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Seizure prediction and recall
Epilepsy and Behavior 18 (2010) 106–109
Contents lists available at ScienceDirect
Epilepsy and Behavior j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / ye b e h
Seizure prediction and recall J.M. DuBois a,⁎, L.S. Boylan a,b, M. Shiyko c, W.B. Barr a,d, O. Devinsky a,d,e,f a
Department of Neurology, NYU Langone School of Medicine, New York, NY, USA Department of Veterans Affairs New York Harbor Healthcare System, New York, NY, USA c The Methodology Center, Pennsylvania State University, State College, PA, USA d Department of Psychiatry, NYU Langone School of Medicine, New York, NY, USA e Institute of Neurology, Saint Barnabas Medical Center, West Orange, NJ, USA f Department of Neurosurgery, NYU Langone School of Medicine, New York, NY, USA b
a r t i c l e
i n f o
Article history: Received 26 January 2010 Received in revised form 19 March 2010 Accepted 22 March 2010 Available online 10 May 2010 Keywords: Epilepsy Prediction Awareness Seizures
a b s t r a c t Using separate generalized mixed-effects models, we assessed seizure recall and prediction, as well as contributing diagnostic variables, in 83 adult patients with epilepsy undergoing video/EEG monitoring. The model revealed that when participants predicted a seizure, probability equaled 0.320 (95% CI: 0.149–0.558), a significant (P b 0.05) increase over negative predictions (0.151, 95% CI: 0.71–0.228]). With no seizure, the rate of remembering was approximately 0.130 (95% CI: 0.73–0.219), increasing significantly to 0.628 (95% CI: 0.439 to 0.784) when a seizure occurred (P b 0.001). Of the variables analyzed, only inpatient seizure rate influenced predictability (P b 0.001) or recollection (P b 0.001). These models reveal that patients were highly aware of their seizures, and in many cases, were able to make accurate predictions, for which seizure rate may be an important factor. © 2010 Elsevier Inc. All rights reserved.
1. Introduction Unpredictability is one of the most disabling and frightening aspects of epilepsy. This problem could be mitigated by recognition of factors that provoke seizures, premonitory symptoms (PS) that predict seizures, and the ability to abort impending seizures. Patients and clinicians frequently identify seizure-provoking factors including missed medication, sleep deprivation and fatigue, emotional stress, excess alcohol, perimenstrual and ovulatory phases, illness and fever, exercise, and environmental stimuli [1–6]. Specific precipitants are well established (e.g., sleep deprivation, menstrual phases) whereas others (e.g., emotional stress) are supported by limited data [7,8]. Some patients report that they can predict when they are seizureprone because of the premonitory mental or physical changes that precede seizures. One-third of patients with partial epilepsy experience PS that may occur 30 minutes to 3 days before seizures [9]. PS are often vague (funny feeling) or nonspecific (headache, dysphoria, confusion, and irritability) [10,11] and precede more than half of seizures for individuals who experience them [11]. EEG changes hours before seizure onset are a potential physiological correlate of predictions [12,13]. Patients may seek low-risk-for-seizure situations, possibly reducing seizure occurrence and increasing perceived selfcontrol [14]. Many patients report self-control of seizures using
⁎ Corresponding author. NYU Epilepsy Center, 223 East 34th Street, New York NY 10016, USA. Fax: +1 646 385 7164. E-mail address: [email protected] (J.M. DuBois). 1525-5050/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.yebeh.2010.03.011
relaxation, distraction, mood changes, or additional medications [10,15–18]. Few studies have examined whether patients' perceived selfcontrol translates into accurate awareness and prediction of seizures; none examined this issue in a monitored clinical setting. In one diarybased outpatient sample, 32% successfully predicted their seizures, although highly accurate predictions were concentrated among 22% of the group [8]. Limited research has been done on seizure recall among video/EEG (VEEG)-monitored patients. In one small series, only 26% of patients with complex partial epilepsy were consistently able to recall their seizures [19]. Therefore, the current study aimed to determine the seizure recall and predictive ability of adult patients with epilepsy undergoing VEEG monitoring in a controlled setting. 2. Methods 2.1. Subjects Eighty-three individuals were recruited at the New York University Epilepsy Center for a prospective study on seizure prediction and awareness between January 2006 and March 2007. To be eligible, the individuals had to be ≥ 18 years old, mentally competent, and experiencing epileptic seizures. The distribution of seizure types prior to admission is summarized in Table 1. Patients in whom nonepileptic seizures were recorded were excluded from analysis. All participants provided written consent to the study, which was approved by the NYU Langone School of Medicine institutional review board.
J.M. DuBois et al. / Epilepsy and Behavior 18 (2010) 106–109 Table 1 Seizure types prior to clinical monitoring. Type of seizure
Number of patients
Percentage of group
Partial Complex partial Simple partial Secondarily generalized Multifocal Generalized Other Unknown
38 11 13 13 33 5 8
46% 13% 16% 16% 40% 6% 10%
days on which a seizure occurred during the present study (mean = 0.252, SD = 24), years since diagnosis (mean = 18.5 years, SD = 13.3), personal belief about the ability to anticipate a seizure (66%), memory of PS (29%), and personal belief about the ability to prevent a seizure (29%), on each of these matched outcomes. All analyses were carried out in the open-source statistical software R [21]. 3. Results
Note. Patients estimated their frequency of prior seizure types per month averaged over the past year. Shown here is the group representation of various seizure types based on patients' frequency estimates.
2.2. Procedure On hospital admission, participants were asked to complete a survey regarding their ability to abort and anticipate seizures, as well as common precipitants. Daily questionnaires were then administered asking whether or not patients could recall having a seizure in the past 24 hours (yes/no/do not know) and whether they predicted having one in the next 24 hours (yes/no/do not know). These were generally administered in the morning, although completely accurate timing was not possible because of complications inherent to the clinical setting. Information regarding subjects' seizure history, such as type and frequency of pre-admission seizures and duration of illness, was collected through chart review. VEEG reports provided the description of clinical events during subjects' admission. 2.3. Statistical analysis To study the extent to which patients could predict daily seizures, a generalized mixed-effects model [20] was fitted to the data, where daily seizure status (1 = seizure, 0 = no seizure) was regressed on daily seizure predictions (1 = predict a seizure, 0 = predict no seizure). To examine whether or not individuals could recollect their seizure occurrence accurately, a separate model was fitted to seizure recall (1 = recall a seizure, 0 = recall having no seizure) as a function of seizure status. The methodology accounted for the observation interdependence due to repeated assessments (ICC = 0.527), missing data (thus preserving information on individuals who did not provide responses for all daily assessments), and random variability in patient responses. In addition, four binary outcome indicators representing concordance between personal assessments and seizure occurrences were constructed to replicate previous methodology (Table 2). True positive (TP) predictions reflected occasions on which individuals accurately predicted their seizure, and TP awareness corresponded to seizure events that were accurately recalled. In a similar fashion, true negative (TN) predictions characterized predictions of not having a seizure that actually did not occur, and TN awareness indicated that a patient accurately recalled not having a seizure. Four generalized mixed-effects models were fitted to study the effects of the monthly outpatient seizure frequency (mean = 11.25, SD = 26.14, two patients were excluded for excessive estimations), Table 2 Prediction and memory outcomes.
Positive prediction Negative prediction Positive memory Negative memory
107
Seizure
No seizure
46 (TP) 56 (FN) 73 (TP) 30 (FN)
88 186 53 215
(FP) (TN) (FP) (TN)
Note. The contingency table displays the tabulated evaluation of recalled and predicted seizures.
Average age of study participants was 39 (SD = 13.8); women constituted 53% of the sample. Clinical assessment of seizure status was made on all but 4 hospital days, yielding 567 assessments (mean = 6.8 per individual, SD = 3.1), with seizures occurring on 143 days. A total of 376 predictions (mean of 4.5 per individual, SD = 2.9) and 371 (mean = 4.5, SD = 3.0) recollection responses were collected. Of these, 232 (61.7%) seizure predictions and 288 (77.6%) recollection responses were correct (TP + TN). 3.1. Prediction and recall accuracy Based on the results of the generalized mixed-effects model, on days when patients made a negative prediction of the upcoming seizure, the probability of having one equaled 0.151 (95% CI: 0.071– 0.228). In comparison, when participants predicted having a seizure, the probability doubled to 0.320 (95% CI: 0.149–0.558), corresponding to a significant increase (P = 0.024). That is, patients were significantly more likely to have a seizure when one was predicted prior. Similarly, on occasions with no seizure occurrence, the rate of seizure awareness was approximately 0.130 (95% CI: 0.073–0.219). When a seizure occurred, the rate of seizure awareness increased significantly (P b 0.001) to 0.628 (95% CI: 0.439–0.784). 3.2. Correlates of seizure prediction and awareness Table 3 summarizes results of four generalized mixed-effects models, testing the associations between clinical and psychological variables and patients' ability to make an accurate seizure prediction or recollection. Based on the first model, the rate of TP seizure prediction was heavily dependent on the hospital seizure rate (P b 0.001). The left graph in Fig. 1 further explores the relationship between these two variables, signifying an increase in seizure predictions for individuals with higher overall hospital seizure rate. Conversely, based on the second model (Table 3), the TN predictions decreased with an increase in the hospital seizure rate (P b 0.001). The graphic relationship between these variables (Fig. 1, left) mirrors that of the TP prediction. Additionally, “years since diagnosis” was positively related to the TN prediction (P = 0.041), such that having the seizure diagnosis for a longer period translated into higher TN prediction rates. Although significant, the magnitude of this relationship is very small: for every additional year of diagnosis the original odds ratio increases by 4%. Experiencing PS was associated inversely with the accuracy of negative seizure prediction (P = 0.022), such that the probability of a correct TN prediction would decrease almost 75% on occasions when PS was experienced but was not associated with TP predictions. Patients' recall of seizure occurrence was also highly related to hospital seizure rate (model 3, Table 3). The strength of this relationship is apparent from the right graph in Fig. 1 (P b 0.001). Finally, the TN recollection was also related to hospital seizure rate (P b 0.001) (model 4, Table 3), with the graphic relationship summarized on the left of Fig. 1. To determine if seizure rate was affected by time as a result of medication withdrawal, a separate mixed-effects model was generated with days as a predictor of seizure rate; no significant effect was observed.
108
J.M. DuBois et al. / Epilepsy and Behavior 18 (2010) 106–109
Table 3 Effects of clinical and questionnaire variables on seizure prediction and awareness. Correlates
Intercept Inpatient seizure rate Outpatient seizure rate Years since diagnosis Can abort a seizure Can predict a seizure Premonitory symptoms
Model 1 TP prediction
Model 2 TN prediction
Model 3 TP memory
Model 4 TN memory
Estimate (SE)
OR
Estimate (SE)
OR
Estimate (SE)
OR
Estimate (SE)
OR
–5.103 6.031 0.002 –0.005 0.823 0.708 0.527
0.006 416.131 1.002 0.995 2.277 2.030 1.164
1.271 –7.901 –0.006 0.042 0.454 –0.162 –1.390
3.564 0.0004 0.994 1.042 1.575 0.850 0.249
–3.841 6.553 0.001 –0.002 0.181 0.075 0.918
0.021 701.345 1.001 0.998 1.198 1.078 2.504
2.660 –8.362 –0.055 –0.005 –0.263 –0.198 –0.883
14.296 0.0002 0.946 0.995 0.767 0.820 0.414
(0.761) (0.963)a (0.002) (0.019) (0.491)c (0.555) (0.596)
(0.534) (1.299)a (0.003) c (0.020) b (0.564) (0.551) (0.608)b
(0.488) (0.787)a (0.001) (0.013) (0.364) (0.394) (0.485)c
(0.376) (0.994)a (0.014)a (0.012) (0.343) (0.346) (0.519)c
Note. Estimates of the generalized mixed-effects models should be interpreted in a fashion similar to the parameters of logistic regression. The estimates and corresponding SE are reported on the logit scale; odds ratios (OR) are exponents of the estimates. a P b 0.001. b P b 0.05. c P b 0.1.
Additionally, higher prehospitalization seizure history was predictive of lower TN accuracy rates (P b 0.001). In all models, patients' beliefs in whether they could predict or abort a seizure did not correlate with the accuracy of seizure prediction or awareness.
4. Discussion Our findings suggest that some patients with epilepsy can predict their seizures when undergoing VEEG monitoring. On days when positive predictions were made, seizures were twice as frequent as on days when no such predictions were made. These results mirror prior outpatient research, which also observed a twofold increase in seizures following a positive prediction, despite disparate seizure rates (9.8% vs 25% in our sample) [8]. This difference in frequency may have been due to sleep deprivation and reduced dosage and increments of medication intended to provoke seizures during clinical monitoring. As a result, outpatient seizure frequency did not affect predictive accuracy. Although precipitants may contribute to patients' perceived selfcontrol of their seizures [1–6], our analysis showed that patients' beliefs regarding their ability to predict or abort seizures did not correlate with the accuracy of predictions. Other means of prediction, such as premonitory symptoms, similarly did not increase predictive accuracy when recalled by patients following seizures. The only factor that influenced predictions in our analysis was the rate of in-hospital seizures. The magnitude of this effect was large, and in our analysis it
was not affected by the length of inpatient monitoring. For patients with a daily seizure rate, the accuracy of predictions was nearly 90%. In addition, for individuals with a daily in-hospital seizure rate, the accuracy of seizure recollection approached 100%. Patients correctly recalled whether or not they had a seizure in the previous 24 hours 77% of the time. On days when positive recollections were made, seizures were approximately five times as frequent as on days when no such recollections were made. These findings support prior evidence of limited reliability of patients' reports of their seizures [19]. Overall, we found that some patients were able to accurately predict and recall their seizures, although predictions were not informed by precipitants, premonitory symptoms, or outpatient frequency. Prediction was significantly influenced by inpatient frequency. As patients were observed to be highly aware of their seizures, predictions may have been based on awareness of day-today changes in frequency. Generalizability of these findings is limited by the nonrepresentative nature of a clinical VEEG monitoring patient population and limited sample size. In addition, patients' awareness and their potential risk of a seizure could have been conveyed or even reinforced by the nursing or medical staff or family members. However, similar risks exist in outpatient studies as information on seizure occurrence or risk can be reinforced by family members and friends. Also, the differences between the outpatient and clinical environments, specifically the provocation of seizures, may underlie the lack of premonitory features experienced by patients. Lastly, the use of a clinical inpatient population did not allow for extensive
Fig. 1. Relationship between hospital seizure (SZ) rate and accuracy of seizure predication and recall.
J.M. DuBois et al. / Epilepsy and Behavior 18 (2010) 106–109
subgroup analysis to determine if predictive accuracy was uniformly distributed across the population. Our observation that patients with epilepsy can be successful in predicting and recalling their seizures suggests that future research should further explore how patients predict seizures. Greater knowledge about the recognition and clinical and EEG features of this state could significantly impact seizure prevention and minimize risks due to seizures. An understanding of clinical and physiological changes that precede seizures could provide the basis for both targeted patient training and the use of preemptive medication to avert seizures. The greatest fear of epilepsy, unpredictability, could be reduced.
References [1] Boylan LS. Peri-ictal behavioral and cognitive changes. Epilepsy Behav 2002;3: 16–26. [2] Nakken KO, Solaas MH, Kjeldsen MJ, et al. Which seizure-precipitating factors do patients with epilepsy most frequently report? Epilepsy Behav 2005;6:85–9. [3] Frucht MM, Quigg M, Schwaner C, Fountain NB. Distribution of seizure precipitants among epilepsy. Epilepsia 2000;41:1534–9. [4] Spatt J, Langbauer G, Mamoli B. Subjective perception of seizure precipitants: results of a questionnaire study. Seizure 1998;7:391–439. [5] Tan JH, Wilder-Smith E, Lim ECH, Ong BKC. Frequency of provocative factors in epileptic patients admitted for seizures: a prospective study in Singapore. Seizure 2005;14:464–9. [6] Pinikahana J, Dono J. Age and gender differences in initial symptoms and precipitant factors of epileptic seizures: an Australian study. Epilepsy Behav 2009;16:231–9.
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[7] Da Silva Sousa P, Lin K, Garzon E, Sakamoto AC, Yacubian EM. Self-perception of factors that precipitate or inhibit seizures in juvenile myoclonic epilepsy. Seizure 2005;14:340–6. [8] Haut SR, Hall CB, LeValley AJ, Lipton RB. Can patients with epilepsy predict their seizures? Neurology 2007;68:262–6. [9] Hughes J, Devinsky O, Feldmann E, Bromfield E. Premonitory symptoms in epilepsy. Seizure 1993;2:201–3. [10] Rajna P, Clemens B, Csibri E, et al. Hungarian multicentre epidemiologic study of the warning and initial symptoms (prodrome, aura) of epileptic seizures. Seizure 1997;6:361–8. [11] Schulze-Bonhage A, Kurth C, Carius A, Steinhoff BJ, Mayer T. Seizure anticipation by patients with focal and generalized epilepsy: a multicentre assessment of premonitory symptoms. Epilepsy Res 2006;70:83–8. [12] Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure anticipation: do mathematical measures correlate with video-EEG evaluation? Epilepsia 2005;46: 1335–6. [13] Litt B, Esteller R, Echauz J, et al. Epileptic seizures may begin hours in advance of clinical onset: a report of five patients. Neuron 2001;30:51–64. [14] Spector S, Cull C, Goldstein LH. High and low perceived self-control of epileptic seizures. Epilepsia 2001;42:556–64. [15] Spector S, Cull C, Goldstein LH. Seizure precipitants and perceived self-control of seizures in adults with poorly-controlled epilepsy. Epilepsy Res 2000;38:207–16. [16] Cull CA, Fowler M, Brown SW. Perceived self-control of seizures in young people with epilepsy. Seizure 1996;5:131–8. [17] Pritchard III PB, Holmstrom VL, Giacinto J. Self-abatement of complex partial seizures. Ann Neurol 1985;18:265–7. [18] Lee SA, No YJ. Perceived self-control of seizures in patients with uncontrolled partial epilepsy. Seizure 2005;14:100–5. [19] Blum DE, Eskola J, Bortz JJ, Fisher RS. Patient awareness of seizures. Neurology 1996;47:260–4. [20] Pinheiro J, Bates D. Mixed effects models in S and S-Plus. New York: SpringerVerlag; 2000. [21] R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2008.
Contents lists available at ScienceDirect
Epilepsy and Behavior j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / ye b e h
Seizure prediction and recall J.M. DuBois a,⁎, L.S. Boylan a,b, M. Shiyko c, W.B. Barr a,d, O. Devinsky a,d,e,f a
Department of Neurology, NYU Langone School of Medicine, New York, NY, USA Department of Veterans Affairs New York Harbor Healthcare System, New York, NY, USA c The Methodology Center, Pennsylvania State University, State College, PA, USA d Department of Psychiatry, NYU Langone School of Medicine, New York, NY, USA e Institute of Neurology, Saint Barnabas Medical Center, West Orange, NJ, USA f Department of Neurosurgery, NYU Langone School of Medicine, New York, NY, USA b
a r t i c l e
i n f o
Article history: Received 26 January 2010 Received in revised form 19 March 2010 Accepted 22 March 2010 Available online 10 May 2010 Keywords: Epilepsy Prediction Awareness Seizures
a b s t r a c t Using separate generalized mixed-effects models, we assessed seizure recall and prediction, as well as contributing diagnostic variables, in 83 adult patients with epilepsy undergoing video/EEG monitoring. The model revealed that when participants predicted a seizure, probability equaled 0.320 (95% CI: 0.149–0.558), a significant (P b 0.05) increase over negative predictions (0.151, 95% CI: 0.71–0.228]). With no seizure, the rate of remembering was approximately 0.130 (95% CI: 0.73–0.219), increasing significantly to 0.628 (95% CI: 0.439 to 0.784) when a seizure occurred (P b 0.001). Of the variables analyzed, only inpatient seizure rate influenced predictability (P b 0.001) or recollection (P b 0.001). These models reveal that patients were highly aware of their seizures, and in many cases, were able to make accurate predictions, for which seizure rate may be an important factor. © 2010 Elsevier Inc. All rights reserved.
1. Introduction Unpredictability is one of the most disabling and frightening aspects of epilepsy. This problem could be mitigated by recognition of factors that provoke seizures, premonitory symptoms (PS) that predict seizures, and the ability to abort impending seizures. Patients and clinicians frequently identify seizure-provoking factors including missed medication, sleep deprivation and fatigue, emotional stress, excess alcohol, perimenstrual and ovulatory phases, illness and fever, exercise, and environmental stimuli [1–6]. Specific precipitants are well established (e.g., sleep deprivation, menstrual phases) whereas others (e.g., emotional stress) are supported by limited data [7,8]. Some patients report that they can predict when they are seizureprone because of the premonitory mental or physical changes that precede seizures. One-third of patients with partial epilepsy experience PS that may occur 30 minutes to 3 days before seizures [9]. PS are often vague (funny feeling) or nonspecific (headache, dysphoria, confusion, and irritability) [10,11] and precede more than half of seizures for individuals who experience them [11]. EEG changes hours before seizure onset are a potential physiological correlate of predictions [12,13]. Patients may seek low-risk-for-seizure situations, possibly reducing seizure occurrence and increasing perceived selfcontrol [14]. Many patients report self-control of seizures using
⁎ Corresponding author. NYU Epilepsy Center, 223 East 34th Street, New York NY 10016, USA. Fax: +1 646 385 7164. E-mail address: [email protected] (J.M. DuBois). 1525-5050/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.yebeh.2010.03.011
relaxation, distraction, mood changes, or additional medications [10,15–18]. Few studies have examined whether patients' perceived selfcontrol translates into accurate awareness and prediction of seizures; none examined this issue in a monitored clinical setting. In one diarybased outpatient sample, 32% successfully predicted their seizures, although highly accurate predictions were concentrated among 22% of the group [8]. Limited research has been done on seizure recall among video/EEG (VEEG)-monitored patients. In one small series, only 26% of patients with complex partial epilepsy were consistently able to recall their seizures [19]. Therefore, the current study aimed to determine the seizure recall and predictive ability of adult patients with epilepsy undergoing VEEG monitoring in a controlled setting. 2. Methods 2.1. Subjects Eighty-three individuals were recruited at the New York University Epilepsy Center for a prospective study on seizure prediction and awareness between January 2006 and March 2007. To be eligible, the individuals had to be ≥ 18 years old, mentally competent, and experiencing epileptic seizures. The distribution of seizure types prior to admission is summarized in Table 1. Patients in whom nonepileptic seizures were recorded were excluded from analysis. All participants provided written consent to the study, which was approved by the NYU Langone School of Medicine institutional review board.
J.M. DuBois et al. / Epilepsy and Behavior 18 (2010) 106–109 Table 1 Seizure types prior to clinical monitoring. Type of seizure
Number of patients
Percentage of group
Partial Complex partial Simple partial Secondarily generalized Multifocal Generalized Other Unknown
38 11 13 13 33 5 8
46% 13% 16% 16% 40% 6% 10%
days on which a seizure occurred during the present study (mean = 0.252, SD = 24), years since diagnosis (mean = 18.5 years, SD = 13.3), personal belief about the ability to anticipate a seizure (66%), memory of PS (29%), and personal belief about the ability to prevent a seizure (29%), on each of these matched outcomes. All analyses were carried out in the open-source statistical software R [21]. 3. Results
Note. Patients estimated their frequency of prior seizure types per month averaged over the past year. Shown here is the group representation of various seizure types based on patients' frequency estimates.
2.2. Procedure On hospital admission, participants were asked to complete a survey regarding their ability to abort and anticipate seizures, as well as common precipitants. Daily questionnaires were then administered asking whether or not patients could recall having a seizure in the past 24 hours (yes/no/do not know) and whether they predicted having one in the next 24 hours (yes/no/do not know). These were generally administered in the morning, although completely accurate timing was not possible because of complications inherent to the clinical setting. Information regarding subjects' seizure history, such as type and frequency of pre-admission seizures and duration of illness, was collected through chart review. VEEG reports provided the description of clinical events during subjects' admission. 2.3. Statistical analysis To study the extent to which patients could predict daily seizures, a generalized mixed-effects model [20] was fitted to the data, where daily seizure status (1 = seizure, 0 = no seizure) was regressed on daily seizure predictions (1 = predict a seizure, 0 = predict no seizure). To examine whether or not individuals could recollect their seizure occurrence accurately, a separate model was fitted to seizure recall (1 = recall a seizure, 0 = recall having no seizure) as a function of seizure status. The methodology accounted for the observation interdependence due to repeated assessments (ICC = 0.527), missing data (thus preserving information on individuals who did not provide responses for all daily assessments), and random variability in patient responses. In addition, four binary outcome indicators representing concordance between personal assessments and seizure occurrences were constructed to replicate previous methodology (Table 2). True positive (TP) predictions reflected occasions on which individuals accurately predicted their seizure, and TP awareness corresponded to seizure events that were accurately recalled. In a similar fashion, true negative (TN) predictions characterized predictions of not having a seizure that actually did not occur, and TN awareness indicated that a patient accurately recalled not having a seizure. Four generalized mixed-effects models were fitted to study the effects of the monthly outpatient seizure frequency (mean = 11.25, SD = 26.14, two patients were excluded for excessive estimations), Table 2 Prediction and memory outcomes.
Positive prediction Negative prediction Positive memory Negative memory
107
Seizure
No seizure
46 (TP) 56 (FN) 73 (TP) 30 (FN)
88 186 53 215
(FP) (TN) (FP) (TN)
Note. The contingency table displays the tabulated evaluation of recalled and predicted seizures.
Average age of study participants was 39 (SD = 13.8); women constituted 53% of the sample. Clinical assessment of seizure status was made on all but 4 hospital days, yielding 567 assessments (mean = 6.8 per individual, SD = 3.1), with seizures occurring on 143 days. A total of 376 predictions (mean of 4.5 per individual, SD = 2.9) and 371 (mean = 4.5, SD = 3.0) recollection responses were collected. Of these, 232 (61.7%) seizure predictions and 288 (77.6%) recollection responses were correct (TP + TN). 3.1. Prediction and recall accuracy Based on the results of the generalized mixed-effects model, on days when patients made a negative prediction of the upcoming seizure, the probability of having one equaled 0.151 (95% CI: 0.071– 0.228). In comparison, when participants predicted having a seizure, the probability doubled to 0.320 (95% CI: 0.149–0.558), corresponding to a significant increase (P = 0.024). That is, patients were significantly more likely to have a seizure when one was predicted prior. Similarly, on occasions with no seizure occurrence, the rate of seizure awareness was approximately 0.130 (95% CI: 0.073–0.219). When a seizure occurred, the rate of seizure awareness increased significantly (P b 0.001) to 0.628 (95% CI: 0.439–0.784). 3.2. Correlates of seizure prediction and awareness Table 3 summarizes results of four generalized mixed-effects models, testing the associations between clinical and psychological variables and patients' ability to make an accurate seizure prediction or recollection. Based on the first model, the rate of TP seizure prediction was heavily dependent on the hospital seizure rate (P b 0.001). The left graph in Fig. 1 further explores the relationship between these two variables, signifying an increase in seizure predictions for individuals with higher overall hospital seizure rate. Conversely, based on the second model (Table 3), the TN predictions decreased with an increase in the hospital seizure rate (P b 0.001). The graphic relationship between these variables (Fig. 1, left) mirrors that of the TP prediction. Additionally, “years since diagnosis” was positively related to the TN prediction (P = 0.041), such that having the seizure diagnosis for a longer period translated into higher TN prediction rates. Although significant, the magnitude of this relationship is very small: for every additional year of diagnosis the original odds ratio increases by 4%. Experiencing PS was associated inversely with the accuracy of negative seizure prediction (P = 0.022), such that the probability of a correct TN prediction would decrease almost 75% on occasions when PS was experienced but was not associated with TP predictions. Patients' recall of seizure occurrence was also highly related to hospital seizure rate (model 3, Table 3). The strength of this relationship is apparent from the right graph in Fig. 1 (P b 0.001). Finally, the TN recollection was also related to hospital seizure rate (P b 0.001) (model 4, Table 3), with the graphic relationship summarized on the left of Fig. 1. To determine if seizure rate was affected by time as a result of medication withdrawal, a separate mixed-effects model was generated with days as a predictor of seizure rate; no significant effect was observed.
108
J.M. DuBois et al. / Epilepsy and Behavior 18 (2010) 106–109
Table 3 Effects of clinical and questionnaire variables on seizure prediction and awareness. Correlates
Intercept Inpatient seizure rate Outpatient seizure rate Years since diagnosis Can abort a seizure Can predict a seizure Premonitory symptoms
Model 1 TP prediction
Model 2 TN prediction
Model 3 TP memory
Model 4 TN memory
Estimate (SE)
OR
Estimate (SE)
OR
Estimate (SE)
OR
Estimate (SE)
OR
–5.103 6.031 0.002 –0.005 0.823 0.708 0.527
0.006 416.131 1.002 0.995 2.277 2.030 1.164
1.271 –7.901 –0.006 0.042 0.454 –0.162 –1.390
3.564 0.0004 0.994 1.042 1.575 0.850 0.249
–3.841 6.553 0.001 –0.002 0.181 0.075 0.918
0.021 701.345 1.001 0.998 1.198 1.078 2.504
2.660 –8.362 –0.055 –0.005 –0.263 –0.198 –0.883
14.296 0.0002 0.946 0.995 0.767 0.820 0.414
(0.761) (0.963)a (0.002) (0.019) (0.491)c (0.555) (0.596)
(0.534) (1.299)a (0.003) c (0.020) b (0.564) (0.551) (0.608)b
(0.488) (0.787)a (0.001) (0.013) (0.364) (0.394) (0.485)c
(0.376) (0.994)a (0.014)a (0.012) (0.343) (0.346) (0.519)c
Note. Estimates of the generalized mixed-effects models should be interpreted in a fashion similar to the parameters of logistic regression. The estimates and corresponding SE are reported on the logit scale; odds ratios (OR) are exponents of the estimates. a P b 0.001. b P b 0.05. c P b 0.1.
Additionally, higher prehospitalization seizure history was predictive of lower TN accuracy rates (P b 0.001). In all models, patients' beliefs in whether they could predict or abort a seizure did not correlate with the accuracy of seizure prediction or awareness.
4. Discussion Our findings suggest that some patients with epilepsy can predict their seizures when undergoing VEEG monitoring. On days when positive predictions were made, seizures were twice as frequent as on days when no such predictions were made. These results mirror prior outpatient research, which also observed a twofold increase in seizures following a positive prediction, despite disparate seizure rates (9.8% vs 25% in our sample) [8]. This difference in frequency may have been due to sleep deprivation and reduced dosage and increments of medication intended to provoke seizures during clinical monitoring. As a result, outpatient seizure frequency did not affect predictive accuracy. Although precipitants may contribute to patients' perceived selfcontrol of their seizures [1–6], our analysis showed that patients' beliefs regarding their ability to predict or abort seizures did not correlate with the accuracy of predictions. Other means of prediction, such as premonitory symptoms, similarly did not increase predictive accuracy when recalled by patients following seizures. The only factor that influenced predictions in our analysis was the rate of in-hospital seizures. The magnitude of this effect was large, and in our analysis it
was not affected by the length of inpatient monitoring. For patients with a daily seizure rate, the accuracy of predictions was nearly 90%. In addition, for individuals with a daily in-hospital seizure rate, the accuracy of seizure recollection approached 100%. Patients correctly recalled whether or not they had a seizure in the previous 24 hours 77% of the time. On days when positive recollections were made, seizures were approximately five times as frequent as on days when no such recollections were made. These findings support prior evidence of limited reliability of patients' reports of their seizures [19]. Overall, we found that some patients were able to accurately predict and recall their seizures, although predictions were not informed by precipitants, premonitory symptoms, or outpatient frequency. Prediction was significantly influenced by inpatient frequency. As patients were observed to be highly aware of their seizures, predictions may have been based on awareness of day-today changes in frequency. Generalizability of these findings is limited by the nonrepresentative nature of a clinical VEEG monitoring patient population and limited sample size. In addition, patients' awareness and their potential risk of a seizure could have been conveyed or even reinforced by the nursing or medical staff or family members. However, similar risks exist in outpatient studies as information on seizure occurrence or risk can be reinforced by family members and friends. Also, the differences between the outpatient and clinical environments, specifically the provocation of seizures, may underlie the lack of premonitory features experienced by patients. Lastly, the use of a clinical inpatient population did not allow for extensive
Fig. 1. Relationship between hospital seizure (SZ) rate and accuracy of seizure predication and recall.
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subgroup analysis to determine if predictive accuracy was uniformly distributed across the population. Our observation that patients with epilepsy can be successful in predicting and recalling their seizures suggests that future research should further explore how patients predict seizures. Greater knowledge about the recognition and clinical and EEG features of this state could significantly impact seizure prevention and minimize risks due to seizures. An understanding of clinical and physiological changes that precede seizures could provide the basis for both targeted patient training and the use of preemptive medication to avert seizures. The greatest fear of epilepsy, unpredictability, could be reduced.
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