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Logo of neurologyNeurologyAmerican Academy of Neurology
Neurology. 2011 January 18; 76(3): 273–279.
PMCID: PMC3034391

Adverse antiepileptic drug effects in new-onset seizures

A case-control study
P. Perucca, MD,corresponding author A. Jacoby, PhD, A.G. Marson, MD, G.A. Baker, PhD, S. Lane, PhD, E.K.T. Benn, MPH, D.J. Thurman, MD, MPH, W.A. Hauser, MD, F.G. Gilliam, MD, MPH, and D.C. Hesdorffer, MPH, PhD



Adverse effects (AEs) are a major concern when starting antiepileptic drug (AED) treatment. This study quantified the extent to which AE reporting in people with new-onset seizures started on AEDs is attributable to the medication per se, and investigated variables contributing to AE reporting.


We pooled data from 2 large prospective studies, the Multicenter Study of Early Epilepsy and Single Seizures and the Northern Manhattan Study of incident unprovoked seizures, and compared adverse event profile (AEP) total and factor scores between adult cases prescribed AEDs for new-onset seizures and untreated controls, adjusting for several demographic and clinical variables. Differences in AEP scores were also tested across different AED monotherapies and controls, and between cases and controls grouped by number of seizures.


A total of 212 cases and 206 controls were identified. Most cases (94.2%) were taking low AED doses. AEP scores did not differ significantly between the 2 groups. Depression, female gender, symptomatic etiology, younger seizure onset age, ≥2 seizures, and history of febrile seizures were associated with higher AEP scores. There were no significant differences in AEP scores across different monotherapies and controls. AEP scores increased in both cases and controls with increasing number of seizures, the increment being more pronounced in cases.


When AED treatment is started at low doses following new-onset seizures, AE reporting does not differ from untreated individuals. Targeting specific factors affecting AE reporting could lead to improved tolerability of epilepsy treatment.

For individuals with newly diagnosed epilepsy starting antiepileptic drug (AED) treatment, adverse effects (AEs) are the most common concern. Cognitive impairment, coordination disturbances, sedation problems, and mood dysfunction affect up to 60% of people taking AEDs,1,2 adversely impacting everyday functioning.3 Idiosyncratic AEs are more frequent with AEDs than with other agents,4 and lead to substantial morbidity and mortality.5

Counseling about tolerability and safety of AEDs relies on information from randomized trials and observational studies. Most randomized trials, however, are designed to address regulatory requirements6 and their results are not easily applicable to routine practice.7,8 In particular, these trials use strict inclusion/exclusion criteria, apply minimally flexible titration schedules and fixed dose ranges, and rarely use self-report measures to detect and quantify AEs.9 Observational studies better reflect clinical practice, but outcomes assessment is hampered by methodologic shortcomings, including inadequate control for potential confounders.

Scant well-designed research has used reliable and valid self-report measures to define and quantify the burden of AED-related toxicity in individuals with new-onset seizures. Moreover, limited data are available on which variables other than AEDs influence AE reporting. Providing these data are a prerequisite for an evidence-based counseling regarding tolerability and safety of treatments used in newly diagnosed epilepsy.

We pooled individual patient data from 2 large prospective studies10,11 of new-onset seizures, and performed a case-control analysis to 1) quantify the extent to which adverse AED effects detected by a reliable and valid self-report measure can be attributed to medication per se, and 2) investigate other variables contributing to AE reporting.


Original studies.

Our analysis is based on pooled individual patient data from the Multicenter Study of Early Epilepsy and Single Seizures (MESS) and the Northern Manhattan Study of incident unprovoked seizures and newly diagnosed epilepsy (NMS). The designs of these studies have been described in detail previously.10,11 In summary, MESS was a pragmatic, parallel-group, unblinded, randomized trial conducted in 83 countries worldwide, comparing immediate vs deferred AED treatment in individuals with new-onset unprovoked seizures. In subjects randomly assigned to immediate treatment, choice of AED and dose was made by the treating physician according to usual practice. Participants in the United Kingdom aged ≥16 years and without any major learning disability were asked to complete an extensive battery of reliable and valid health assessments, including the adverse event profile (AEP),12,13 which was mailed as soon as possible following randomization. A reminder was mailed to nonresponders 3 weeks after the initial mailing, and those failing to respond were contacted by telephone after a further 3-week period.

NMS was a population-based prospective study of the incidence of first unprovoked seizures and newly diagnosed epilepsy among residents in the Northern Manhattan communities of Washington Heights and Inwood, NY. Several hospitals were included in the case ascertainment, but only people identified at New York Presbyterian Hospital and at The Allen Pavilion were interviewed. In NMS, prescription of AED treatment, if indicated, was left to managing physicians according to usual practice. Cases identified and eligible for the interview were sent a letter explaining the study. After 2 weeks, if subjects had not called to refuse, they were contacted by telephone and invited to participate. Those who accepted underwent a comprehensive structured interview, including the AEP items.

Standard protocol approvals, registrations, and patient consent.

Study protocols were approved by the ethics committee at each participating institution. All participants gave written informed consent. MESS is registered at (ISRCTN 98767960).


Selection criteria for the present analysis included 1) age ≥16 years; 2) history of ≥1 unprovoked seizure; 3) available AED exposure data; and 4) available AEP data within 2 months of enrollment in each study. Additionally, we excluded participants with <50% of AEP items available (n = 36) and those with <4 weeks of AED treatment when completing the AEP (n = 11). For each subject, the following data were included: age, gender, age at seizure onset, seizure type, etiology, number of seizures before enrollment, history of febrile seizures, comorbid depression, and AED type and dosing. Cases were defined as individuals who were prescribed AEDs and controls were untreated individuals.

Measures and assessments.

Adverse event profile.

The AEP is a widely used component of the clinician's armamentarium to evaluate the interictal state,14 developed at the University of Liverpool to detect the most frequent AEs of AEDs.12 The reliability, validity, and responsiveness to change of this instrument have been independently assessed13 and demonstrated in prior large epilepsy studies.2,15 It consists of 19 items assessing the frequency of occurrence of different AEs during the previous 4 weeks using a 4-point Likert scale, with 4 corresponding to more frequent occurrence. Individual ratings can be added to yield a total score ranging from 19 to 76. We also calculated scores for each of the 5 biologically plausible factors into which AEP items were found to segregate3: cognition/coordination, mood/emotion, sleep, weight/cephalgia, and tegument/mucosa. To facilitate interpretation, we divided weight/cephalgia into 2 separate factors, i.e., “weight” and “cephalgia.” Each factor score ranges from 0 to 100, with higher scores being indicative of a greater burden of AEs. Higher scores for each factor have also been found to be associated with worse health-related quality of life.3 In both MESS and NMS, the AEP was administered to participants, regardless of whether or not they were prescribed an AED. They were presented a list of items preceded by the heading “During the last 4 weeks, have you had any of the problems listed below?” No indication was given that these items could be related to AED use.

Seizure type.

Seizures were categorized as simple partial, complex partial, secondary generalized tonic-clonic, other (unknown whether primary or secondary generalized) tonic-clonic, myoclonic, other, or a combination of 2 or more different types.


Seizure etiology was categorized as remote/progressive symptomatic or idiopathic/cryptogenic.16 A remote/progressive symptomatic etiology was defined by the presence of a static or nonstatic CNS insult increasing the risk for seizures (e.g., stroke, head trauma, meningitis/encephalitis, perinatal hypoxic/ischemic injury, degenerative neurologic disorders).16 An idiopathic/cryptogenic etiology was defined by the absence of identifiable acute precipitating factors or prior neurologic insults.

Psychiatric assessment.

Mood status was determined by the depression subscale of the Hospital Anxiety and Depression Scale (HADS)17 in MESS and by the Diagnostic Interview Schedule for Children (DISC)18 in NMS. The HADS is a self-completed instrument consisting of two 7-item subscales scored on a 4-point Likert scale, one for anxiety and the other for depression. Both subscales have undergone extensive reliability and validity testing in different chronic conditions,19,20 including epilepsy.21 The HADS depression subscale yields a total score ranging from 0 to 21, with scores ≥11 suggesting the presence of symptoms of depression. The DISC is a structured interview for individuals aged 6 years or older, through which a diagnosis of major depression can be made, according to DSM-IV criteria.22 The DISC was used in the development of the most recent version of the Diagnostic Interview Schedule (DIS).23

Study endpoints.

The primary endpoint of the study was to compare AEP total and factor scores between subjects started on AED treatment (cases) and those for whom AED treatment was not prescribed (controls). This comparison was complemented by an investigation of the individual contributions to AEP total and factor scores among a large set of demographic and clinical variables.

Secondary endpoints included a comparison of AEP scores between cases and controls grouped by number of seizures prior to enrollment and an assessment of differences in AEP scores across subjects on each of the most common AED monotherapies and controls.

Statistical analysis.

We used one-way analysis of variance to compare continuous data and the χ2 test to compare categorical data. Separate multivariate linear regression models were constructed to compare the AEP total score and each factor score between cases and controls, adjusting for study (MESS vs NMS), gender, age at seizure onset, seizure type, etiology, number of seizures (single vs 2 or more), history of febrile seizures, and comorbid depression. Comorbid depression was excluded from the model assessing differences in mood/emotion scores due to overlapping between independent and dependent variables. When testing for differences in AEP scores between cases and controls grouped by number of seizures prior to enrollment (1; 2 or 3; and 4 or more), and for differences in AEP scores across the most common AED monotherapies and controls, the Scheffe test was used to detect significant between-group differences, while adjusting for multiple comparisons. Individual means were imputed for missing AEP values.24

Significance was set at p ≤ 0.05. All analyses were performed with SAS (SAS Institute, Cary, NC).


Subject characteristics.

A total of 418 subjects (326 from MESS and 92 from NMS) were eligible for this analysis. Subjects from the 2 studies were similar for most factors. Compared to NMS participants, MESS subjects had a younger age at enrollment (mean ± SD: 37.7 ± 18.6 vs 51.6 ± 24.1; p < 0.0001) and at seizure onset (36.7 ± 18.9 vs 51.1 ± 24.4; p < 0.0001), and a higher proportion of other tonic-clonic seizures (57.7% vs 29.7%; p < 0.0001) and idiopathic/cryptogenic etiology (85.3% vs 47.8%; p < 0.0001). Of 418 subjects, 212 were cases prescribed AEDs and 206 controls not prescribed AEDs. As shown in table 1, the only between-group difference in terms of seizure type distribution was a higher proportion of other tonic-clonic seizures in controls than in cases (61.5% vs 42.0%, p < 0.01). Among the 212 cases, 191 (90.1%) were taking a single AED and 21 (9.9%) were taking 2 or more AEDs. The most common monotherapies were valproic acid (VPA, n = 85), carbamazepine (CBZ, n = 62), phenytoin (PHT, n = 16), levetiracetam (LEV, n = 13), and lamotrigine (LTG, n = 10). Of the 173 monotherapy cases with available dosing data, 163 (94.2%) were taking low daily AED doses (VPA, ≤1,000 [range 400–1,000] mg/day; CBZ, ≤600 [200–800] mg/day; PHT, ≤300 [200–900] mg/day; LEV, ≤1,000 [250–3,000] mg/day; LTG, ≤200 [100–300] mg/day).25

Table 1
Demographic and clinical characteristics of subjects started on AEDs (cases) and those in whom AEDs were withheld (controls)

Comparison of AEP total and factor scores between cases and controls and evaluation of the effect of several covariates.

As the standardized regression coefficients in table 2 show, there were no differences between cases and controls in AEP total scores (mean ± SD: 37.3 ± 11.3 vs 36.5 ± 12.2; p = 0.75), nor in scores for cognition/coordination (32.6 ± 24.5 vs 29.1 ± 25.6; p = 0.28), mood/emotion (34.9 ± 30.0 vs 33.4 ± 30.6; p = 0.95), sleep (41.5 ± 26.7 vs 39.8 ± 27.6; p = 0.73), weight (22.9 ± 34.3 vs 20.1 ± 34.5; p = 0.21), cephalgia (39.8 ± 37.5 vs 41.2 ± 38.3; p = 0.83), and tegument/mucosa (12.7 ± 18.9 vs 15.7 ± 19.6; p = 0.06).

Table 2
Comparison of AEP total and factor scores between cases and controls (AED exposure), while controlling for the individual effect of several covariatesa

Four variables emerged as predictors of higher AEP total scores. Depression was the strongest predictor, followed by female gender, symptomatic etiology, and younger age at seizure onset. The model explained 19% of the variance in total AEP scores.

As for AEP factor scores, depression was the strongest predictor of higher cognition/coordination scores, followed by symptomatic etiology and female gender, with the model explaining 20% of the variance. Younger age at seizure onset was the strongest predictor of higher mood/emotion scores, followed by symptomatic etiology, history of 2 or more seizures, and enrollment in NMS, with the model accounting for 8% of the variance. Depression was the strongest predictor of higher sleep scores, followed by female gender, symptomatic etiology, and younger age at seizure onset, with the model explaining 11% of the variance. Female gender and enrollment in MESS were associated with higher weight scores, with the model explaining 7% of the variance. Younger age at seizure onset was the strongest predictor of higher cephalgia scores, followed by female gender, depression, and history of febrile seizures, with the model explaining 13% of the variance. Depression and female gender were associated with higher tegument/mucosa scores, with the model accounting for 7% of the variance.

Results were largely unchanged after performing the analyses separately on the MESS and the NMS cohorts. To further disentangle any relationship between psychiatric items in the AEP and depressed mood, we additionally excluded depression from the models assessing differences in AEP total and sleep scores. This had no effect on the findings (results not shown).

Comparison of AEP total and factor scores between cases and controls grouped by number of seizures prior to enrollment.

In total, 370 subjects (88.5% of the entire sample) with detailed data on number of seizures prior to enrollment qualified for this analysis. As shown in table 3, the AEP total score increased with the increasing number of seizures both in cases and controls (p < 0.01). A similar pattern was found for 4 AEP factors scores, i.e., cognition/coordination (p < 0.01), mood/emotion, sleep, and cephalgia (all p < 0.05). The AEP total score and cognition/coordination and sleep scores progressively worsened in cases compared to controls with the increasing number of seizures, differences in scores being significant between controls with one seizure and cases with 4 or more seizures.

Table 3
Comparison of AEP total and factor scores between cases and controls grouped by number of seizures prior to enrollment (1; 2 or more; and 4 or more)a

Assessment of differences in AEP total and factor scores across the most common AED monotherapies and controls.

Although weight scores tended to be lower in subjects taking LEV compared to other groups, no significant between-group differences were found in AEP total or factor scores across VPA, CBZ, PHT, LEV, and LTG monotherapy groups and controls (table 4).

Table 4
Assessment of differences in AEP total and factor scores across the most common AED monotherapies and controls


We performed a case-control analysis on 418 patients pooled from 2 large prospective studies of new-onset seizures to quantify the extent to which AE reporting as measured by a validated screening instrument, the AEP, can be reliably attributed to AED treatment, and to investigate which variables contribute to the different AEs reported.

In line with prior observations on similar samples,26 we found low AEP scores in individuals newly started on AED treatment. The vast majority of these subjects were taking a single AED at low daily doses. When compared to a control group with new-onset seizures in which AED treatment was withheld, no significant difference was found in the AEP total score or in each of the 6 factor scores. Moreover, a comparison of AEP scores across the 5 most commonly used AEDs and untreated controls did not yield any significant between-group difference. These findings suggest that when an AED is started at low daily doses in patients with new-onset seizures, it is generally well-tolerated. Considering that almost 50% of people with newly diagnosed epilepsy become seizure-free on the first-ever prescribed AED,27 and that more than 90% of these do so at low or moderate doses,25 the desirable goal of epilepsy medical management—“no seizures–no AEs”—may be accomplished in a relevant proportion of patients. These subjects may largely account for those seizure-free individuals who have quality of life ratings similar to the general population.28 Overall, these findings have important implications for better patient counseling regarding initiation of AED treatment.

We identified several variables contributing to AE reporting. Depression emerged as the strongest predictor of worst AEP total scores and worst scores for 3 of 6 factors (cognition/coordination, sleep, and tegument/mucosa), its effect being on average 2-fold greater than that of other predictors. This effect remained after performing the analyses separately on MESS and NMS samples, confirming the strong contribution of depression to AE reporting, which is independent of measures used to assess it. Similar findings have been reported in previous studies using the AEP26,29 or other measures of AEs,30 and may be interpreted as result of a substantial overlapping between epilepsy-associated depression and AED-related toxicity. In fact, some symptoms of depression can be utterly indistinguishable from AEs of AEDs, particularly cognitive impairment and sleep disturbances. In the absence of overt depressed mood, these symptoms may be erroneously attributed to AED treatment, possibly in part accounting for the common failure to recognize and subsequently treat depression in epilepsy.31 This is exceptionally insidious in newly treated epilepsy, where attention typically focuses on AEs rather than the increased risk of depression.32 Therefore, mood assessment should always complement adverse AED effects screening.33

Although compelling evidence exists that pharmacokinetic and pharmacodynamic drug properties are influenced by gender, leading to a higher incidence of AEs in females than in males across a variety of drug classes, including AEDs,34 we found that female gender predicts AE reporting independently of drug exposure. This effect could not be explained by the well-known interaction between female gender and mood dysfunction, as more men in our study were depressed compared to women (9.5% vs 3.4%, p < 0.05); this is not an unprecedented finding.35 Future research should elucidate potential mechanisms, and whether this association is limited to women with seizures.

Younger age at seizure onset, symptomatic etiology, and a history of febrile seizures impacted negatively on AEP scores. The association between earlier seizure onset age and AE reporting is unclear; it could be related to a higher propensity in younger patients to report subtle adverse experiences. Prior studies described higher rates of AEs in symptomatic epilepsy compared to other epilepsy types, possibly because patients with symptomatic epilepsy usually require higher AED loads.27 However, our findings suggest that CNS insults may lead to neurologic sequelae increasing patients' self-perception of AEs. The effect of history of febrile seizures on worst cephalgia scores is intriguing, particularly in light of recent evidence that certain genotypes, e.g., mutations in the Na+, K+ -ATPase pump gene ATP1A2, may predispose to febrile seizures, epilepsy, and migraine.36

AE reporting increased with the increasing number of prior seizures, and for AEP total, cognition/coordination, and sleep scores, this effect was more pronounced in subjects taking AEDs than in untreated controls. These findings reinforce existing evidence on the deleterious consequences of seizure recurrence,37,38 and suggest that more severe brain dysfunction associated with the increasing number of seizures may predispose to greater AED-related toxicity.

adverse effect
antiepileptic drug
Adverse Event Profile
Diagnostic Interview Schedule
Diagnostic Interview Schedule for Children
Diagnostic and Statistical Manual of Mental Disorders, 4th edition
Hospital Anxiety and Depression Scale
Multicenter Study of Early Epilepsy and Single Seizures
Northern Manhattan Study
valproic acid.

Disclaimer: The findings and conclusions in the report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.


Statistical analysis was conducted by Dr. Perucca and Dr. Hesdorffer.


Dr. Perucca reports no disclosures. Dr. Jacoby has served on scientific advisory boards for Stitchting Epilepsia Instellingen Nederland (SEIN) and Janssen; has received funding for travel or speaker honoraria from Janssen and Eisai Inc.; serves on the editorial boards of Seizure and Epilepsy & Behaviour; receives publishing royalties from Quality of Life in Epilepsy: Beyond Seizure Counts in Assessment and Treatment (Harwood Academic Publishers, 2000); has served as a consultant for Janssen, GlaxoSmithKline, sanofi-aventis, and Eisai Inc.; and receives research support from Pfizer Inc, National Health Service UK (NHS), MRC, Epilepsy Research Foundation, and Epilepsy Action. Dr. Marson has served on a scientific advisory board for UCB; has received funding for travel or speaker honoraria from GlaxoSmithKline, sanofi-aventis, UCB, Eisai Inc., and Janssen; serves on the editorial boards of Epilepsia and Therapeutics Bulletin, and Coordinating Editor for the Cochrane Epilepsy Group; and receives research support from GlaxoSmithKline, UCB, Eisai Inc., NHS, MRC, Epilepsy Research Foundation, NIHR, and Epilepsy Action. Dr. Baker has served on a scientific advisory board for sanofi-aventis; serves on the editorial board of Epilepsy and Behaviour; has received speaker honoraria from Eisai Inc.; has given expert testimony on fetal anticonvulsant syndrome; and has received research support from sanofi-aventis, Pfizer Inc, UCB, Epilepsy Research UK, Medical Research Council, Epilepsy Action UK. Dr. Lane has received research support from the Leukaemia Research Fund and the NIHR. Ms. Benn has received research support from the NIH (NINDS and NICHD). Dr. Thurman reports no disclosures. Dr. Hauser has served on a scientific advisory board for Lundbeck, Inc. Ovation Pharmaceuticals, Inc.; has served as a consultant for Pfizer Inc and Intranasal; serves on the editorial boards of Acta Neurologica Scandinavia, Neuroepidemiology, and Epilepsy Research; and has received research support from the CDC and the NIH/NINDS, and the Hotchkiss Neurological Institute. Dr. Gilliam has served on a scientific advisory board for Cyberonics, Inc.; has received speaker honoraria from Ortho-McNeil-Janssen Pharmaceuticals, Inc.; and serves on the editorial board of Epileptic Disorders. Dr. Hesdorffer has served on a scientific advisory board for Pfizer Inc; has received funding for travel from UCB; serves as an Editor of Epilepsia, Editor of Epilepsy Research, and as a Contributing Editor of Epilepsy Currents; and has received research support from the CDC, the AUCD, the NIH (NINDS, NICHD, and the Maternal and Child Health Bureau).


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