The unpredictability of seizures remains one of the most challenging aspects of epilepsy (Murray, 1993; Fisher, 2000). Simply knowing when a seizure is coming, may in itself, reduce the burden of unpredictability and improve health-related quality of life (Schulze-Bonhage & Buller, 2008). For the most part, efforts to predict seizures have relied on electroencephalography (EEG) data, although the concept of self-prediction of seizures by persons with epilepsy has been the focus of increasing research and discussion (Spector et al., 2000; Lee & No, 2005; Schulze-Bonhage et al., 2006; Haut et al., 2007a; Dionisio & Tatum, 2010; DuBois et al., 2010). In questionnaire studies, many patients report a “pre-seizure state” characterized by prodromal or premonitory symptoms (Hughes et al., 1993; Rajna et al., 1997; Lee & No, 2005; Petitmengin et al., 2006; Schulze-Bonhage et al., 2006; Scaramelli et al., 2009); more recently, prodromes and seizure self-prediction have been investigated in prospective studies (Haut et al., 2007a; DuBois et al., 2010; Maiwald et al., 2011).
In a paper diary study, we showed that a subset of patients with localization-related epilepsy (LRE) successfully predicted their seizures over a 24 h window (Haut et al., 2007a). We conceptualize seizure self-prediction as a conscious or subconscious awareness of prodromal features, trigger factors, and possibly unmeasured variables such as state correlates of electrophysiologic changes.
To further explore the nature of clinical seizure self-prediction, we conducted an e-diary study that is the basis of the present report. We also included an extensive inventory of trigger factors, premonitory symptoms, and measures of mood, thereby expanding our ability to characterize the preictal state. Based on these data, we reported clinical features of the preictal state, demonstrating that mood changes and premonitory features predicted seizure occurrence over 12 h (Haut et al., 2012).
Our primary aim in the present report is to confirm clinical seizure self-prediction utilizing electronic data capture to provide time-stamped data collection, thereby reducing the potential for retrospective reporting and recall bias. Furthermore, because of the collection of exposure data twice daily and the time-stamped reporting of seizure onset, we are in a strong position to explore a number of secondary aims, including the following: defining time frames of seizure occurrence following self-prediction; assessing self-prediction as an outcome in its own right, independent of accuracy; identifying components of self-prediction and ultimately to improve its accuracy; and finally, determining the separate and joint effect of seizure self-prediction, mood and change in mood, as well as premonitory features on the subsequent occurrence of seizures. Insights into the predictability of seizures could lead to a novel approach to epilepsy treatment, namely, preemptive therapy during the preictal state.
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This study demonstrates that 9 (43%) of 19 participants with refractory partial epilepsy were able to accurately predict their seizures, drawing on awareness of prodromal features such as mood and premonitory symptoms. Self-prediction was more accurate in participants who were more confident in the accuracy of their predictions. For the most confident prediction choices, the odds of seizure increased more than eightfold compared to times when seizures were thought to be “very unlikely” in unadjusted models. Self-prediction was most robust for prediction windows of 6 h or less, remaining highly significant over 12 h but not for longer time frames.
These results confirm and extend findings from our previous paper diary study with nightly measures (Haut et al., 2007a). The present report is more robust and informative because the electronic diary format provides time stamping, because exposures were captured twice daily, and because we included detailed inventories of mood and premonitory features. We also show that elements of the prodromal state play a large role in seizure self-prediction and ultimately, in accurate modeling of seizure occurrence.
Identifying the elements that contribute to seizure self-prediction offers the possibility of both understanding and improving self-prediction (Fig. 1A). The most significant variables associated with self-prediction were favorable mood and number of reported premonitory features. As both of these elements are relatively easy for patients to attend to and record, this observation offers the promise of improved self-prediction with use of education and training.
Mood and stress are reported to be among the strongest seizure precipitants in both questionnaire and prospective diary studies (Neugebauer et al., 1994; Spector et al., 2000; Nakken et al., 2005; Haut et al., 2007b, 2012; Sperling et al., 2008). In this e-diary study, current mood and not change in mood influenced self-prediction in multivariate models, whereas change in mood was associated with actual seizure occurrence. Training patients to be more aware of mood change from one day to the next might improve their ability to self-predict seizures accurately and yield more powerful models of seizure probability (Fig. 1B).
Premonitory symptoms make a strong contribution to self-prediction, which similarly offers opportunities to train patients about their own symptoms. Of note, premonitory features have been examined in a number of studies to date with conflicting results (Schulze-Bonhage et al., 2006; Maiwald et al., 2011; Haut et al., 2012), as was discussed in a recent review (Schulze-Bonhage & Haut, 2011).
In the modeling of seizure occurrence, self-prediction, favorable change in mood, and premonitory features remain independent predictors (Fig. 1B). The OR for the associations in multivariate modeling suggests that self-prediction and premonitory symptoms both contribute to accurately assessing the probability of seizure occurrence.
Significant seizure self-prediction has been similarly reported in the inpatient epilepsy monitoring setting (DuBois et al., 2010). Developing seizure self-prediction and seizure occurrence models may have important clinical implications. If “at risk” seizure states can be identified, interventions can range from taking precautionary measures to the actual use of preemptive therapies. Preemptive treatment will rely on robust modeling of seizure probability, of which seizure self-prediction may be a significant contributor. There is no evidence-based approach for pre-emptive therapy in adult epilepsy, although in practice clinicians may prescribe oral benzodiazepines for use in certain settings. If clinically based seizure prediction becomes more robust, candidate preemptive treatments might include short-term use of benzodiazepines, or even supplemental antiepileptic medications. The association between mood and prediction suggests the possibility of utilizing a behavioral intervention during periods of increased seizure risk. In fact, a randomized controlled e-diary trial of a behavioral intervention is currently being conducted (Polak et al., 2012).
As in other studies (Haut et al., 2007a; DuBois et al., 2010), predictive ability was not uniformly distributed among patients. The current cohort was enriched with subjects who described perceived self-predictive ability and/or awareness of precipitants, and almost 50% of the subjects demonstrated significant self-prediction. This percentage is much higher than in our previous study where 21% of the subjects were significant predictors. Here, older age was associated with better predictive ability, in contrast to our prior study where younger patients were better predictors, (Haut et al., 2007a). DuBois et al. (2010) reported that subjects with a longer duration of epilepsy were better at predicting “no-seizure” days. The current findings support the concept that longer experience with seizures is associated with more accurate prediction. DuBois et al. (2010) also found that higher seizure rates were associated with better prediction, which was not the case in our current study. This disparity may well relate to differences between outpatient and inpatient seizure frequencies.
Is self-prediction and seizure modeling ready for clinical use? Seizure self-prediction has a very high specificity (Haut et al., 2007a; DuBois et al., 2010), reflecting the accuracy of negative predictions. Successful negative prediction is important for preemption; if the intervention carries any risk, this will limit unnecessary treatment. A clinically relevant preemptive therapy also requires high sensitivity. In the group of predictors, median sensitivity and specificity were 50% and 95%. These numbers, although sufficient for a behavioral intervention, will not support a preemptive pharmacologic trial, but may be improved with training.
Our study has certain limitations. Our primary outcome measure is the occurrence of self-reported seizures as recorded using an electronic diary. This approach is vulnerable to errors of both underreporting or overreporting of seizures (Neugebauer, 1989; Blum et al., 1996; Tatum et al., 2001; Hoppe et al., 2007; Cook et al., 2013).
The accuracy of self-reported seizures is a concern, as recently reported in a long-term study using implanted electrodes, where disparities between reports of seizures in patient diaries and electrographic seizure patterns on EEG reached statistical significant in almost one-third of subjects (Cook et al., 2013). Although this area requires additional attention, continuous EEG monitoring is rarely available. As a consequence, we will continue to rely on self-report both in clinical trials and clinical practice for the foreseeable future. However, unless errors in seizure reporting are associated with the exposures of interest, we would expect our reported associations to get stronger with perfect reporting of seizure occurrence.
Another challenge in a seizure self-prediction study is that patients may be predicting a seizure during their aura, reporting the “ictal” and not “pre-ictal” state. Absent EEG monitoring, this possibility cannot be completely ruled out. However, the most accurate prediction window of this study was 4–6 h after a self-prediction, whereas a reported seizure would be expected to follow an aura report by minutes. Finally, although the number of subjects is modest, we had >3000 diary days and almost 250 seizures. The positive results support our feeling that the sample size is appropriate to confirm seizure self-prediction using electronic data capture.
There remains modeling evidence that as yet unmeasured variables are contributing to seizure self-prediction. These variables may represent other biologic phenomenon that patients recognize as heralding a seizure, for example self-awareness of electrophysiologic changes. A follow-up study that includes continuous EEG monitoring, while logistically challenging, would likely clarify the phenomenon of self-prediction even further.
Our data confirm our previous findings that seizure self-prediction is possible for a subgroup of patients with epilepsy, and that in these individuals, the odds of a seizure following a positive prediction is high. Although these findings may only be generalizable to patients who report either self-predictive ability or awareness of seizure precipitants to their clinicians, prevalence studies indicate that this may be a substantial subgroup. Improvement in predictive ability will be necessary for a planned preemptive trial; this may be accomplished with education and training individuals on their own data, focusing on features of the prodromal state such as premonitory symptoms, and change in mood. Ultimately, EEG analysis may also be utilized in combination with self-prediction to enhance the effectiveness of both techniques. We anticipate that this work may represent a step toward a new paradigm of treatment, namely preemptive therapy for epilepsy.
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Dr. Haut receives grant support from National Institute of Health (NIH) (RO1 NS053998) and the Shor Foundation for Epilepsy Research. She has consulted for Acorda, Upsher-Smith, and Impax. She is on the editorial boards of Epilepsy and Behavior and Epilepsy Currents. Dr. Hall receives or has received research support from the National Institute of Aging (NIA) (P01 AG03949, P01 AG027734, R01 AG022092, R01 AG034087, R21 AG036935), the National Center for Research Resources (UL1-RR025750-01), the National Cancer Institute (P30 CA13330-35), and the National Institute of Occupational Safety and Health (contracts 200-2011-39372 and 200-2011-39489 39379, and grants U01-OH10411 and U01-OH10412, the last as principal investigator). Dr. Borkowski has nothing to disclose. Dr. Tennen receives research support from the NIH 5P60AA003510-33, Center Component PI and Center Component Investigator; 5R01AA016599-03, Subcontractor; 5R01AA12827-07, Investigator; 1R01DA031275-01A1, Investigator. He serves as a consultant or has received honoraria from: John Wiley & Sons, and Best Practice Project Management. Dr. Richard B. Lipton receives research support from the NIH [PO1 AG03949 (Program Director), RO1AG025119 (Investigator), RO1AG022374-06A2 (Investigator), RO1AG034119 (Investigator), RO1AG12101 (Investigator), K23AG030857 (Mentor), K23NS05140901A1 (Mentor), and K23NS47256 (Mentor)], the National Headache Foundation, and the Migraine Research Fund; serves on the editorial board of Neurology, has reviewed for the NIA and National Institute of Neurological Disorders and Stroke (NINDS), holds stock options in eNeura Therapeutics, serves as consultant, advisory board member, or has received honoraria from: Allergan, American Headache Society, Autonomic Technologies, Boehringer-Ingelheim Pharmaceuticals, Boston Scientific, Bristol Myers Squibb, Cognimed, Colucid, Eli Lilly, ENDO, eNeura Therapeutics, GlaxoSmithKline, Merck, Novartis, NuPathe, Pfizer, and Vedanta. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.