on request from the corresponding author and KIDS due to the inclusion of patient information and ethical concerns.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| ADE | Adverse Drug Event |
| AE | Adverse Event |
| AED | Antiepileptic Drug |
| AST | Acid-Suppressive Therapy |
| CI | Confidence Interval |
| H2RA | Histamine-2 Receptor Antagonist |
| HR | Hazard Ratio |
| KIDS | Korean Institute of Drug Safety and Risk Management |
| NSAIDs | Non-steroidal Anti-inflammatory Drug |
| OR | Odds Ratio |
| PPI | Proton Pump Inhibitor |
| RES | Reticuloendothelial System |
| ROR | Reporting Odds Ratio |
| SAE | Serious Adverse Event |
| SOC | System Organ Class |
References
- Fisher, R.S.; van Emde Boas, W.; Blume, W.; Elger, C.; Genton, P.; Lee, P.; Engel, J., Jr. Epileptic seizures and epilepsy: Definitions proposed by the Intenational League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 2005, 46, 470–472. [Google Scholar] [CrossRef]
- GBD Epilepsy Collaborators; Global, regional, and national burden of epilepsy, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021. Lancet Public Health 2025, 10, e203–e227. [CrossRef] [PubMed]
- Kanner, A.M.; Ashman, E.; Gloss, D.; Harden, C.; Bourgeois, B.; Bautista, J.F.; Abou–Khalil, B.; Burakgazi-Dalkilic, E.; Llanas Park, E.; Stern, J.; et al. Practice guideline update summary: Efficacy and tolerability of the new antiepileptic drugs I: Treatment of new–onset epilepsy: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology and the American Epilepsy Society. Neurology 2018, 91, 74–81. [Google Scholar]
- Marouf, H.; Mohamed, L.A.; El Ftatary, A.; Gaber, D.E. Prevalence and risk factors associated with drug–resistant epilepsy in adult epileptic patients. Egypt J. Neurol. Psychiatry Neurosurg. 2023, 59, 153. [Google Scholar] [CrossRef]
- Kanner, A.M.; Bicchi, M.M. Antiseizure Medications for adults with epilepsy: A review. JAMA 2022, 327, 1269–1281. [Google Scholar] [CrossRef] [PubMed]
- Kim, G.J.; Lee, J.S.; Jang, S.; Lee, S.; Jeon, S.; Lee, S.; Kim, J.H.; Lee, K.H. Polypharmacy and elevated risk of severe adverse events in older adults based on the Korea Institute of Drug Safety and Risk Management–Korea Adverse Event Reporting System Database. J. Korean Med. Sci. 2024, 39, e205. [Google Scholar] [CrossRef]
- Acharya, J.N.; Acharya, V.J. Epilepsy in the elderly: Special considerations and challenges. Ann. Indian. Acad. Neurol. 2014, 17 (Suppl. S1), S18–S26. [Google Scholar] [CrossRef] [PubMed]
- Roberti, R.; Palleria, C.; Nesci, V.; Tallarico, M.; Di Bonaventura, C.; Cerulli Irelli, E.; Morano, A.; De Sarro, G.; Russo, E.; Citraro, R. Pharmacokinetic considerations about antiseizure medications in the elderly. Expert Opin. Drug Metab. Toxicol. 2020, 16, 983–995. [Google Scholar] [CrossRef]
- Park, C.S.; Kang, D.Y.; Kang, M.G.; Kim, S.; Ye, Y.M.; Kim, S.H.; Park, H.K.; Park, J.W.; Nam, Y.H.; Yang, M.S.; et al. Severe cutaneous adverse reactions to antiepileptic drugs: A nationwide registry–based study in Korea. Allergy Asthma. Immunol. Res. 2019, 11, 709–722. [Google Scholar] [CrossRef]
- Kim, H.K.; Kim, D.Y.; Bae, E.K.; Kim, D.W. Adverse skin reactions with antiepileptic drugs using Korea Adverse Event Reporting System Database, 2008–2017. J. Korean Med. Sci. 2020, 35, e17. [Google Scholar] [CrossRef]
- Heng, Y.K.; Lim, Y.L. Cutaneous Advers. drug reactions in the elderly. Curr. Opin. Allergy Clin. Immunol. 2015, 15, 300–307. [Google Scholar] [CrossRef] [PubMed]
- Thong, B.Y.; Lucas, M.; Kang, H.R.; Chang, Y.S.; Li, P.H.; Tang, M.M.; Yun, J.; Fok, J.S.; Kim, B.K.; Nagao, M.; et al. Drug hypersensitivity reactions in Asia: Regional issues and challenges. Asia Pac. Allergy 2020, 10, e8. [Google Scholar] [CrossRef]
- Jang, Y.; Moon, J.; Kim, N.; Kim, T.J.; Jun, J.S.; Shin, Y.W.; Chang, H.; Kang, H.R.; Lee, S.T.; Jung, K.H.; et al. A new rapid titration protocol for lamotrigine that reduces the risk of skin rash. Epilepsia Open 2021, 6, 394–401. [Google Scholar] [CrossRef]
- Vidaurre, J.; Gedela, S.; Yarosz, S. Antiepileptic drugs and liver disease. Pediatr. Neurol 2017, 77, 23–36. [Google Scholar] [CrossRef]
- Levy, R.H. Cytochrome P450 isozymes and antiepileptic drug interactions. Epilepsia 1995, 36 (Suppl. S5), S8–S13. [Google Scholar] [CrossRef] [PubMed]
- Zaccara, G.; Gangemi, P.F.; Cincotta, M. Central nervous system adverse effects of new antiepileptic drugs. A meta–analysis of placebo–controlled studies. Seizure 2008, 17, 405–421. [Google Scholar] [CrossRef]
- Ren, L.; Zheng, Y.; Wu, L.; Gu, Y.; He, Y.; Jiang, B.; Zhang, J.; Zhang, L.; Li, J. Investigation of the prevalence of Cognitive Impairment and its risk factors within the elderly population in Shanghai, China. Sci. Rep. 2018, 8, 3575. [Google Scholar] [CrossRef] [PubMed]
- Fantini, M.; Gianni, L.; Tassinari, D.; Nicoletti, S.; Possenti, C.; Drudi, F.; Sintini, M.; Bagli, L.; Tamburini, E.; Ravaioli, A. Toxic encephalopathy in elderly patients during treatment with capecitabine: Literature review and a case report. J. Oncol. Pharm. Pract. 2011, 17, 288–291. [Google Scholar] [CrossRef]
- Bachmann, T.; Bertheussen, K.H.; Svalheim, S.; Rauchenzauner, M.; Luef, G.; Gjerstad, L.; Taubøll, E. Haematological side effects of antiepileptic drug treatment in patients with epilepsy. Acta Neurol. Scand. Suppl. 2011, 191, 23–27. [Google Scholar] [CrossRef]
- Verrotti, A.; Scaparrotta, A.; Grosso, S.; Chiarelli, F.; Coppola, G. Anticonvulsant drugs and hematological disease. Neurol. Sci. 2014, 35, 983–993. [Google Scholar] [CrossRef]
- Willmore, L.J. Antiepileptic drug therapy in the elderly. Pharmacol. Ther. 1998, 78, 9–16. [Google Scholar] [CrossRef] [PubMed]
- Kaur, U.; Chauhan, I.; Gambhir, I.S.; Chakrabarti, S.S. Antiepileptic drug therapy in the elderly: A clinical pharmacological review. Acta Neurol. Belg. 2019, 119, 163–173. [Google Scholar] [CrossRef]
- French, J.; Glue, P.; Friedman, D.; Almas, M.; Yardi, N.; Knapp, L.; Pitman, V.; Posner, H.B. Adjunctive pregabalin vs. gabapentin for focal seizures: Interpretation of comparative outcomes. Neurology 2016, 87, 1242–1249. [Google Scholar] [CrossRef] [PubMed]
- Markota, M.; Morgan, R.J. Treatment of generalized anxiety disorder with gabapentin. Case Rep. Psychiatry 2017, 6045017. [Google Scholar] [CrossRef] [PubMed]
- Schmader, K.E.; Baron, R.; Haanpää, M.L.; Mayer, J.; O’Connor, A.B.; Rice, A.S.; Stacey, B. Treatment considerations for elderly and frail patients with neuropathic pain. Mayo Clin. Proc. 2010, 85 (Suppl. S3), S26–S32. [Google Scholar] [CrossRef]
- Bykov, K.; Bateman, B.T.; Franklin, J.M.; Vine, S.M.; Patorno, E. Association of gabapentinoids with the risk of opioid–related adverse events in surgical patients in the United States. JAMA Netw. Open 2020, 3, e2031647. [Google Scholar] [CrossRef]
- Patsalos, P.N.; Spencer, E.P.; Berry, D.J. Therapeutic drug monitoring of antiepileptic drugs in epilepsy: A 2018 update. Ther. Drug Monit. 2018, 40, 526–548. [Google Scholar] [CrossRef]
- Liang, C.S.; Bai, Y.M.; Hsu, J.W.; Huang, K.L.; Ko, N.Y.; Tsai, C.K.; Yeh, T.C.; Chu, H.T.; Tsai, S.J.; Chen, T.J.; et al. The risk of epilepsy after long–term proton pump inhibitor therapy. Seizure 2021, 87, 88–93. [Google Scholar] [CrossRef]
- von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. J. Clin. Epidemiol. 2008, 61, 344–349. [Google Scholar] [CrossRef]
- Shin, J.Y.; Jung, S.Y.; Ahn, S.H.; Lee, S.H.; Kim, S.J.; Seong, J.M.; Chung, S.Y.; Park, B.J. New initiatives for pharmacovigilance in South Korea: Introducing the Korea Institute of Drug Safety and Risk Management (KIDS). Pharmacoepidemiol. Drug Saf. 2014, 23, 1115–1122. [Google Scholar] [CrossRef]
- Choi, Y.J.; Choi, C.Y.; Kim, C.U.; Shin, S. A nationwide pharmacovigilance investigation on trends and seriousness of adverse events induced by anti–obesity medication. J. Glob. Health 2023, 13, 04095. [Google Scholar] [CrossRef] [PubMed]
- Choi, Y.J.; Yang, S.W.; Kwack, W.G.; Lee, J.K.; Lee, T.H.; Jang, J.Y.; Chung, E.K. Comparative safety profiles of sedatives commonly used in clinical practice: A 10–year nationwide pharmacovigilance study in Korea. Pharmaceuticals 2021, 14, 783. [Google Scholar] [CrossRef] [PubMed]
- The Uppsala Monitoring Centre. The Use of the WHO–UMC System for Standarised Case Causality Assessment. Available online: https://www.who.int/docs/default-source/medicines/pharmacovigilance/whocausality-assessment.pdf (accessed on 25 October 2024).
- International Conference on Harmonisation. Post–Aproval Safety Data Management: Definitions and Standard for Expedites Reporting E2D. Available online: https://database.ich.org/sites/default/files/E2D_Guideline.pdf (accessed on 25 October 2024).
- Ismail, Z.; Ahmad, W.I.W.; Hamjah, S.H.; Astina, I.K. The impact of population ageing: A review. Iran J. Public Health 2021, 50, 2451–2460. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Data acquisition process.
Figure 2.
Impact of sex on duration of ADEs in elderly patients.
Figure 3.
Duration of ADEs associated with SOC-based classifications in elderly patients: (A) skin and appendages disorders; (B) central and peripheral nervous system disorders; (C) respiratory system disorders; (D) psychiatric disorders; and (E) disorders of the body as a whole.
Figure 3.
Duration of ADEs associated with SOC-based classifications in elderly patients: (A) skin and appendages disorders; (B) central and peripheral nervous system disorders; (C) respiratory system disorders; (D) psychiatric disorders; and (E) disorders of the body as a whole.
Figure 4.
Duration of ADEs associated with types of concomitant medications: (A) acid-suppressive therapy (AST), (B) opioids, (C) acetaminophen, and (D) non-steroidal anti-inflammatory drugs (NSAIDs).
Figure 4.
Duration of ADEs associated with types of concomitant medications: (A) acid-suppressive therapy (AST), (B) opioids, (C) acetaminophen, and (D) non-steroidal anti-inflammatory drugs (NSAIDs).
Table 1.
Baseline demographic characteristics of patients.
| Characteristics | No. of Cases (% Relative Frequency) or Median (IQR) |
|---|---|
| Age (years) | Median: 60 IQR: 24 |
| 0–9 | 827 (2.25%) |
| 10–19 | 887 (2.41%) |
| 20–29 | 1946 (5.29%) |
| 30–39 | 2631 (7.15%) |
| 40–49 | 4190 (11.38%) |
| 50–59 | 7173 (19.49%) |
| 60–69 | 8757 (23.79%) |
| 70–79 | 7576 (20.58%) |
| 80–89 | 2655 (7.21%) |
| 90–99 | 167 (0.45%) |
| Sex | |
| Men | 12,656 (34.38%) |
| Women | 24,153 (65.62%) |
| Causality | |
| Certain | 514 (1.40%) |
| Probable/Likely | 7477 (20.31%) |
| Possible | 28,818 (78.29%) |
| Seriousness | |
| Serious adverse events | 1392 (3.78%) |
| Nonserious adverse events | 35,417 (96.22%) |
| Reporting individuals | |
| Doctors | 6503 (17.67%) |
| Pharmacists | 18,232 (49.53%) |
| Nurses and other healthcare professionals | 11,049 (30.02%) |
| General public | 1025 (2.78%) |
| No. concomitantly used medications | |
| 1 | 18,064 (49.07%) |
| 2 | 4132 (11.23%) |
| 3 | 4018 (10.92%) |
| 4 | 3589 (9.75%) |
| ≥5 | 7006 (19.03%) |
| Anticonvulsant types | |
| Gabapentin | 11,062 (30.05%) |
| Divalproex | 169 (0.46%) |
| Lamotrigine | 1611 (4.38%) |
| Lacosamide | 477 (1.30%) |
| Levetiracetam | 2501 (6.79%) |
| Valproic Acid | 404 (1.10%) |
| Ethosuximide | 2 (0.01%) |
| Oxcarbazepine | 1067 (2.90%) |
| Zonisamide | 178 (0.48%) |
| Carbamazepine | 2098 (5.70%) |
| Clonazepam | 2540 (6.90%) |
| Topiramate | 1576 (4.28%) |
| Phenobarbital | 161 (0.44%) |
| Phenytoin | 498 (1.35%) |
| Pregabalin | 12,318 (33.46%) |
| Primidone | 147 (0.40%) |
Abbreviation: IQR—interquartile range.
Table 2.
Association of anticonvulsant medication class with the seriousness of ADEs.
| AEDs | Non-SAE (N = 35,417) | SAE (N = 1392) | Total (N = 36,809) | ROR (95%CI) | p-Value |
|---|---|---|---|---|---|
| Gabapentin | 10,949 (30.91%) | 113 (8.12%) | 11,062 (30.05%) | 0.20 (0.16–0.24) | <0.001 |
| Divalproex | 156 (0.44%) | 13 (0.93%) | 169 (0.46%) | 2.13 (1.21–3.76) | 0.009 |
| Lamotrigine | 1427 (4.03%) | 184 (13.22%) | 1611 (4.38%) | 3.63 (3.08–4.27) | <0.001 |
| Lacosamide | 462 (1.30%) | 15 (1.08%) | 477 (1.30%) | 0.82 (0.49–1.38) | 0.464 |
| Levetiracetam | 2292 (6.47%) | 209 (15.01%) | 2501 (6.79%) | 2.55 (2.19–2.98) | <0.001 |
| Valproic Acid | 328 (0.93%) | 76 (5.46%) | 404 (1.10%) | 6.18 (4.79–7.98) | <0.001 |
| Ethosuximide | 2 (0.01%) | 0 (0.00%) | 2 (0.01%) | N/A | N/A |
| Oxcarbazepine | 996 (2.81%) | 71 (5.10%) | 1067 (2.90%) | 1.86 (1.45–2.38) | <0.001 |
| Zonisamide | 166 (0.47%) | 12 (0.86%) | 178 (0.48%) | 1.85 (1.03–3.33) | 0.041 |
| Carbamazepine | 1817 (5.13%) | 281 (20.19%) | 2098 (5.70%) | 4.68 (4.07–5.38) | <0.001 |
| Clonazepam | 2483 (7.01%) | 57 (4.09%) | 2540 (6.90%) | 0.57 (0.43–0.74) | <0.001 |
| Topiramate | 1521 (4.29%) | 55 (3.95%) | 1576 (4.28%) | 0.92 (0.70–1.21) | 0.535 |
| Phenobarbital | 140 (0.40%) | 21 (1.51%) | 161 (0.44%) | 3.86 (2.43–6.13) | <0.001 |
| Phenytoin | 432 (1.22%) | 66 (4.74%) | 498 (1.35%) | 4.03 (3.09–5.25) | <0.001 |
| Pregabalin | 12,101 (34.17%) | 217 (15.59%) | 12,318 (33.46%) | 0.36 (0.31–0.41) | <0.001 |
| Primidone | 145 (0.41%) | 2 (0.14%) | 147 (0.40%) | 0.35 (0.09–1.41) | 0.141 |
Table 3.
Association between system organ class (SOC)-based ADEs and seriousness caused by AEDs.
| System Organ Class | Non-SAE (N = 35,417) | SAE (N = 1392) | Total (N = 36,809) | ROR (95% CI) | p-Value |
|---|---|---|---|---|---|
| Skin and appendages disorders | 4243 (11.98%) | 356 (25.57%) | 4599 (12.49%) | 2.53 (2.23–2.86) | <0.001 |
| Musculoskeletal system disorders | 191 (0.54%) | 7 (0.50%) | 198 (0.54%) | 0.93 (0.44–1.99) | 0.855 |
| Collagen disorders | 4 (0.01%) | 1 (0.07%) | 5 (0.01%) | 6.37 (0.71–56.98) | 0.098 |
| Central and peripheral nervous system disorders | 8119 (22.92%) | 151 (10.85%) | 8270 (22.47%) | 0.41 (0.35–0.49) | <0.001 |
| Vision disorders | 284 (0.80%) | 9 (0.65%) | 293 (0.80%) | 0.81 (0.41–1.57) | 0.523 |
| Hearing and vestibular disorders | 39 (0.11%) | 1 (0.07%) | 40 (0.11%) | 0.65 (0.09–4.75) | 0.673 |
| Special senses and other disorders | 31 (0.09%) | 0 (0.00%) | 31 (0.08%) | N/A | N/A |
| Psychiatric disorders | 4809 (13.58%) | 43 (3.09%) | 4852 (13.18%) | 0.20 (0.15–0.28) | <0.001 |
| Gastrointestinal system disorders | 5199 (14.68%) | 13 (0.93%) | 5212 (14.16%) | 0.06 (0.03–0.10) | <0.001 |
| Liver and biliary system disorders | 415 (1.17%) | 82 (5.89%) | 497 (1.35%) | 5.28 (4.14–6.73) | <0.001 |
| Metabolic and nutritional disorders | 641 (1.81%) | 21 (1.51%) | 662 (1.80%) | 0.83 (0.54–1.29) | 0.407 |
| Endocrine disorders | 5 (0.01%) | 3 (0.22%) | 8 (0.02%) | 15.30 (3.65–64.07) | <0.001 |
| General cardiovascular disorders | 115 (0.32%) | 11 (0.79%) | 126 (0.34%) | 2.45 (1.31–4.55) | 0.005 |
| Heart rate and rhythm disorders | 128 (0.36%) | 7 (0.50%) | 135 (0.37%) | 1.39 (0.65–2.99) | 0.394 |
| Vascular (extracardiac) disorders | 36 (0.10%) | 1 (0.07%) | 37 (0.10%) | 0.71 (0.10–5.16) | 0.732 |
| Respiratory system disorders | 7598 (21.45%) | 378 (27.16%) | 7976 (21.67%) | 1.37 (1.21–1.54) | <0.001 |
| Red blood cell disorders | 23 (0.06%) | 9 (0.65%) | 32 (0.09%) | 10.01 (4.63–21.68) | <0.001 |
| White cell and RES disorders | 229 (0.65%) | 77 (5.53%) | 306 (0.83%) | 9.00 (6.91–11.72) | <0.001 |
| Platelet, bleeding, and clotting disorders | 44 (0.12%) | 0 (0.00%) | 44 (0.12%) | N/A | N/A |
| Urinary system disorders | 615 (1.74%) | 17 (1.22%) | 632 (1.72%) | 0.70 (0.43–1.14) | 0.149 |
| Reproductive disorders, male | 12 (0.03%) | 0 (0.00%) | 12 (0.03%) | N/A | N/A |
| Reproductive disorders, female | 42 (0.12%) | 0 (0.00%) | 42 (0.11%) | N/A | N/A |
| Neoplasms | 5 (0.01%) | 2 (0.14%) | 7 (0.02%) | 10.19 (1.98–52.57) | 0.006 |
| Body as a whole—general disorders | 2517 (7.11%) | 202 (14.51%) | 2719 (7.39%) | 2.22 (1.90–2.59) | <0.001 |
| Application site disorders | 5 (0.01%) | 0 (0.00%) | 5 (0.01%) | N/A | N/A |
| Resistance mechanism disorders | 4 (0.01%) | 0 (0.00%) | 4 (0.01%) | N/A | N/A |
| Secondary terms—events | 63 (0.18%) | 1 (0.07%) | 64 (0.17%) | 0.40 (0.06–2.91) | 0.368 |
| Poison-specific terms | 1 (0.00%) | 0 (0.00%) | 1 (0.00%) | N/A | N/A |
Abbreviations: N/A—not applicable; RES—reticuloendothelial system; ROR—reporting odds ratio; SAE—serious adverse events
Table 4.
Disproportionality analysis of SOC-based ADEs by individual AEDs.
| AEDs | Non-SAEs | SAEs | ||
|---|---|---|---|---|
| ROR (95% CI) | p-Value | ROR (95% CI) | p-Value | |
| Skin and Appendages Disorder | ||||
| Gabapentin | 0.31 (0.29–0.34) | <0.001 | 0.53 (0.31–0.89) | 0.016 |
| Divalproex | 1.83 (1.23–2.71) | 0.003 | N/A | N/A |
| Lamotrigine | 12.46 (11.15–13.92) | <0.001 | 3.30 (2.40–4.55) | <0.001 |
| Lacosamide | 1.10 (0.84–1.44) | 0.502 | N/A | N/A |
| Levetiracetam | 2.42 (2.19–2.68) | <0.001 | 0.40 (0.27–0.61) | <0.001 |
| Valproic acids | 2.48 (1.93–3.19) | <0.001 | 0.70 (0.39–1.25) | 0.232 |
| Oxcarbazepine | 6.80 (5.98–7.74) | <0.001 | 1.24 (0.73–2.09) | 0.428 |
| Zonisamide | 1.12 (0.72–1.76) | 0.613 | 2.09 (0.66–6.64) | 0.209 |
| Carbamazepine | 5.10 (4.61–5.64) | <0.001 | 2.17 (1.64–2.87) | <0.001 |
| Clonazepam | 0.43 (0.36–0.51) | <0.001 | 0.53 (0.26–1.10) | 0.089 |
| Topiramate | 0.66 (0.55–0.79) | <0.001 | 0.64 (0.32–1.28) | 0.203 |
| Phenobarbital | 5.41 (3.86–7.58) | <0.001 | 0.68 (0.23–20.38) | 0.492 |
| Phenytoin | 5.18 (4.26–6.29) | <0.001 | 2.73 (1.66–4.50) | <0.001 |
| Pregabalin | 0.2 (0.19–0.23) | <0.001 | 0.20 (0.12–0.33) | <0.001 |
| Primidone | 0.26 (0.11–0.64) | 0.003 | N/A | N/A |
| Musculoskeletal Disorder | ||||
| Gabapentin | 0.97 (0.72–1.33) | 0.869 | N/A | N/A |
| Divalproex | 1.19 (0.17–8.55) | 0.862 | 4.32 (2.24–8.36) | <0.001 |
| Lacosamide | 2.05 (0.84–5.00) | 0.116 | N/A | N/A |
| Levetiracetam | 0.97 (0.54–1.74) | 0.915 | N/A | N/A |
| Oxcarbazepine | 0.74 (0.27–1.99) | 0.549 | N/A | N/A |
| Carbamazepine | 1.02 (0.54–1.94) | 0.947 | N/A | N/A |
| Clonazepam | 1.47 (0.91–2.36) | 0.113 | N/A | N/A |
| Topiramate | 2.18 (1.54–4.00) | <0.001 | N/A | N/A |
| Pregabalin | 0.80 (0.58–1.09) | 0.157 | N/A | N/A |
| Central and Peripheral Nervous System Disorder | ||||
| Gabapentin | 1.20 (1.14–1.27) | <0.001 | 1.63 (0.96–2.79) | 0.072 |
| Divalproex | 0.55 (0.35–0.87) | 0.01 | N/A | N/A |
| Lamotrigine | 0.14 (0.11–0.18) | <0.001 | N/A | N/A |
| Lacosamide | 1.35 (1.10–1.66) | 0.004 | N/A | N/A |
| Levetiracetam | 0.39 (0.34–0.45) | <0.001 | 0.80 (0.48–1.32) | 0.377 |
| Valproic acids | 0.63 (0.47–0.85) | 0.002 | 4.96 (2.98–8.24) | <0.001 |
| Oxcarbazepine | 0.45 (0.37–0.54) | <0.001 | 0.61 (0.24–1.54) | 0.295 |
| Zonisamide | 0.54 (0.35–0.84) | 0.006 | N/A | N/A |
| Carbamazepine | 0.59 (0.52–0.67) | <0.001 | 0.25 (0.13–0.49) | <0.001 |
| Clonazepam | 0.55 (0.49–0.62) | <0.001 | 2.85 (1.52–5.34) | 0.001 |
| Topiramate | 1.03 (0.91–1.16) | 0.693 | 1.42 (0.66–3.07) | 0.371 |
| Phenobarbital | 0.15 (0.07–0.34) | <0.001 | N/A | N/A |
| Phenytoin | 0.86 (0.68–1.09) | 0.204 | N/A | N/A |
| Pregabalin | 1.71 (1.63–1.80) | <0.001 | 2.64 (1.80–3.88) | <0.001 |
| Primidone | 1.93 (1.37–2.71) | <0.001 | N/A | N/A |
| Vision Disorders | ||||
| Gabapentin | 0.50 (0.37–0.67) | <0.001 | N/A | N/A |
| Divalproex | 4.15 (1.69–10.20) | 0.002 | N/A | N/A |
| Lamotrigine | 0.87 (0.46–1.64) | 0.662 | N/A | N/A |
| Lacosamide | 6.62 (4.24–10.33) | <0.001 | N/A | N/A |
| Levetiracetam | 0.86 (0.52–1.43) | 0.565 | N/A | N/A |
| Valproic acids | 2.74 (1.28–5.85) | 0.009 | N/A | N/A |
| Oxcarbazepine | 2.80 (1.79–4.38) | <0.001 | N/A | N/A |
| Carbamazepine | 0.82 (0.46–1.46) | 0.488 | N/A | N/A |
| Clonazepam | 0.69 (0.4–1.18) | 0.17 | N/A | N/A |
| Topiramate | 2.37 (1.59–3.53) | <0.001 | 54.45 (13.23–224.12) | <0.001 |
| Pregabalin | 0.91 (0.71–1.17) | 0.449 | N/A | N/A |
| Psychiatric Disorders | ||||
| Gabapentin | 1.40 (1.31–1.49) | <0.001 | N/A | N/A |
| Divalproex | 0.88 (0.55–1.43) | 0.609 | N/A | N/A |
| Lamotrigine | 0.29 (0.22–0.37) | <0.001 | N/A | N/A |
| Lacosamide | 0.93 (0.71–1.23) | 0.61 | N/A | N/A |
| Levetiracetam | 1.06 (0.94–1.20) | 0.32 | 1.31 (0.60–2.86) | 0.504 |
| Valproic acids | 1.04 (0.76–1.42) | 0.813 | N/A | N/A |
| Oxcarbazepine | 0.37 (0.28–0.48) | <0.001 | N/A | N/A |
| Zonisamide | 1.58 (1.08–2.32) | 0.019 | N/A | N/A |
| Carbamazepine | 0.45 (0.38–0.54) | <0.001 | N/A | N/A |
| Clonazepam | 2.40 (2.18–2.64) | <0.001 | 9.74 (4.62–20.52) | <0.001 |
| Topiramate | 1.41 (1.21–1.61) | <0.001 | 7.50 (3.40–16.54) | <0.001 |
| Phenobarbital | 0.76 (0.45–1.31) | 0.323 | N/A | N/A |
| Phenytoin | 0.32 (0.21–0.50) | <0.001 | N/A | N/A |
| Pregabalin | 0.70 (0.65–0.74) | <0.001 | 1.25 (0.57–2.73) | 0.58 |
| Primidone | 1.02 (0.64–1.63) | 0.94 | N/A | N/A |
| Gastrointestinal Disorders | ||||
| Gabapentin | 1.70 (1.60–1.80) | <0.001 | N/A | N/A |
| Divalproex | 1.01 (0.65–1.57) | 0.982 | N/A | N/A |
| Lamotrigine | 0.20 (0.15–0.27) | <0.001 | N/A | N/A |
| Lacosamide | 0.39 (0.27–0.56) | <0.001 | N/A | N/A |
| Levetiracetam | 0.93 (0.83–1.05) | 0.26 | N/A | N/A |
| Valproic acids | 0.32 (0.19–0.51) | <0.001 | N/A | N/A |
| Oxcarbazepine | 0.37 (0.29–0.48) | <0.001 | N/A | N/A |
| Zonisamide | 0.62 (0.37–1.04) | 0.068 | N/A | N/A |
| Carbamazepine | 0.42 (0.35–0.51) | <0.001 | N/A | N/A |
| Clonazepam | 1.00 (0.89–1.12) | 0.977 | N/A | N/A |
| Topiramate | 0.69 (0.59–0.82) | <0.001 | N/A | N/A |
| Phenobarbital | 0.45 (0.23–0.85) | 0.014 | N/A | N/A |
| Phenytoin | 0.28 (0.18–0.44) | <0.001 | N/A | N/A |
| Pregabalin | 1.07 (1.01–1.14) | 0.03 | N/A | N/A |
| Primidone | 0.82 (0.5–1.35) | 0.441 | N/A | N/A |
| Liver and Biliary Disorders | ||||
| Gabapentin | 0.17 (0.12–0.25) | <0.001 | 1.06 (0.48–2.36) | 0.886 |
| Lamotrigine | 1.47 (0.97–2.23) | 0.069 | 0.32 (0.12–0.89) | 0.029 |
| Lacosamide | 0.92 (0.38–2.24) | 0.857 | N/A | N/A |
| Levetiracetam | 12.82 (10.51–15.63) | <0.001 | 9.52 (5.96–15.19) | <0.001 |
| Valproic acids | 6.02 (3.83–9.48) | <0.001 | N/A | N/A |
| Oxcarbazepine | 1.48 (0.91–2.42) | 0.114 | N/A | N/A |
| Zonisamide | 3.76 (1.75–8.07) | <0.001 | N/A | N/A |
| Carbamazepine | 1.29 (0.87–1.91) | 0.203 | 0.24 (0.10–0.61) | 0.002 |
| Clonazepam | 0.92 (0.62–1.37) | 0.685 | N/A | N/A |
| Topiramate | 1.17 (0.73–1.89) | 0.512 | N/A | N/A |
| Phenobarbital | 2.50 (0.92–6.78) | 0.073 | N/A | N/A |
| Phenytoin | 4.96 (3.22–7.64) | <0.001 | 2.33 (1.08–5.07) | 0.032 |
| Pregabalin | 0.12 (0.08–0.18) | <0.001 | N/A | N/A |
| Metabolic disorders | ||||
| Gabapentin | 0.68 (0.57–0.82) | <0.001 | N/A | N/A |
| Divalproex | 1.80 (0.74–4.41) | 0.197 | N/A | N/A |
| Lamotrigine | 0.57 (0.34–0.95) | 0.03 | N/A | N/A |
| Lacosamide | 0.71 (0.32–1.60) | 0.409 | N/A | N/A |
| Levetiracetam | 1.98 (1.55–2.52) | <0.001 | N/A | N/A |
| Valproic acids | 4.62 (3.05–7.00) | <0.001 | N/A | N/A |
| Oxcarbazepine | 3.10 (2.31–4.15) | <0.001 | 12.78 (5.11–31.94) | <0.001 |
| Zonisamide | 2.40 (1.12–5.15) | 0.024 | N/A | N/A |
| Carbamazepine | 1.17 (0.84–1.63) | 0.356 | 0.93 (0.31–2.78) | 0.896 |
| Clonazepam | 1.03 (0.76–1.39) | 0.868 | N/A | N/A |
| Topiramate | 2.58 (1.99–3.36) | <0.001 | N/A | N/A |
| Pregabalin | 0.60 (0.50–0.72) | <0.001 | N/A | N/A |
| Cardiovascular Disorders | ||||
| Gabapentin | 1.34 (0.92–1.95) | 0.134 | N/A | N/A |
| Levetiracetam | N/A | N/A | 3.28 (0.95–11.30) | 0.06 |
| Carbamazepine | 0.67 (0.25–1.81) | 0.424 | N/A | N/A |
| Clonazepam | 1.26 (0.66–2.42) | 0.479 | N/A | N/A |
| Topiramate | 2.13 (1.11–4.08) | 0.023 | N/A | N/A |
| Pregabalin | 0.59 (0.38–0.91) | 0.017 | N/A | N/A |
| Heart Rate and Rhythm Disorders | ||||
| Gabapentin | 0.98 (0.67–1.43) | 0.913 | N/A | N/A |
| Levetiracetam | 1.36 (0.73–2.53) | 0.33 | N/A | N/A |
| Lacosamide | 2.45 (0.90–6.67) | 0.079 | N/A | N/A |
| Carbamazepine | 0.75 (0.31–1.84) | 0.531 | N/A | N/A |
| Clonazepam | 1.13 (0.59–2.15) | 0.722 | N/A | N/A |
| Topiramate | 4.55 (2.89–7.16) | <0.001 | N/A | N/A |
| Pregabalin | 0.54 (0.36–0.82) | 0.003 | N/A | N/A |
| Vascular (Extracardiac) Disorders | ||||
| Gabapentin | 0.75 (0.35–1.58) | 0.444 | N/A | N/A |
| Oxcarbazepine | 4.33 (1.53–12.28) | 0.006 | N/A | N/A |
| Pregabalin | 0.74 (0.36–1.54) | 0.42 | N/A | N/A |
| Respiratory Disorders | ||||
| Gabapentin | 0.76 (0.71–0.80) | <0.001 | 1.17 (0.77–1.78) | 0.465 |
| Divalproex | 0.80 (0.53–1.21) | 0.286 | 1.69 (0.55–5.19) | 0.362 |
| Lamotrigine | 0.83 (0.72–0.95) | 0.007 | 0.13 (0.09–0.18) | <0.001 |
| Lacosamide | 1.13 (0.91–1.41) | 0.26 | 7.57 (2.40–23.92) | <0.001 |
| Levetiracetam | 0.54 (0.48–0.61) | <0.001 | 0.80 (0.56–1.12) | 0.192 |
| Valproic acids | 0.52 (0.38–0.72) | <0.001 | 0.44 (0.23–0.84) | 0.013 |
| Oxcarbazepine | 0.55 (0.45–0.66) | <0.001 | 0.84 (0.48–1.46) | 0.533 |
| Zonisamide | 0.98 (0.69–1.42) | 0.908 | N/A | N/A |
| Carbamazepine | 0.77 (0.68–0.88) | <0.001 | 0.85 (0.63–1.14) | 0.273 |
| Clonazepam | 1.22 (1.11–1.34) | <0.001 | 0.56 (0.28–1.12) | 0.1 |
| Topiramate | 0.81 (0.70–0.92) | 0.001 | 0.51 (0.25–1.06) | 0.071 |
| Phenobarbital | 0.01 (0.28–0.79) | 0.471 | 2.48 (1.04–5.88) | 0.04 |
| Phenytoin | 0.34 (0.24–0.48) | <0.001 | 0.16 (0.06–0.46) | <0.001 |
| Pregabalin | 1.74 (1.65–1.83) | <0.001 | 2.89 (2.14–3.90) | <0.001 |
| Primidone | 1.40 (0.97–2.01) | 0.073 | N/A | N/A |
| Red Blood Cell Disorders | ||||
| Gabapentin | 0.62 (0.23–1.67) | 0.45 | N/A | N/A |
| Levetiracetam | 3.05 (1.04–8.96) | 0.043 | N/A | N/A |
| Oxcarbazepine | N/A | N/A | 15.71 (4.13–59.86) | <0.001 |
| Carbamazepine | 3.90 (1.33–11.47) | 0.013 | N/A | N/A |
| White Cell and RES disorders | ||||
| Gabapentin | 0.62 (0.23–1.67) | 0.345 | N/A | N/A |
| Divalproex | 4.10 (1.51–11.16) | 0.006 | N/A | N/A |
| Lamotrigine | 0.64 (0.28–1.44) | 0.281 | 0.35 (0.13–0.96) | 0.041 |
| Levetiracetam | 9.52 (7.27–12.46) | <0.001 | 3.81 (2.34–6.20) | <0.001 |
| Valproic acids | 3.94 (1.93–8.05) | <0.001 | 1.22 (0.48–3.11) | 0.682 |
| Oxcarbazepine | 2.62 (1.57–4.38) | <0.001 | 3.07 (1.51–6.26) | 0.002 |
| Carbamazepine | 2.82 (1.91–4.15) | <0.001 | 0.44 (0.21–0.93) | 0.032 |
| clonazepam | 0.48 (0.24–0.97) | 0.041 | ||
| Topiramate | 1.23 (0.65–2.34) | 0.521 | N/A | N/A |
| Phenobarbital | 5.52 (2.02–15.07) | <0.001 | N/A | N/A |
| Phenytoin | 8.20 (4.93–13.61) | <0.001 | N/A | N/A |
| Pregabalin | 0.16 (0.10–0.28) | <0.001 | N/A | N/A |
| Urinary System Disorders | ||||
| Gabapentin | 1.87 (1.59–2.19) | <0.001 | N/A | N/A |
| Divalproex | 1.49 (0.55–4.04) | 0.431 | N/A | N/A |
| Lamotrigine | 0.15 (0.06–0.41) | <0.001 | N/A | N/A |
| Levetiracetam | 0.56 (0.37–0.85) | 0.006 | N/A | N/A |
| Oxcarbazepine | 0.34 (0.15–0.75) | 0.008 | N/A | N/A |
| Zonisamide | 1.76 (0.72–4.31) | 0.213 | N/A | N/A |
| Carbamazepine | 0.55 (0.35–0.89) | 0.014 | N/A | N/A |
| Clonazepam | 1.02 (0.75–1.39) | 0.888 | N/A | N/A |
| Topiramate | 0.63 (0.60–1.36) | 0.903 | N/A | N/A |
| Phenytoin | N/A | N/A | 8.98 (3.07–26.28) | <0.001 |
| Pregabalin | 0.49 (0.79–1.12) | 0.941 | 2.29 (0.80–6.56) | 0.125 |
| Body as a Whole Disorders | ||||
| Gabapentin | 1.32 (1.21–1.44) | <0.001 | 1.30 (0.78–2.15) | 0.317 |
| Divalproex | 0.90 (0.47–1.70) | 0.734 | N/A | N/A |
| Lamotrigine | 0.87 (0.70–1.08) | 0.192 | 1.40 (0.94–2.11) | 0.102 |
| Levetiracetam | 0.72 (0.59–0.86) | <0.001 | 0.41 (0.24–0.71) | 0.002 |
| Valproic acids | 0.94 (0.61–1.45) | 0.777 | 0.58 (0.26–1.29) | 0.182 |
| Oxcarbazepine | 0.60 (0.44–0.81) | <0.001 | 0.03 (0.01–0.10) | <0.001 |
| Zonisamide | 0.07 (0.04–0.14) | <0.001 | 2.99 (0.89–10.01) | 0.076 |
| Carbamazepine | 0.92 (0.76–1.11) | 0.392 | 2.53 (1.83–3.50) | <0.001 |
| Clonazepam | 0.85 (0.72–1.01) | 0.058 | 0.82 (0.37–1.83) | 0.626 |
| Topiramate | 0.93 (0.75–1.14) | 0.469 | N/A | N/A |
| Phenobarbital | 1.46 (0.84–2.53) | 0.184 | 1.39 (0.46–4.19) | 0.554 |
| Phenytoin | 0.70 (0.45–1.08) | 0.103 | 0.91 (0.44–1.87) | 0.797 |
| Pregabalin | 1.02 (0.93–1.11) | 0.727 | 0.66 (0.42–1.05) | 0.077 |
| Primidone | 0.66 (0.31–1.42) | 0.288 | N/A | N/A |
Table 5.
Association of type of concomitantly used medication class with seriousness of ADEs.
| Types of Concomitantly Used Medication | Non-SAE (N = 35,457) | SAE (N = 1392) | Total (N = 36,809) | ROR (95%CI) | p-Value |
|---|---|---|---|---|---|
| Acid-suppression therapy | 3671 (14.49%) | 22 (6.9%) | 3693 (14.4%) | 0.14 (0.09–0.21) | <0.001 |
| H2RA | 2671 (10.54%) | 9 (2.82%) | 2680 (10.45%) | 0.08 (0.04–0.15) | <0.001 |
| PPI | 972 (3.84%) | 12 (3.76%) | 984 (3.84%) | 0.31 (0.17–0.55) | <0.001 |
| Rabeprazole | 322 (1.27%) | 9 (2.82%) | 331 (1.25%) | 0.71 (0.37–1.38) | 0.311 |
| H2RA and PPI combinations | 28 (0.11%) | 1 (0.31%) | 29 (0.11%) | 0.91 (0.12–6.68) | 0.925 |
| Acetaminophen | 4875 (19.24%) | 45 (14.11%) | 4920 (19.18%) | 0.21 (0.16–0.28) | <0.001 |
| NSAIDs | 6064 (23.94%) | 71 (22.26%) | 6135 (23.92%) | 0.26 (0.21–0.33) | <0.001 |
| Loxoprofen | 896 (3.54%) | 5 (1.57%) | 901 (3.51%) | 0.14 (0.06–0.34) | <0.001 |
| Celecoxib | 1314 (5.19%) | 24 (7.52%) | 1338 (5.22%) | 0.46 (0.30–0.68) | <0.001 |
| Aceclofenac | 1522 (6.01%) | 14 (4.39%) | 1536 (5.99%) | 0.23 (0.13–0.38) | <0.001 |
| Ibuprofen | 172 (0.68%) | 6 (1.88%) | 178 (0.69%) | 0.89 (0.39–2.01) | 0.773 |
| Multiple NSAID combinations | 64 (0.25%) | 5(1.57%) | 69 (0.27%) | 1.99 (0.80–4.96) | 0.139 |
| Opioids | 4889 (19.30%) | 37 (11.60%) | 4926 (19.20%) | 0.17 (0.12–0.24) | <0.001 |
| Tramadol | 4525 (17.86%) | 30 (9.4%) | 4555 (17.76%) | 0.15 (0.11–0.22) | <0.001 |
| Benzodiazepines | 1289 (5.09%) | 43 (13.48%) | 1332 (5.19%) | 0.84 (0.62–1.15) | 0.281 |
| Zolpidem | 293 (1.16%) | 5(1.57%) | 298 (1.16%) | 0.43 (0.18–1.05) | 0.063 |
| Antidepressants | 2182 (8.61%) | 30 (9.4%) | 2212 (8.62%) | 0.34 (0.23–0.48) | <0.001 |
| Amitriptyline | 776 (3.06%) | 9 (2.82%) | 785 (3.06%) | 0.29 (0.15–0.56) | <0.001 |
| Duloxetine | 370 (1.46%) | 5 (1.57%) | 375 (0.50%) | 0.34 (0.14–0.83) | 0.017 |
| Sedatives | 122 (0.48%) | 6(1.88%) | 128 (0.5%) | 1.25 (0.55–2.85) | 0.591 |
| Dementia treatment | 434 (1.71%) | 11 (3.45%) | 445 (1.73%) | 0.64 (0.35–1.17) | 0.148 |
| Immunosuppressants | 75 (0.30%) | 5 (1.57%) | 80 (0.31%) | 1.70 (0.69–4.21) | 0.252 |
| Chemotherapy | 132 (0.52%) | 10 (3.13%) | 142 (0.55%) | 1.93 (1.02–3.69) | 0.045 |
| Antihyperglycemic Treatments | 454 (1.79%) | 7 (2.19%) | 461 (1.8%) | 0.39 (0.18–0.82) | 0.013 |
Abbreviations: H2RA—histamine-2 receptor antagonists; PPI—proton pump inhibitor.
Table 6.
System organ class (SOC)-based ADEs caused by anticonvulsants in elderly patients.
| System Organ Class | Non-SAE (N = 18,566) | SAE (N = 589) | Total (N = 19,155) | ROR | p-Value |
|---|---|---|---|---|---|
| Skin and appendages disorders | 1479 (7.97%) | 114 (19.35%) | 1593 (8.32%) | 2.77 (2.25–3.43) | <0.001 |
| Musculoskeletal system disorders | 103 (0.55%) | 2 (0.34%) | 105 (0.55%) | 0.61 (0.15–2.48) | 0.491 |
| Collagen disorders | 1 (0.01%) | 0 (0.00%) | 1 (0.01%) | N/A | N/A |
| Central and peripheral nervous system disorders | 4562 (24.57%) | 75 (12.73%) | 4637 (24.21%) | 0.45 (0.35–0.57) | <0.001 |
| Vision disorders | 119 (0.64%) | 1 (0.17%) | 120 (0.63%) | 0.26 (0.04–1.89) | 0.185 |
| Hearing and vestibular disorders | 7 (0.04%) | 1 (0.17%) | 8 (0.04%) | 4.51 (0.55–36.71) | 0.159 |
| Special senses and other disorders | 17 (0.09%) | 0 (0.00%) | 17 (0.09%) | N/A | N/A |
| Psychiatric disorders | 2342 (12.61%) | 19 (3.23%) | 2361 (12.33%) | 0.23 (0.15–0.37) | <0.001 |
| Gastrointestinal system disorders | 2886 (15.54%) | 3 (0.51%) | 2889 (15.08%) | 0.03 (0.01–0.09) | <0.001 |
| Liver and biliary system disorders | 169 (0.91%) | 32 (5.43%) | 201 (1.05%) | 6.25 (4.25–9.21) | <0.001 |
| Metabolic and nutritional disorders | 269 (1.45%) | 14 (2.38%) | 283 (1.48%) | 1.66 (0.96–2.85) | 0.069 |
| Endocrine disorders | 0 (0.00%) | 3 (0.51%) | 3 (0.02%) | N/A | N/A |
| General cardiovascular disorders | 62 (0.33%) | 6 (1.02%) | 68 (0.35%) | 3.07 (1.32–7.13) | 0.009 |
| Heart rate and rhythm disorders | 55 (0.3%) | 4 (0.68%) | 59 (0.31%) | 2.30 (0.83–6.37) | 0.109 |
| Vascular (extracardiac) disorders | 21 (0.11%) | 0 (0.00%) | 21 (0.11%) | N/A | N/A |
| Respiratory system disorders | 4525 (24.37%) | 191 (32.43%) | 4716 (24.62%) | 1.49 (1.25–1.78) | <0.001 |
| Red blood cell disorders | 11 (0.06%) | 0 (0.00%) | 11 (0.06%) | N/A | N/A |
| White cell and RES disorders | 92 (0.50%) | 30 (5.09%) | 122 (0.64%) | 10.78 (7.08–16.41) | <0.001 |
| Platelet, bleeding, and clotting disorders | 29 (0.16%) | 0 (0.00%) | 29 (0.15%) | N/A | N/A |
| Urinary system disorders | 415 (2.24%) | 11 (1.87%) | 426 (2.22%) | 0.83 (0.46–1.52) | 0.552 |
| Reproductive disorders, male | 1 (0.01%) | 0 (0.00%) | 1 (0.01%) | N/A | N/A |
| Reproductive disorders, female | 2 (0.01%) | 0 (0.00%) | 2 (0.01%) | N/A | N/A |
| Body as a whole—general disorders | 1364 (7.35%) | 83 (14.09%) | 1447 (7.55%) | 2.07 (1.63–2.63) | <0.001 |
| Application site disorders | 1 (0.01%) | 0 (0.00%) | 1 (0.01%) | N/A | N/A |
| Resistance mechanism disorders | 2 (0.01%) | 0 (0.00%) | 2 (0.01%) | N/A | N/A |
| Secondary terms—events | 32 (0.17%) | 0 (0.00%) | 32 (0.17%) | N/A | N/A |
Abbreviations: N/A—not applicable; RES—reticuloendothelial system; ROR—reporting odds ratio; SAE—serious adverse events
Table 7.
Association of types of concomitantly used medication class with seriousness of ADEs in elderly patients.
Table 7.
Association of types of concomitantly used medication class with seriousness of ADEs in elderly patients.
| Types of Concomitantly Used Medication | Non-SAE (N = 18,566) | SAE (N = 589) | Total (N = 19,155) | ROR (95%CI) | p-Value |
|---|---|---|---|---|---|
| Acid-suppression therapy | 2050 (14.23%) | 9 (6.21%) | 2059 (14.15%) | 0.13 (0.07–0.24) | <0.001 |
| Acetaminophen | 2990 (20.75%) | 24 (16.55%) | 3014 (20.71%) | 0.22 (0.15–0.33) | <0.001 |
| NSAIDs | 3482 (24.16%) | 40 (27.59%) | 3522 (24.20%) | 0.32 (0.23–0.44) | <0.001 |
| Celecoxib | 975 (6.77%) | 11 (7.59%) | 986 (6.77%) | 0.34 (0.19–0.63) | <0.001 |
| Aceclofenac | 815 (5.66%) | 7 (4.83%) | 822 (5.65%) | 0.26 (0.12–0.55) | <0.001 |
| Opioids | 3056 (21.21%) | 22 (15.17%) | 3078 (21.15%) | 0.20 (0.13–0.30) | <0.001 |
| Tramadol | 2829 (19.63%) | 18 (12.41%) | 2847 (19.56%) | 0.18 (0.11–0.28) | <0.001 |
| Benzodiazepines | 443 (3.07%) | 12 (8.28%) | 455 (3.13%) | 0.85 (0.48–1.52) | 0.585 |
| Antidepressants | 1106 (7.67%) | 9 (6.21%) | 1115 (7.66%) | 0.25 (0.13–0.48) | <0.001 |
| Antipsychotics | 189 (1.31%) | 6 (4.14%) | 195 (1.34%) | 1.00 (0.44–2.27) | 0.999 |
| Dementia treatment | 361 (2.51%) | 9 (6.21%) | 370 (2.54%) | 0.78 (0.40–1.52) | 0.471 |
| Antihyperglycemic Treatments | 330 (2.29%) | 5 (3.45%) | 335 (2.30%) | 0.47 (0.20–1.15) | 0.098 |
Table 8.
Univariate and multivariate analyses for the identification of predictors of SAEs in the general population.
Table 8.
Univariate and multivariate analyses for the identification of predictors of SAEs in the general population.
| Predictors | Number of Cases (%) | Univariate Analysis | Multivariate Analysis | |||
|---|---|---|---|---|---|---|
| Nonserious AEs (N = 35,417) | SAEs (N = 1392) | OR (95% CI) | p-Value | OR (95% CI) | p-Value | |
| Sex | ||||||
| Male | 12,008 (33.90%) | 648 (46.55%) | 1.70 (1.53–1.89) | <0.001 | 1.36 (1.21–1.52) | <0.001 |
| Female | 23,409 (66.10%) | 744 (53.45%) | Reference | Reference | ||
| Age | ||||||
| 0–9 | 772 (2.18%) | 55 (3.95%) | 0.98 (0.96–1.00) | 0.039 | N/A | N/A |
| 10–19 | 792 (2.24%) | 95 (6.82%) | ||||
| 20–29 | 1818 (5.13%) | 128 (9.20%) | ||||
| 30–39 | 2493 (7.04%) | 138 (9.91%) | ||||
| 40–49 | 4036 (11.40%) | 154 (11.06%) | ||||
| 50–59 | 6940 (19.60%) | 233 (16.74%) | ||||
| 60–69 | 8501 (24.00%) | 256 (18.39%) | ||||
| 70–79 | 7344 (20.74%) | 232 (16.67%) | ||||
| 80–89 | 2561 (7.23%) | 94 (6.75%) | ||||
| 90–99 | 160 (0.45%) | 7 (0.50%) | ||||
| Causality | ||||||
| Certain | 464 (1.31%) | 50 (3.59%) | Reference | <0.001 | reference | <0.001 |
| Probable/likely | 7003 (19.77%) | 474 (34.05%) | 0.62 (0.44–0.85) | 0.004 | 0.63 (0.46–0.87) | 0.005 |
| Possible | 27,950 (78.92%) | 868 (62.36%) | 0.27 (0.20–0.37) | <0.001 | 0.47 (0.34–0.64) | <0.001 |
| Number of concurrently used medications | ||||||
| 1 | 17,166 (48.47%) | 898 (64.51%) | 0.67 (0.64–0.71) | <0.001 | 0.89 (0.84–0.94) | <0.001 |
| 2 | 3901 (11.01%) | 231 (16.59%) | ||||
| 3 | 3897 (11.00%) | 121 (8.69%) | ||||
| 4 | 3535 (9.98%) | 54 (3.88%) | ||||
| ≥5 | 6918 (19.35%) | 88 (6.32%) | ||||
| Types of concomitantly used medication | ||||||
| Acid-suppressive therapy | 3671 (10.37%) | 22 (1.58%) | 0.17 (0.11–0.26) | <0.001 | 0.38 (0.24–0.60) | <0.001 |
| Anesthetics | 15 (0.04%) | 0 (0.00%) | N/A | N/A | N/A | N/A |
| Steroids | 11 (0.03%) | 0 (0.00%) | N/A | N/A | N/A | N/A |
| Acetaminophen | 4875 (13.76%) | 45 (3.23%) | N/A | N/A | N/A | N/A |
| NSAIDs | 6064 (17.12%) | 71 (5.10%) | N/A | N/A | N/A | N/A |
| Opioids | 4889 (13.80%) | 37 (2.66%) | 0.20 (0.14–0.28) | <0.001 | 0.62 (0.43–0.89) | 0.010 |
| Benzodiazepines | 1289 (3.64%) | 43 (3.09%) | N/A | N/A | N/A | N/A |
| Neurological disorders | 217 (0.61) | 3 (0.22%) | N/A | N/A | N/A | N.A |
| Zolpidem | 293 (0.83%) | 5 (0.36%) | N/A | N/A | N/A | N/A |
| Antidepressants | 2182 (6.16%) | 30 (2.16%) | 0.35 (0.24–0.50) | <001 | 0.57 (0.39–0.84) | 0.005 |
| Sedatives | 122 (0.34%) | 6 (0.43%) | N/A | N/A | N/A | N/A |
| Antipsychotics | 611 (1.73%) | 24 (1.72$) | N/A | N/A | N/A | N/A |
| Dementia treatment | 434 (1.23%) | 11 (0.79%) | N/A | N/A | N/A | N/A |
| Immunosuppressants | 75 (0.21%) | 5 (0.36%) | 2.62 (1.04–6.64) | 0.042 | 3.68 (1.39–9.74) | 0.009 |
| Chemotherapy | 132 (0.37%) | 10 (0.72%) | 2.51 (1.30–4.83) | 0.006 | 2.22 (1.11–4.45) | 0.024 |
| Diabetic medications | 454 (1.28%) | 7 (0.50%) | 0.42 (0.20–0.89) | 0.024 | N/A | N/A |
| Anticonvulsant Types | ||||||
| Gabapentin | 10,949 (30.91%) | 113 (8.12%) | 0.43 (0.32–0.58) | <0.001 | 0.42 (0.32–0.54) | <0.001 |
| Divalproex | 156 (0.44%) | 13 (0.93%) | 3.48 (1.89–6.42) | <0.001 | 3.06 (1.69–5.57) | <0.001 |
| Lamotrigine | 1427 (4.03%) | 184 (13.22%) | 5.39 (4.08–7.11) | <0.001 | 3.43 (2.70–4.35) | <0.001 |
| Lacosamide | 462 (1.30%) | 15 (1.08%) | N/A | N/A | N/A | N/A |
| Levetiracetam | 2292 (6.47%) | 209 (15.01%) | 3.81 (2.91–4.99) | <0.001 | 2.61 (2.08–3.28) | <0.001 |
| Valproic acid | 328 (0.93%) | 76 (5.46%) | 9.68 (6.89–13.60) | <0.001 | 7.88 (5.77–10.77) | <0.001 |
| Ethosuximide | 2 (0.01%) | 0 (0.00%) | N/A | NA | N/A | N/A |
| Oxcarbazepine | 996 (2.81%) | 71 (5.10%) | 2.98 (2.13–1.16) | <0.001 | 1.98 (1.46–2.67) | <0.001 |
| Zonisamide | 166 (0.47%) | 12 (0.86%) | 3.02 (1.61–5.67) | <0.001 | 2.15 (1.16–3.98) | 0.014 |
| Carbamazepine | 1817 (5.13%) | 281 (20.19%) | 6.46 (4.97–8.40) | <0.001 | 4.47 (3.59–5.57) | <0.001 |
| Clonazepam | 2483 (7.01%) | 57 (4.09%) | N/A | N/A | N/A | N/A |
| Topiramate | 1521 (4.29%) | 55 (3.95%) | 1.51 (1.06–2.15) | 0.022 | N/A | N/A |
| Phenobarbital | 140 (0.40%) | 21 (1.51%) | 6.27 (3.75–10.47) | <0.001 | 4.49 (2.73–7.36) | <0.001 |
| Phenytoin | 432 (1.22%) | 66 (4.74%) | 6.38 (4.51–9.03) | <0.001 | 4.02 (2.92–5.52) | <0.001 |
| Pregabalin | 12,101 (34.17%) | 217 (15.59%) | 0.75 (0.75–0.98) | 0.034 | 0.63 (0.50–0.79) | <0.001 |
| Primidone | 145 (0.41%) | 2 (0.14%) | N/A | N/A | N/A | N/A |
Table 9.
Univariate and multivariate analyses for the identification of predictors for SAEs in elderly patients.
Table 9.
Univariate and multivariate analyses for the identification of predictors for SAEs in elderly patients.
| Predictors | Number of Cases (%) | Univariate Analysis | Multivariate Analysis | |||
|---|---|---|---|---|---|---|
| Nonserious AEs (N = 18,566) | SAEs (N = 589) | OR (95% CI) | p-Value | OR (95% CI) | p-Value | |
| Sex | ||||||
| Male | 6090 (32.8%) | 306 (51.95%) | 2.22 (1.88–2.61) | <0.001 | 1.91 (1.62–2.27) | <0.001 |
| Female | 12,476 (67.2%) | 283 (48.05%) | reference | Reference | ||
| Age | ||||||
| 60–69 | 8501 (45.79%) | 256 (43.46%) | N/A | N/A | 1.17 (1.04–1.31) | 0.007 |
| 70–79 | 7344 (39.56%) | 232 (39.39%) | ||||
| 80–89 | 2561 (13.79%) | 94 (15.96%) | ||||
| 90–99 | 160 (0.86%) | 7 (1.19%) | ||||
| Causality | ||||||
| Certain | 252 (1.36%) | 18 (3.06%) | Reference | <0.001 | Reference | 0.002 |
| Probable/likely | 3397 (1.8.30%) | 175 (29.71%) | 0.72 (0.44–1.19) | 0.202 | 0.76 (0.45–1.28) | 0.298 |
| Possible | 14,917 (80.35%) | 396 (67.23%) | 0.37 (0.23–0.61) | <0.001 | 0.52 (0.34–0.94) | 0.027 |
| Number of concurrently used medications | ||||||
| 1 | 8735 (47.05%) | 385 (65.37%) | 0.72 (0.67–0.76) | <0.001 | N/A | N/A |
| 2 | 2100 (11.31%) | 86 (14.6%) | ||||
| 3 | 2080 (11.20%) | 46 (7.81%) | ||||
| 4 | 1892 (10.19%) | 28 (4.75%) | ||||
| ≥5 | 3759 (20.25%) | 44(7.47%) | ||||
| Types of concomitantly used medication | ||||||
| Acid Suppressive Therapy | 2050 (11.04%) | 9 (1.53%) | 0.21 (0.11–0.42) | <0.001 | 0.23 (0.12–0.46) | <0.001 |
| NSAIDs | 3482 (18.75%) | 40 (6.79%) | 0.50 (0.36–0.70) | <0.001 | N/A | N/A |
| Opioids | 3056 (16.46%) | 22 (3.74%) | 0.26 (0.17–0.40) | <0.001 | 0.43 (0.28–0.67) | <0.001 |
| Antidepressants | 1106 (5.96%) | 9 (1.53%) | 0.25 (0.13–0.48) | <0.001 | 0.34 (0.17–0.66) | 0.002 |
| Neurological disorders | 106 (0.57%) | 1 (0.17%) | N/A | N/A | N/A | N/A |
| Anesthetics | 12 (0.06%) | 0 (0.00%) | N/A | N/A | N/A | N/A |
| Steroids | 9 (0.05%) | 0 (0.00%) | N/A | N/A | N/A | N/A |
| Acetaminophen | 2990 (16.10%) | 24 (4.07%) | N/A | N/A | N/A | N/A |
| Benzodiazepines | 443 (2.39%) | 12 (2.04%) | N/A | N/A | N/A | N/A |
| Zolpidem | 136 (0.73%) | 4 (0.68%) | N/A | N/A | N/A | N/A |
| Antipsychotics | 189 (1.02%) | 6 (1.02%) | N/A | N/A | N/A | N/A |
| Sedatives | 45 (0.24%) | 0 (0.00%) | N/A | N/A | N/A | N/A |
| Dementia treatment | 361 (1.94%) | 9 (1.53%) | N/A | N/A | N/A | N/A |
| Immunosuppressants | 31 (0.17%) | 1 (0.17%) | N/A | N/A | N/A | N/A |
| Chemotherapy | 65 (0.35%) | 3 (0.51%) | N/A | N/A | N/A | N/A |
| Diabetic medications | 330 (1.78%) | 5 (0.85%) | N/A | N/A | N/A | N/A |
| Anticonvulsant Types | ||||||
| Gabapentin | 6258 (33.71%) | 65 (11.04%) | 0.20 (0.14–0.30) | <001 | 0.26 (0.18–0.39) | <001 |
| Divalproex | 27 (0.15%) | 1 (0.17%) | N/A | N/A | N/A | N/A |
| Lamotrigine | 285 (1.54%) | 40 (6.79%) | 2.73 (1.75–4.28) | <001 | 2.26 (1.43–3.56) | <001 |
| Lacosamide | 125 (0.67%) | 9 (1.53%) | N/A | N/A | N/A | N/A |
| Levetiracetam | 801 (4.31%) | 81 (13.75%) | 1.97 (1.35–2.88) | <001 | 1.83 (1.25–2.69) | 0.002 |
| Valproic | 80 (0.43%) | 15 (2.55%) | 3.64 (1.95–6.85) | <001 | 3.70 (1.96–7.00) | <001 |
| Oxcarbazepine | 248 (1.34%) | 14 (2.38%) | N/A | N/A | N/A | N/A |
| Zonisamide | 60 (0.32%) | 5 (0.85%) | N/A | N/A | N/A | N/A |
| Carbamazepine | 851 (4.58%) | 122 (20.71%) | 2.79 (1.95–3.99) | <001 | 2.78 (1.94–3.98) | <001 |
| Clonazepam | 1361 (7.33%) | 32 (5.43%) | 0.46 (0.29–0.73) | <001 | 0.53 (0.33–0.85) | 0.008 |
| Topiramate | 237 (1.28%) | 10 (1.70%) | N/A | N/A | N/A | N/A |
| Phenobarbital | 46 (0.25%) | 3 (0.51%) | N/A | N/A | N/A | N/A |
| Phenytoin | 136 (0.73%) | 27 (4.58%) | 3.87 (2.32–6.45) | <001 | 3.33 (1.98–5.58) | <001 |
| Pregabalin | 7937 (42.75%) | 163 (27.67%) | 0.40 (0.29–0.56) | <001 | 0.47 (0.33–0.66) | <001 |
| Primidone | 114 (0.61%) | 2 (0.34%) | N/A | N/A | N/A | N/A |
Table 10.
ROR calculation.
| Specific Adverse Events | All other Adverse Events | |
|---|---|---|
| Specific Drug | A | B |
| All Other Drugs | C | D |
| ROR = (A/B)/(C/D) | ||
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Lee, S.H.; Sung, D.H.; Cho, E.; Min, J.; Shin, S.; Choi, Y.J. Investigation into Safety Profiles of Antiepileptic Drugs and Identification of Predictors for Serious Adverse Events: Insights from National Pharmacovigilance Data. Pharmaceuticals 2025, 18, 1013. https://doi.org/10.3390/ph18071013
AMA Style
Lee SH, Sung DH, Cho E, Min J, Shin S, Choi YJ. Investigation into Safety Profiles of Antiepileptic Drugs and Identification of Predictors for Serious Adverse Events: Insights from National Pharmacovigilance Data. Pharmaceuticals. 2025; 18(7):1013. https://doi.org/10.3390/ph18071013
Chicago/Turabian StyleLee, Soo Hyeon, Dae Hyeon Sung, Euna Cho, Jeongah Min, Sooyoung Shin, and Yeo Jin Choi. 2025. "Investigation into Safety Profiles of Antiepileptic Drugs and Identification of Predictors for Serious Adverse Events: Insights from National Pharmacovigilance Data" Pharmaceuticals 18, no. 7: 1013. https://doi.org/10.3390/ph18071013
APA StyleLee, S. H., Sung, D. H., Cho, E., Min, J., Shin, S., & Choi, Y. J. (2025). Investigation into Safety Profiles of Antiepileptic Drugs and Identification of Predictors for Serious Adverse Events: Insights from National Pharmacovigilance Data. Pharmaceuticals, 18(7), 1013. https://doi.org/10.3390/ph18071013
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
