Prevalence and predictors of seizure clusters: A prospective observational study of adult patients with epilepsy

YEBEH-05985; No of Pages 8 Epilepsy & Behavior xxx (xxxx) xxx–xxx

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Prevalence and predictors of seizure clusters: A prospective observational study of adult patients with epilepsy Kamil Detyniecki a,⁎, Jane O'Bryan a,⁎⁎, Tenzin Choezom a,b, Grzegorz Rak a,c, Chanthia Ma a,d, Shiliang Zhang a,e, Jennifer Bonito a, Lawrence J. Hirsch a a

Yale Comprehensive Epilepsy Center, Department of Neurology, Yale School of Medicine, New Haven, CT, United States of America Yale Cancer Center, New Haven, CT, United States of America Second Faculty of Medicine, Medical University of Warsaw, Warsaw, Poland d Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, United States of America e David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA, United States of America b c

a r t i c l e

i n f o

Article history: Received 26 August 2018 Accepted 24 September 2018 Available online xxxx Keywords: Seizure clusters Epilepsy Seizures Acute repetitive seizures Rescue medication

a b s t r a c t Objective: The purpose of this prospective observational study was to describe the prevalence and adverse outcomes associated with seizure clusters (defined as ≥2 seizures in a 6-hour period) in a large sample of adult patients with a range of epilepsy severities and to identify clinical characteristics predictive of clustering. Methods: Patients maintained a seizure diary and were contacted monthly to verify compliance and data accuracy. Logistic regression models were utilized to test associations between individual patient demographic/clinical characteristics and seizure clustering. Fisher's exact test was utilized to test associations between rescue medication use and adverse seizure-related outcomes. Results: A total of 300 patients were followed prospectively for one year; 247 patients qualified for final analysis. Six-hour seizure clusters occurred in 45.8% of patients with active epilepsy at enrollment, including 62.7% of those with prior day-clusters and 30.0% of those without prior day-clusters. The odds of clustering were markedly greater among patients who reported a higher seizure frequency (N4 seizures per year vs. 1–4 seizures per year) (adjusted odds ratio (OR): 8.9; 95% confidence interval (CI): 3.2–24.6; p b 0.0001) and among patients with prior day-clusters (adjusted OR: 11.0; 95% CI: 1.2–104.2; p = 0.036). Rescue medication use was associated with significantly fewer injuries and emergency department visits, but rescue medication was underutilized. Conclusions: Seizure clusters are common, occurring in nearly half of adult patients with active epilepsy followed prospectively over one year, and are more frequent in those with higher seizure frequencies and prior day-clusters. Although underutilized, rescue medication was associated with fewer injuries and emergency department visit. © 2018 Elsevier Inc. All rights reserved.

1. Introduction Seizure clustering is a clinical phenomenon that is underrepresented in the literature relative to its prevalence among adult patients with epilepsy. A standardized definition of “seizure cluster” (SC) is lacking in the extant literature [1–3]. Clinical definitions of clustering are generally based on frequency of seizures within a defined period of time, whereas statistical definitions seek to evaluate whether the temporal distribution of seizures is random or characterized by a significant change in frequency or periodicity from a patient's typical seizure pattern [4,5].

⁎ Correspondence to: K. Detyniecki, Yale Comprehensive Epilepsy Center, 15 York Street, LLCI 7, New Haven, CT 06511, United States of America. ⁎⁎ Corresponding author. E-mail address: [email protected] (K. Detyniecki).

Studies investigating clinically defined SC have utilized several different definitions varying with regard to seizure frequency, time period of assessment, and seizure type. The most common clinical definitions utilized to date include N2 seizures in ≤4 h [6–8], 2–4 seizures in ≤48 h [9,10], ≥2 seizures in ≤24 h [5,11], and ≥3 seizures in ≤24 h [6,7,11– 19]. Studies investigating statistically defined SC have quantified increases in seizure frequency over a patient's baseline (i.e., 3- to 4-fold increase in frequency within a 3-day period) or applied probability tests (i.e., Poisson distribution) to evaluate the randomness of a seizure pattern and identify deviations (clusters) [4,20–25]. The reported prevalence of SC among adult patients with epilepsy varies widely among studies and patient populations. This can be partially attributed to the absence of a standardized definition of clusters. Prevalence of clustering reported in outpatient settings has largely been based on prospective seizure diary studies and spans an extremely wide range (3–76%) [14,15,18,19,21–23,25]. Inpatient studies have

https://doi.org/10.1016/j.yebeh.2018.09.035 1525-5050/© 2018 Elsevier Inc. All rights reserved.

Please cite this article as: Detyniecki K, et al, Prevalence and predictors of seizure clusters: A prospective observational study of adult patients with epilepsy, Epilepsy Behav (2018), https://doi.org/10.1016/j.yebeh.2018.09.035

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primarily surveyed patients undergoing video-electroencephalographic (EEG) monitoring, and the reported prevalence range is similarly varied (18–61%) [6–8,11–13,26,27]. Retrospective chart reviews have yielded lower prevalence estimates within a slightly narrower range (15–37%) [5,10,16,17]. There is an overall bias toward patients with intractable epilepsy (and generally high seizure burden) in the existing body of literature investigating SC [15]. As such, clustering prevalence in the patients studied to date may not be generalizable to the population of adults with epilepsy as a whole. Seizure clustering has the potential to seriously impact the health and well-being of adult patients with epilepsy. Previous studies on clinical and quality of life trajectories and correlates of disability in epilepsy have documented diminished quality of life, poorer long-term seizure and mortality outcomes, increased risk of postictal psychosis, status epilepticus (SE), emergency room visits and hospitalizations, and negative socioeconomic implications due to SC [4,16,26,28–34]. Patients who experience SC have been identified as ideal candidates for abortive treatment strategies with the goal of reducing the risk of subsequent seizures [14,16]. Several studies have documented efficacy of rescue medications for the treatment of SC and have also used variable cluster definitions. Dreifuss et al. compared rectal diazepam gel with placebo for home-based treatment of acute repetitive seizures (ARS), defined as “an episode of multiple complex partial or generalized seizures occurring within a 24-hour period in adults…with a pattern distinguishable from the patient's usual seizure pattern, and with onset readily recognizable by a care giver” [24]. Diazepam treatment was superior to placebo in terms of seizure frequency reduction and improved treatment outcomes as assessed by caregivers. Time to seizure recurrence was also longer for patients who received diazepam than for patients who received placebo. Abou-Khalil et al. assessed the efficacy and safety of an auto-injector (AI) for intramuscular administration of diazepam and defined ARS as “a bout of multiple complex partial or generalized seizures occurring over a brief period” (up to 12 h) [35]. The number of seizures experienced during the 12-hour postdose period was significantly lower for patients who received diazepam AI than for patients who received placebo, and time to next seizure or rescue was also significantly longer among patients receiving AI diazepam than among patients who received placebo. The ARTEMIS1 phase three clinical trial evaluated the safety and efficacy of intranasal midazolam in outpatient treatment of clusters defined as ≥ 2 seizures in a 6-hour period [36]. Midazolam nasal spray was found to be efficacious with a higher proportion of seizure terminations and lower seizure recurrence compared with placebo [37]. The results of these studies support the assertion that patients who experience SC may be ideal candidates for rescue medication use. Improved understanding of the risk factors, prevalence, and adverse outcomes of SC can inform appropriate treatment strategies and positively impact patient health. The purpose of this prospective observational study was to describe the prevalence and adverse outcomes associated with SC in a large sample of adult patients with a range of epilepsy severities and to identify clinical characteristics predictive of clustering. 2. Methods 2.1. Standard protocol approvals, registrations, and patient consents Study procedures were approved by the Yale University Human Investigation Committee. All subjects or legally authorized representatives gave informed consent for study participation. 2.2. Study design This was a prospective observational study. Electronic medical records were reviewed, and a baseline epilepsy history questionnaire was administered to identify epilepsy diagnosis, localization, risk

factors, and history of seizures and clusters in the year prior to enrollment. Patients were provided with either a paper or electronic seizure diary (Epilepsy Foundation — My Seizure Diary) and instructed to record seizures (date, time, count, duration, type, triggers); antiepileptic, concomitant, and rescue medication use; and adverse outcomes (injuries and emergency department (ED) visits). Coded usernames were utilized for electronic diaries to maintain patient confidentiality. Patients were followed-up on a monthly basis via telephone to verify seizure diary compliance. Paper diaries were collected and reviewed at regular intervals, and electronic diaries were reviewed on a monthly basis prior to follow-up calls. During follow-up, seizure history, medication use, and adverse outcomes were reviewed and compared with patient diaries to ensure data accuracy. 2.3. Subject population Patients were recruited from the Yale Comprehensive Epilepsy Center outpatient clinics. All patients presenting to the center were eligible to participate provided that they were ≥12 years of age, English speaking, and had the ability to maintain a seizure diary. Patients with a history of psychogenic seizures in the two years prior to enrollment were excluded. A total of 300 patients met inclusion criteria and were enrolled in the study between September 2013 and 2014. 2.4. Variable definitions 2.4.1. Patient demographics and clinical characteristics We examined patient demographic and clinical characteristics including age, age at epilepsy onset, duration of epilepsy, maximum seizure-free interval, sex, race, ethnicity, epilepsy diagnosis, epilepsy localization, history of epilepsy surgery, lifetime number of antiepileptic drugs (AEDs) tried, and risk factors for epilepsy (Table 1). Risk factors included dysgenesis, central nervous system (CNS) autoimmunity, CNS infection, cognitive delay, vascular malformation, stroke, family history of epilepsy (defined as two or more first degree relatives with epilepsy), brain tumor, hypoxic–ischemic encephalopathy (HIE), traumatic brain injury (TBI), birth injury, cerebral palsy, febrile seizures, and mesial temporal sclerosis (MTS). 2.4.2. Risk group at enrollment Patients were divided into predefined risk groups based on two criteria: epilepsy severity (being seizure-free in the past year or not) and presence/absence of “day-clusters” (days with two or more seizures in the past year). Patients classified as low risk had an epilepsy diagnosis but were seizure-free in the year preceding enrollment. Patients in the intermediate risk category had experienced at least one seizure in the year preceding enrollment but no day-clusters. High risk patients had experienced at least one day-cluster in the year preceding enrollment. 2.4.3. Seizure clustering For the prospective portion of the study, an SC was defined as ≥2 seizures in a 6-hour time period (“6-hour cluster”). This definition was chosen as one which might suggest clinical utility of nonoral rescue medications especially fast acting benzodiazepines administered through nasal, buccal, or intramuscular routes to reduce subsequent seizure risk. This definition is also consistent with the cluster definition employed in a recent phase three clinical trial evaluating the safety and efficacy of intranasal midazolam in outpatient treatment of subjects with SC [36]. When accurate times of seizures were not recorded, calendar dates were utilized to identify likely clusters. If a patient had ≥ 4 seizures in a 24-hour period, it was assumed that at least one 6-hour cluster occurred because of the high probability of at least two seizures occurring within a 6-hour period that day. We considered, at minimum, an undisrupted 6-hour period free from any seizures to signify the end of a cluster.

Please cite this article as: Detyniecki K, et al, Prevalence and predictors of seizure clusters: A prospective observational study of adult patients with epilepsy, Epilepsy Behav (2018), https://doi.org/10.1016/j.yebeh.2018.09.035

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which included assessment of available EEG and magnetic resonance imaging (MRI) results as well as descriptions of the seizure semiology.

Table 1 Demographic and clinical characteristics by seizure and cluster status.a,b Characteristic

Age (years) Age at epilepsy onset (years) Duration of epilepsy (years) Maximum seizure-free interval (years) Sex Female Male Race White Black or African American Asian Other Ethnicity Hispanic or Latino/a Non-Hispanic or Latino/a Epilepsy diagnosis Focal Generalized Localizatione Temporal Extratemporal History of epilepsy surgery Yes No Lifetime number of AEDsf 1–2 3–5 N6 Risk factors for epilepsyg Yes No

Seizure and cluster status during 1-year prospective follow-up (N = 247)

3

pg

2.5. Statistical analyses

Seizure-free Active (n = 110) epilepsy-no clusterc (n = 65)

Active epilepsy-clusterd (n = 72)

38.6 (30.9) 17.0 (23.0)

36.5 (22.8) 16.0 (18.0)

35.1 (25.7) 14.0 (19.5)

0.515 0.437

16.0 (21.3)

18.0 (23.0)

18.0 (21.0)

0.754

3.0 (8.0)

1.0 (1.8)

0.3 (0.9)

b0.001

57 (51.8) 53 (48.2)

28 (43.1) 37 (56.9)

45 (62.5) 27 (37.5)

0.074

92 (83.6) 14 (12.7)

43 (66.2) 13 (20.0)

55 (76.4) 6 (8.3)

0.022

0 (0.0) 3 (2.7)

1 (1.5) 5 (7.7)

2 (2.8) 8 (11.1)

7 (6.7) 98 (93.3)

8 (12.5) 56 (87.5)

8 (11.3) 63 (88.7)

0.379

80 (78.4) 22 (21.6)

56 (87.5) 8 (12.5)

68 (94.4) 4 (5.6)

0.009

25 (64.1) 14 (35.9)

19 (54.3) 16 (45.7)

27 (49.1) 28 (50.9)

0.358

11 (10.0) 99 (90.0)

9 (13.9) 56 (86.2)

10 (13.9) 62 (86.1)

0.639

70 (63.6) 25 (22.7) 15 (13.6)

25 (38.5) 28 (43.1) 12 (18.5)

20 (27.8) 19 (26.4) 33 (45.8)

b0.001

52 (47.3) 58 (52.7)

38 (58.5) 27 (41.5)

47 (65.3) 25 (34.7)

0.049

Differences in patient demographic and clinical characteristics by seizure/cluster status during one year of prospective follow-up (seizure-free, active epilepsy-no clusters, active-epilepsy-clusters) were tested for significance using Wilcoxon rank-sum tests for continuous variables and Fisher's exact test for categorical variables. Logistic regression models were utilized to test the associations between individual patient demographic/clinical characteristics and SC. Variables that met the threshold for significance in the univariate analyses (p b 0.05) were entered into a multivariate logistic regression model. A Bonferroni correction was applied to the multivariate model to account for multiple comparisons (p = 0.05/number of variables); therefore, the significance threshold was adjusted to p b 0.0125 in the multivariate model. We also evaluated adverse outcomes by rescue medication use or the lack thereof. The analytic sample was limited to the subset of patients who had at least once utilized a rescue medication over the course of the study (n = 26). Fisher's exact test was utilized to test associations between rescue medication use and adverse seizure-related outcomes including injuries and ED visits. A program was developed in Pascal to detect clusters based on available date and time data exported from seizure diaries and monthly follow-up forms. The results were organized and consolidated in Microsoft Excel. All statistical analyses were performed in SAS version 9.4. 3. Results 3.1. Patient population

Significance was set as a p value b 0.05. a Table values are median (IQR) for continuous variables and n (column %) for categorical variables; p-values are for Wilcoxon rank-sum test for continuous variables and for Fisher exact test for categorical variables. b Numbers may not sum to total because of missing data, and percentages may not sum to 100% because of rounding. c Active epilepsy-no cluster: isolated seizures, but no 6-hour clusters reported over one year of follow-up. d Active epilepsy-cluster: at least one 6-hour cluster (≥2 seizures in a 6-hour period) reported over one year of follow-up. e Includes only patients with known localization; patients with focal epilepsy but nonlateralized or unknown localization are not included. f Lifetime number of antiepileptic drugs tried. g Risk factors: dysgenesis, CNS autoimmunity, cognitive delay, vascular malformation, stroke, family history of epilepsy (2 or more 1st degree relatives), brain tumor, hypoxic– ischemic encephalopathy, traumatic brain injury, birth injury, cerebral palsy, central nervous system infection, febrile seizures, and mesial temporal sclerosis.

2.4.4. Seizure type Seizure types were classified in accordance with the International League Against Epilepsy (ILAE) criteria [38,39] based on patient-reported descriptions and electronic medical records.

2.4.5. Epilepsy diagnosis and localization Epilepsy diagnosis and localization were classified in accordance with ILAE criteria [38,39] based on electronic medical record review,

A total of 300 patients were initially enrolled in the study; 53 patients were excluded from the analytic sample; 10 patients withdrew consent during the study; 16 patients were lost to follow-up (defined as ≥3 failed attempts to contact); and 27 patients had insufficient seizure data because of diary noncompliance. 3.2. Patient demographic and clinical characteristics by seizure/cluster status The final analytic sample included 247 patients, all aged ≥14 years (97.6% ≥ 18 years of age) with a median age of 37.0 (±16.9) years. Of this sample, 78.5% were identified as White, 13.6% as Black or African American, 1.2% as Asian, and 6.6% as other. Only 9.6% of individuals were identified as Hispanic or Latino/a, and the sample was primarily female (52.6%). Patients were categorized into one of three groups based on seizure and cluster status during the year of prospective follow-up: 1) seizurefree: patients who reported no seizures (n = 110), 2) active epilepsyno clusters: patients who experienced isolated seizures but no 6-hour clusters (n = 65), and 3) active epilepsy-clusters: patients who experienced at least one 6-hour cluster (at least two seizures within a 6-hour period on at least one occasion; n = 72). There were no statistically significant differences between groups with regard to median age, age at epilepsy onset or duration of epilepsy, sex, ethnicity, or history of epilepsy surgery (Table 1). There were differences between groups with regard to maximum seizure-free interval (p b 0.001), race (p = 0.022), epilepsy diagnosis (p = 0.009), lifetime number of AEDs (p b 0.001), and risk factors for epilepsy (p = 0.049) (Table 1). The median duration of maximum seizure-free interval was shortest among patients in the cluster group (0.3 years; Interquartile range (IQR): 0.9). Seizures in cluster patients were also more refractory to seizure medication overall compared with those in the other two groups. Nearly half of cluster patients had used six or more lifetime AEDs

Please cite this article as: Detyniecki K, et al, Prevalence and predictors of seizure clusters: A prospective observational study of adult patients with epilepsy, Epilepsy Behav (2018), https://doi.org/10.1016/j.yebeh.2018.09.035

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Table 2 Risk group at enrollment and seizure/cluster status at 1 year.a Risk groupb

Low riskc Intermediate riskd High riske

N (% experiencing clusters)

92 (1.1) 80 (30.0) 75 (62.7)

Seizure and cluster status during 1-year prospective follow-up (N = 247)

p

Seizure-free n = 110

Active epilepsy-no clusterf n = 65

Active epilepsy-clusterg n = 72

77 (70.0) 25 (22.7) 8 (7.3)

14 (21.5) 31 (47.7) 20 (30.8)

1 (1.4) 24 (33.3) 47 (65.3)

b0.001

Note: Among patients with active epilepsy at enrollment (intermediate risk and high risk combined), 45.8% experienced clusters during the year of follow-up; among patients with active epilepsy during the study period (no cluster and cluster groups combined excluding seizure-free patients), 52.6% experienced clusters during follow-up. a Table values are n (column %) except for N (% experiencing clusters), which is n (row %); p-value is for Fisher exact test for global null hypothesis; pairwise comparisons with Bonferroni correction for multiple comparisons were all significant at the p b 0.001 level (low risk vs. intermediate risk: p b 0.001; low risk vs. high risk: p b 0.001; intermediate risk vs. high risk: p b 0.001). b Risk group based on seizure and day-cluster history in the year prior to study enrollment. c Low risk: seizure-free in the year prior to enrollment. d Intermediate risk: at least one seizure in the year prior to enrollment but no days with ≥2 seizures (no day-clusters). e High risk: at least one day with ≥2 seizures (day-cluster) in the year prior to enrollment. f Active epilepsy-no cluster: isolated seizures, but no 6-hour clusters reported over one year of follow-up. g Active epilepsy-cluster: at least one 6-hour cluster (≥2 seizures in a 6-hour period) reported over one year of follow-up.

(45.8%). Epilepsy risk factors were also more prevalent among patients with clusters (65.3%). A greater proportion of patients were identified as non-White in the active epilepsy-no cluster (29.2%) and active epilepsycluster groups (22.2%) as that in the seizure-free group (15.4%) (Table 1). Among patients for whom semiological seizure classification was available, the following seizure types were represented: simple partial motor/focal aware motor (6.7%), simple partial sensory/focal aware nonmotor (20.0%), complex partial/focal impaired awareness (40.0%), generalized tonic–clonic (including generalized and focal to bilateral tonic–clonic, 27.8%), absence (1.7%), myoclonic (1.7%), and tonic (generalized tonic, 2.2%). 3.3. Risk group at enrollment and clustering There was a statistically significant association between risk group at enrollment (based on seizure and day-cluster history in the year prior to enrollment) and seizure/cluster status during the one year of prospective follow-up (p b 0.001) (Table 2). Pairwise comparisons by risk group (low risk vs. intermediate risk; low risk vs. high risk; intermediate risk vs. high risk) were all statistically significant (p b 0.001). Only 1.1% of low risk patients experienced clusters while 30.0% of all intermediate risk patients and nearly two-thirds (62.7%) of high risk patients did so. 3.4. Prevalence of seizure clustering The overall prevalence of SC in the sample was 29.1% [72/247]. Among patients with active epilepsy at enrollment (intermediate and high risk groups combined), 45.8% reported at least one 6-hour cluster during the year of prospective follow-up. Among patients with active epilepsy during the study (nonseizure-free patients), 52.6% reported at least one 6-hour cluster during the year of prospective follow-up. Overall seizure burden was significantly higher among patients in the active epilepsy-cluster group than among patients in the active epilepsy-no cluster group (p b 0.001) (Table 3). Patients in the active

epilepsy-cluster group recorded a total of 5974 seizures (3270 isolated seizures and 2254 intracluster seizures), accounting for 92.4% of all seizures recorded during the one-year prospective follow-up period. A total of 911 clusters were recorded. 3.5. Risk factors for seizure clustering 3.5.1. Logistic regression model Univariate logistic regression models tested the associations between the following study variables and SC: demographic characteristics including age, sex, race, and ethnicity and clinical characteristics including age at epilepsy onset, epilepsy diagnosis, epilepsy localization, SE (during the follow-up period), lifetime number of AEDs tried, risk factors for epilepsy, risk group at enrollment, and seizure frequency (during the follow-up period). The univariate regression models identified four variables that were significantly associated with SC: female sex (61.6% had clusters vs. 42.2% of males; unadjusted odds ratio (OR): 2.20; 95% confidence interval (CI): 1.11–4.37; p = 0.024); ≥6 lifetime AEDs (73.3% vs. 40.3% if 3–5 AEDs or 44.4% if 1–2 AEDs; unadjusted OR: 3.44; 95% CI: 1.42–6.59; p = 0.006); risk group at enrollment: intermediate risk group (43.6% vs. 6.7% in low risk group; unadjusted OR: 10.84; 95% CI: 1.33–88.25; p = 0.26) and high risk group (70.2% vs. 6.7% in low risk group; unadjusted OR: 32.89; 95% CI: 4.05–267.24; p = 0.001); and seizure frequency during the year of follow-up. An annual frequency of 1–4 seizures was used as the reference category; the comparative odds of clustering among patients with an annual seizure frequency of N4 seizures were significantly greater (71.4% vs. 15.2% if 1–4 seizures per year; unadjusted OR: 2.12; 95% CI: 1.07–4.19; p b 0.031) (Table 4). Univariate logistic regressions were performed for all individual epilepsy risk factors (dysgenesis, CNS autoimmunity, CNS infection, cognitive delay, vascular malformation, stroke, family history of epilepsy, brain tumor, HIE, TBI, birth injury, cerebral palsy, febrile seizures, and MTS) but did not yield any statistically significant associations. The four variables that were significant in the unadjusted univariate analysis were entered into the multivariate analysis (Table 4).

Table 3 Prevalence of isolated seizures and intracluster seizures among patients with active epilepsy. Variable

Events

Active epilepsy-no cluster (n = 65) Total seizures Isolated seizures Intracluster seizures a b c

6463 4209 2254

pc

Cluster status during 1-year prospective follow-up (N = 137) a

489 (7.6) 489 (11.6) 0

Active epilepsy-cluster (n = 72) 5974 (92.4) 3720 (88.4) 2254 (100.0)

b

b0.001 b0.001 –

Active epilepsy-no cluster: isolated seizures, but no 6-hour clusters reported over one year of follow-up. Active epilepsy-cluster: at least one 6-hour cluster (≥2 seizures in a 6-hour period) reported over one year of follow-up. p-Value is for Wilcoxon 2-sample test; table values are seizure counts (row %).

Please cite this article as: Detyniecki K, et al, Prevalence and predictors of seizure clusters: A prospective observational study of adult patients with epilepsy, Epilepsy Behav (2018), https://doi.org/10.1016/j.yebeh.2018.09.035

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Table 4 Univariate and multivariate associations between study variables and seizure clusters.a Characteristic

Age (years) b30 30–49 50+ Sex Male Female Race White Black or African American Asian Other Ethnicity Non-Hispanic or Latino/a Hispanic or Latino/a Age at epilepsy onset Childhood (b18 years) Adulthood (18+ years) Epilepsy diagnosis Generalized Focal Localization Temporal Extratemporal Status epilepticusb No Yes Lifetime number of AEDs 1–2 3–5 N6 Risk factors for epilepsy No Yes Risk group Low Intermediate High Seizure frequencyc 1–4 N4

Na

% experiencing clusters

Unadjusted model

Adjusted model

OR (95% CI)

p

OR (95% CI)

p

1.00 1.96 (0.83–4.63)

– 0.128

1.00 0.77 (0.27–2.22) 1.43 (0.48–4.28)

– 0.623 0.523

53 48 36

54.7 50.0 52.8

1.00 0.83 (0.38–1.81) 0.93 (0.40–2.16)

– 0.636 0.857

64 73

42.2 61.6

1.00 2.20 (1.11–4.37)

– 0.024

98 19 3 13

56.1 31.6 66.7 61.5

1.00 0.38 (0.13–1.08) 1.64 (0.14–18.7) 1.31 (0.40–4.29)

– 0.068 0.689 0.651

119 16

52.9 50.0

1.00 0.89 (0.31–2.52)

– 0.825

79 58

55.7 48.3

1.00 0.74 (0.38–1.47)

– 0.391

12 124

33.3 54.8

1.00 2.73 (0.80–9.34)

– 0.109

46 44

58.7 63.6

1.00 1.95 (0.93–4.07)

– 0.076

116 21

49.1 71.4

1.00 2.59 (0.94–7.14)

– 0.066

45 47 45

44.4 40.3 73.3

1.00 0.85 (0.37–1.94) 3.44 (1.42–8.32)

– 0.697 0.006

52 85

48.1 55.3

1.00 1.34 (0.67–2.67)

– 0.412

15 55 67

6.7 43.6 70.2

1.00 10.84 (1.33–88.25) 32.89 (4.05–267.24)

– 0.026 0.001

1.00 5.07 (0.54–47.84) 11.02 (1.17–104.16)

– 0.156 0.036

46 91

15.2 71.4

1.00 13.92 (5.53–35.1)

– b0.0001

1.00 8.88 (3.20–24.64)

– b0.0001

a The significance level for univariate analysis was set to p b 0.05 for all analyses. Bonferroni correction was applied to the multivariate model to account for multiple comparisons (p = 0.05/number of variables); therefore, the significance threshold was adjusted to p b 0.0125 in the multivariate model. b Status epilepticus (defined as a seizure lasting N5 min) reported during the year of prospective follow-up. c Seizure frequency during the year of prospective follow-up.

Controlling for all other variables, the only factor that remained significant in the multivariate model was seizure frequency. The odds of clustering were markedly higher among patients who reported a higher seizure frequency (N 4 seizures per year) (adjusted OR: 8.88; 95% CI: 3.20–24.64; p b 0.0001). High risk patients who reported at least one day-cluster in the year preceding enrollment had significantly greater odds of clustering compared with low risk patients (adjusted OR: 11.02; 95% CI: 1.17–104.16; p = 0.036); however, this association did not meet the corrected threshold for significance (p b 0.0125). 3.6. Injuries and emergency department visits A total of 113 seizure-related injuries were recorded by 39 (16%) patients over the year of follow-up (Table 5). A greater proportion (61.5%) of the patients who experienced injuries were in the active epilepsycluster group; however, the vast majority of injuries occurred in association with isolated rather than intracluster seizures (87.6% vs. 12.4%). Only 2.4% of isolated seizures and 1.5% of clusters resulted in injuries, and the association between seizure type (isolated seizure vs. cluster) and injuries was not significant (p = 0.137). Fifty-one seizure-related ED visits were recorded by 41 patients over the year of follow-up (Table 5). Slightly over half (53.7%) of patients

reporting ED visits were in the active epilepsy-cluster group. As with injuries, the vast majority of ED visits were associated with isolated seizures rather than clusters (76.5% vs. 23.5%). Only 0.9% of isolated seizures and 1.3% of clusters resulted in ED visits, and the association between seizure type (isolated seizure vs. cluster) and ED visits was not significant (p = 0.272). 3.7. Rescue medication use Only 9.3% of patients reported having a prescription for a rescue medication at enrollment; 11 additional patients received a prescription during the year of follow-up. Less than a third (27.6%) of patients in the active epilepsy-cluster group and only 14.8% of patients in the active epilepsy-no cluster group reported having a rescue medication prescription either at enrollment or during follow-up. Over the one-year period of prospective follow-up, 26 (11%) patients utilized a rescue medication a total of 96 times (Table 5). The rescue medication utilized most frequently was oral lorazepam (74.1%) followed by intranasal midazolam (14.1%) and rectal diazepam (2.2%). Nearly three-quarters (73.1%) of patients who utilized a rescue medication during the study were in the active epilepsy-cluster group, and rescue medication was used more frequently for isolated seizures as

Please cite this article as: Detyniecki K, et al, Prevalence and predictors of seizure clusters: A prospective observational study of adult patients with epilepsy, Epilepsy Behav (2018), https://doi.org/10.1016/j.yebeh.2018.09.035

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Table 5 Prevalence of injuries, ED visits, and rescue medication use by isolated seizure vs. cluster. Patients

Injuries ED visits Rescue medication use a b c

39 41 26

Cluster status during 1-year prospective follow-up (N = 137) Active epilepsy-no clustera (n = 65)

Active epilepsy-clusterb (n = 72)

15 (23.1) 19 (29.2) 7 (10.8)

24 (33.3) 22 (30.6) 19 (26.4)

pc

0.255 1.000 0.028

Events

113 51 96

Seizure type

p

Isolated (n = 4209)

Clusterc (n = 911)

99 (2.4) 39 (0.9) 68 (1.6)

14 (1.5) 12 (1.3) 28 (3.1)

0.137 0.272 0.006

Active epilepsy-no cluster: isolated seizures, but no 6-hour clusters reported over one year of follow-up. Active epilepsy-cluster: at least one 6-hour cluster (≥2 seizures in a 6-hour period) reported over one year of follow-up. Injuries, ED visits, and rescue medication use were counted once per cluster.

compared with clusters (70.8% vs. 29.2%, p = 0.006) (Table 5). Rescue medication was utilized in 1.6% of all isolated seizure events and 3.1% of all 6-hour clusters. An outcomes analysis was conducted to evaluate adverse outcomes by rescue medication use or the lack thereof (Table 6). The analytic sample was limited to the subset of patients who had at least once utilized a rescue medication over the course of the study (n = 26). The sample was restricted in order to compare frequency of adverse outcomes when rescue medication was and was not utilized. Rescue medications were utilized in 61.9% of all recorded isolated and intracluster seizures in this subset of patients. Rescue medication administration was counted only once per cluster; only two clusters were treated with rescue medication more than one time. No adverse outcomes were reported for the vast majority of seizure events when rescue medication was administered. A total of only two injuries and five ED visits occurred in the 26 cases when rescue medication was used. When rescue medication was used, injuries occurred in only 2.1% [2/96] of events, and ED visits occurred in only 5.2% [5/96] compared with injuries in 91.5% [54/59] of events and ED visits in 16.9% [10/59] when rescue medication was not used (p b 0.0001 and p = 0.0239, respectively; Table 6). The majority of adverse outcomes occurred in association with seizure events for which rescue medication was not used (54 injuries (96.4% of reported injuries) and 10 ED visits (66.7% of reported ED visits) (Table 6)). 4. Discussion The main findings of our study are as follows: SCs, defined as at least 2 seizures in a 6-hour period, occurred in 45.8% of patients with active epilepsy at enrollment (at least one seizure in the prior year) including 62.7% of those with prior day-clusters (at least two seizures in one day in the prior year) and 30.0% of those without prior day-clusters. The main risk factors were higher seizure frequency and prior clusters. Rescue medication use was associated with significantly fewer injuries and ED visits, but rescue medication was underutilized. Seizure clustering was common in this sample of adult patients with epilepsy. Our calculated prevalence falls within the wide range

Table 6 Intrapatient analysis of outcomes by rescue medication use (n = 26).a Outcome

Injury Yes No ED visit Yes No

Rescue medicationb

p

Used (n = 96 seizures)

Not used (n = 59 seizures)

2 (3.6) 94 (94.9)

54 (96.4) 5 (5.1)

5 (33.3) 91 (65.0)

10 (66.6) 49 (35.0)

b0.0001

0.0239

a The sample was limited to the subset of patients who ever used a rescue medication during the year of prospective follow-up (n = 26). b Table values are patient reported seizure counts (row %), demonstrating the frequency of associated adverse outcomes (injuries and ED visits) when rescue medication was and was not used.

previously reported, at the lower end of the spectrum when including the seizure-free patients in the analysis. This is perhaps due to selection bias in previous studies toward patients with intractable epilepsy and patient recruitment from inpatient monitoring settings involving seizure medication withdrawal. It is noteworthy that over half of the patients with active epilepsy experienced clustering during the year of prospective follow-up, including a third of patients with no history of day-clusters (no days with more than one seizure) in the year preceding enrollment. Patients in the active epilepsy-cluster group had a significantly higher seizure burden compared with patients in the active epilepsy-no cluster group (p b 0.001) (Table 3). These findings corroborate the results of past studies, which documented higher daily seizure frequency [5] and higher frequency of nonclustered (isolated) seizures [16] among cluster patients compared with noncluster patients. Previous studies have identified the following clinical characteristics as risk factors for SC: drug-resistant epilepsy [16,17], intractable epilepsy (defined as lack of seizure control/higher seizure frequency/absence of one-year seizure freedom) [15,17,19,21,22], duration of epilepsy [25,40], localization (extratemporal epilepsy, in particular frontal lobe epilepsy) [6,15,27,41], MTS [13], head trauma [15], remote symptomatic epilepsy [15], symptomatic generalized epilepsy [17], posttraumatic epilepsy [15], CNS infection [17], cortical dysplasia [17], SE [17], and history of SC at home [13]. Our analyses confirmed independent associations between several of the aforementioned risk factors and clustering, including drug-resistant epilepsy (N 6 lifetime AEDs), higher seizure frequency, and higher risk group at enrollment (history of seizures/clusters). We also found an independent association between female sex and clustering, possibly attributable to catamenial clustering, though we were not able to determine this. There were marginally significant associations between localization and clustering (58.7% temporal vs. 63.6% extratemporal; unadjusted OR: 1.95; 95% CI: 0.93–4.07; p = 0.076) and between SE and clustering (71.4% SE vs. 49.1% no SE; unadjusted OR: 2.59; 95% CI: 0.94–7.14; p = 0.066) (Table 4). Our multivariate analysis identified the following two risk factors for clustering at the p b 0.05 level: seizure frequency (N 4 seizures per year) and risk group at enrollment (history of day-clusters). Seizure frequency during the study was the only significant predictor of clustering at the corrected p b 0.0125 level. Sillanpää and Schmidt and Fisher et al. previously reported similar results concluding that cluster patients experience significantly higher daily seizure frequency and frequency of isolated seizures compared with noncluster patients [5,16]. Although it was not significant in the multivariate model, our finding that history of clusters may be predictive of clustering corroborates the results of an earlier study. Haut et al. identified history of SC at home (p = 0.0003) and MTS (p = 0.0172) as the sole risk factors for clustering among patients with intractable epilepsy undergoing presurgical evaluation [13]. Having MTS was not a significant risk factor in our study even in univariate analysis. Patients with epilepsy are at greater risk of injury and hospitalization compared with the general population [42,43], and injuries resulting from seizures account for these discrepancies [44]. Two of the established risk factors for clustering are also risk factors for injury:

Please cite this article as: Detyniecki K, et al, Prevalence and predictors of seizure clusters: A prospective observational study of adult patients with epilepsy, Epilepsy Behav (2018), https://doi.org/10.1016/j.yebeh.2018.09.035

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greater number of AEDs used and higher seizure frequency [42,45]. Effective seizure control, including control through abortive treatment, can reduce the risk of seizure-related injuries and hospitalizations [45, 46]. While no causal relationship can be inferred from the associations we documented between rescue medication use and avoidance of adverse seizure-related outcomes including injury and ED visits, these findings raise important questions about the potential utility of rescue medications for the prevention of adverse outcomes and reduction of healthcare service utilization, especially when convenient forms of rapidly-acting rescue medications are approved for this use. While the sample size in these adverse outcome analyses was limited, our findings are concordant with the results of a larger retrospective chart review study investigating healthcare resource utilization associated with rescue medication use in adult patients with SC [46]. Vazquez et al. found that rescue medication “under-utilizers” (patients who lacked a prescription or failed to use a rescue medication for at least one documented cluster) were more likely to experience SE, visit the ED, and be hospitalized compared with “utilizers” (patients who consistently used rescue medication) [46]. The healthcare resources consumed by the subset of “under-utilizers” and associated costs were also significantly higher [46]. The results of this study exemplify the potential positive impact of effective treatment strategies for both patients with epilepsy and the healthcare system as a whole. Additional studies are needed to better characterize this highly prevalent and understudied clinical phenomenon. This need is particularly urgent given the advent of rescue medications with novel routes of administration that will soon become available for the abortive treatment of clusters.

4.1. Limitations This study is limited by its observational nature and reliance on selfreported seizure data. Patients may have been more or less likely to accurately record information in seizure diaries depending on seizure burden. Patients with well-controlled epilepsy may have had more complete data; therefore, population prevalence of clustering may have been underestimated in the sample. Past studies utilizing similar seizure diary methodologies, however, have reported that patients with intractable epilepsy and high seizure burden are actually more likely to record seizure activity [4,5]. If this was the case in our sample, the reported prevalence may actually be inflated. We reduced the risk of differential reporting by contacting patients monthly to verify diary compliance and improve data quality. In spite of these efforts to ensure data accuracy, our adverse outcome analysis of injuries and ED visits associated with seizure events among rescue medication users may have significantly overestimated the potential effect of rescue medications. Patients may have been more likely to report seizures and clusters that resulted in an injury or ED visit. As such, the total number of seizures reported by this subset of patients is likely an underestimate. It is also possible that seizure severity (or other known risk factors for injury) is confounding variables in this association. Including and controlling for other known risk factors for injury and ED visits may be a more appropriate analytic strategy that should be explored in future studies. The existing body of literature on SC is biased toward patients with refractory epilepsy. We aimed to reduce the potential for selection bias by purposefully recruiting three groups of patients with differing levels of seizure burden and risk of clustering, including a seizure-free group. Ultimately, our study may actually have been skewed toward patients with less refractory epilepsy given the distribution of patients by seizure/cluster status during the year of prospective follow-up (seizurefree (n = 110), active epilepsy-no clusters (n = 65), active epilepsyclusters (n = 72)). The low dropout rate (17.6%) over one year of prospective follow-up is a strength of the study. Sufficient sample sizes were maintained across all three baseline risk groups, and this ensured

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representation and comparability of the study groups, which were determined by seizure/cluster status. 5. Conclusions Seizure clusters are common among adult patients with epilepsy and occurred in nearly half (45.8%) of study patients with active epilepsy at enrollment. Rescue medication use is associated with fewer injuries and ED visits, but rescue medication is underutilized at present even at a tertiary care epilepsy center. Additional scientifically rigorous studies of diverse patient populations with the aim of characterizing SCs, identifying risk factors, and optimizing treatment strategies should be prioritized to fill crucial gaps in the epilepsy literature. Author contributions Kamil Detyniecki conceptualized and designed the study, obtained study funding, supervised the study, interpreted the data, and wrote and revised the manuscript. Jane O'Bryan analyzed and interpreted the data, performed the statistical analysis, created the figures, and wrote the manuscript. Tenzin Choezom contributed to the study design and implementation, created the study tools, recruited the patients, acquired the data, and prepared the data for analysis. Grzegorz Rak contributed to the data analysis and interpretation and developed a program and algorithm to detect seizure clusters. Chanthia Ma acquired the data and created programs to clean and prepare the data for analysis. Shiliang Zhang recruited the patients, acquired the data, and prepared the data for analysis. Jennifer Bonito recruited the patients and acquired the data. Lawrence J. Hirsch supervised the study, interpreted the data, and revised the manuscript. All authors critically reviewed and approved the manuscript. Disclosures J. O'Bryan, T. Choezom, G. Rak, C. Ma, S. Zhang, and J. Bonito report no disclosures relevant to the manuscript. K. Detyniecki has received research support to Yale University for investigator-initiated studies from Eisai, Sunovion, Acorda, and Upsher-Smith. L.J. Hirsch has received research support to Yale University for investigator-initiated studies from Eisai, Monteris, and Upsher-Smith, consultation fees for advising from Ceribell, Eisai, Monteris, Sun Pharma, and Engage Therapeutics, royalties for authoring chapters for UpToDate-Neurology, and from Wiley for coauthoring the book “Atlas of EEG in Critical Care” by Hirsch and Brenner, and honoraria for speaking from Neuropace. Funding This research was supported by Upsher-Smith Laboratories, Inc. through an investigator-initiated grant. The research sponsor had no influence over the study design, data collection, data analysis, data interpretation, or writing of the manuscript. References [1] Buelow JM, Shafer P, Shinnar R, Austin J, Dewar S, Long L, et al. Perspectives on seizure clusters: gaps in lexicon, awareness, and treatment. Epilepsy Behav 2016;57: 16–22. [2] Pellock J, Arzimanoglou A, Hesdorffer D, Leppik I, Shinnar S, Haut S. Seizure cluster — the need for consistent terminology. Presented at 68th Annual Meeting of the American Epilepsy Society (AES), December 5–9, 2014, Seattle, WA; 2014. [3] Komaragiri A, Detyniecki K, Hirsch LJ. Seizure clusters: a common, understudied and undertreated phenomenon in refractory epilepsy. Epilepsy Behav 2016;59:83–6. [4] Haut SR. Seizure clustering. Epilepsy Behav 2006;8:50–5. [5] Fisher RS, Bartfeld E, Cramer JA. Use of an online epilepsy diary to characterize repetitive seizures. Epilepsy Behav 2015;47:66–71. [6] Ferastraoaru V, Schulze-Bonhage A, Lipton RB, Dümpelmann M, Legatt AD, Blumberg J, et al. Termination of seizure clusters is related to the duration of focal seizures. Epilepsia 2016;57:889–95.

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Please cite this article as: Detyniecki K, et al, Prevalence and predictors of seizure clusters: A prospective observational study of adult patients with epilepsy, Epilepsy Behav (2018), https://doi.org/10.1016/j.yebeh.2018.09.035