Prediction of seizure recurrence risk following discontinuation of antiepileptic drugs

Antiseizure medication withdrawal in adult patients with idiopathic generalized epilepsy: Performance of two seizure recurrence prediction models

PURPOSE

Currently, there is a limited availability of tools to predict seizure recurrence after discontinuation of antiseizure medications (ASMs). This study aimed to establish the seizure recurrence rate following ASM cessation in adult patients with idiopathic generalized epilepsy (IGE) and to assess the predictive performance of the Lamberink and the Stevelink prediction models using real-world data.

METHODS

Retrospective longitudinal study in IGE patients who underwent ASM withdrawal in a tertiary epilepsy clinic since June 2011, with the latest follow up in January 2024. The minimum follow-up period was 12 months. Clinical and demographic variables were collected, and the seizure recurrence prediction models proposed by Lamberink and Stevelink were applied and evaluated.

RESULTS

Forty-seven patients (mean age 33.15 ± 8 [20-55] years; 72.35 % women) were included. During the follow-up period, seizures recurred in 25 patients (53.2 %). Median time to recurrence was 8 months [IQR 3-13.5 months], and 17 patients (68 %) relapsed within the first year. None of the relapsing patients developed drug-resistant epilepsy. The only significant risk factor associated with recurrence was a seizure-free period of less than 2 years before discontinuing medication (91.7 % vs 40 %, p =.005). The Stevelink prediction model at both 2 (p =.015) and 5 years (p =.020) achieved statistical significance, with an AUC of 0.72 (95 % CI 0.56-0.88), while the Lamberink model showed inadequate prognostic capability.

CONCLUSION

In our real-world cohort, a seizure-free period of at least 2 years was the only factor significantly associated with epilepsy remission after ASM withdrawal. Larger studies are needed to accurately predict seizure recurrence in IGE patients.

Non-discontinuation of antiseizure medication in seizure-free epilepsy patients

BACKGROUND AND PURPOSE

In patients with epilepsy and sustained seizure freedom, guidelines recommend considering discontinuation of antiseizure medication (ASM) based on shared decision-making. This study aims to identify factors associated with non-discontinuation of ASM in seizure-free patients.

METHODS

Retrospective data from three sites of an academic outpatient clinic were analyzed. Adult patients with epilepsy who have been seizure-free for ≥24 months on ASM monotherapy were included. The primary end-point was non-discontinuation of ASM, defined as no discontinuation or no dose reduction of ≥25% at the last outpatient clinic visit in the ultimate seizure-free interval. Secondary end-points included frequency of discussion on discontinuation attempts between patients and physicians, adherence to ASM discontinuation decisions, and post-discontinuation seizure outcomes.

RESULTS

Out of 338 included patients, 81.7% did not discontinue ASM and did not reduce its dose, 11.5% discontinued ASM and 6.8% had a significant dose reduction. Factors independently associated with non-discontinuation of ASM were history of focal to bilateral or generalized tonic-clonic seizures (odds ratio [OR] 2.33, 95% confidence interval [CI] 1.08-5.06), history of breakthrough seizures (OR 3.32, 95% CI 1.10-10.04), history of failed attempts to discontinue or reduce the ASM dose (OR 4.67, 95% CI 1.03-21.11) and higher ASM load at the index visit (OR 6.10, 95% CI 2.09-17.78). Discontinuation attempts were made during the entire period of seizure freedom and were most commonly undertaken ≥10 years after the last seizure.

CONCLUSIONS

This study provides insights into factors associated with the shared decision-making process regarding ASM discontinuation in seizure-free patients and highlights the importance of considering individual patient characteristics and seizure history.

Postoperative interictal epileptiform discharges predict seizure recurrence after antiepileptic drug withdrawal regardless of concordance with surgical site

Objective

The study aimed to explore the association between the site of interictal epileptic discharges (IEDs) on postoperative electroencephalogram (EEG) and seizure recurrence after antiepileptic drug (AED) withdrawal. The study hypothesizes that the concordance of IED sites with surgical sites indicates incomplete resection of epileptic focus, while non-concordance of IED sites with surgical sites indicates postoperative changes or cortical stimulation. The former has a higher risk of seizure recurrence.

Methods

We retrospectively analyzed the postoperative EEG pattern of 182 consecutive children who underwent resection surgery. To identify the risk factors for seizure recurrence, we compared the attributes of seizure recurred and seizure-free groups by univariate and multivariate analyses. AED tapering was standardized, involving a 25% reduction in the dose of a single type of AED every 2 weeks, independent of the presurgical AED load.

Results

We attempted AED withdrawal in 116 (63.7%) children. Twenty-eight (24.1%) children experienced seizure recurrence during or after AED withdrawal. A greater number of AEDs used at the time of surgery (p=0.005), incomplete resection (p=0.001), and presence of IED on postoperative EEG (p=0.011) are predictors of seizure recurrence. The completeness of resection and seizure recurrence after AED withdrawal were related to the presence of IED on the EEG, but not to the concordance of IED with surgical sites.

Conclusion

For children with abnormal EEG, the decision to discontinue AED should be made more cautiously, regardless of the relative location of the discharge site and the surgical site.

Variation in seizure risk increases from antiseizure medication withdrawal among patients with well-controlled epilepsy: A pooled analysis

OBJECTIVE

Guidelines suggest considering antiseizure medication (ASM) discontinuation in seizure-free patients with epilepsy. Past work has poorly explored how discontinuation effects vary between patients. We evaluated (1) what factors modify the influence of discontinuation on seizure risk; and (2) the range of seizure risk increase due to discontinuation across low- versus high-risk patients.

METHODS

We pooled three datasets including seizure-free patients who did and did not discontinue ASMs. We conducted time-to-first-seizure analyses. First, we evaluated what individual patient factors modified the relative effect of ASM discontinuation on seizure risk via interaction terms. Then, we assessed the distribution of 2-year risk increase as predicted by our adjusted logistic regressions.

RESULTS

We included 1626 patients, of whom 678 (42%) planned to discontinue all ASMs. The mean predicted 2-year seizure risk was 43% [95% confidence interval (CI) 39%-46%] for discontinuation versus 21% (95% CI 19%-24%) for continuation. The mean 2-year absolute seizure risk increase was 21% (95% CI 18%-26%). No individual interaction term was significant after correcting for multiple comparisons. The median [interquartile range (IQR)] risk increase across patients was 19% (IQR 14%-24%; range 7%-37%). Results were unchanged when restricting analyses to only the two RCTs.

SIGNIFICANCE

No single patient factor significantly modified the influence of discontinuation on seizure risk, although we captured how absolute risk increases change for patients that are at low versus high risk. Patients should likely continue ASMs if even a 7% 2-year increase in the chance of any more seizures would be too much and should likely discontinue ASMs if even a 37% risk increase would be too little. In between these extremes, individualized risk calculation and a careful understanding of patient preferences are critical. Future work will further develop a two-armed individualized seizure risk calculator and contextualize seizure risk thresholds below which to consider discontinuation.

PLAIN LANGUAGE SUMMARY

Understanding how much antiseizure medications (ASMs) decrease seizure risk is an important part of determining which patients with epilepsy should be treated, especially for patients who have not had a seizure in a while. We found that there was a wide range in the amount that ASM discontinuation increases seizure risk-between 7% and 37%. We found that no single patient factor modified that amount. Understanding what a patient's seizure risk might be if they discontinued versus continued ASM treatment is critical to making informed decisions about whether the benefit of treatment outweighs the downsides.

Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study

BACKGROUND

The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event.

METHODS

This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database. The study population comprised patients with an initial diagnosis of either epilepsy or convulsions before the age of 21 years, based on International Classification of Diseases, Clinical Modification (ICD-CM) codes. We compared machine learning-based predictive modelling using structured data (logistic regression and XGBoost) with emerging techniques in natural language processing by use of large language models.

FINDINGS

The primary cohort comprised 14 021 patients at Boston Children's Hospital matching inclusion criteria with an initial seizure-like event and the comparison cohort comprised 15 062 patients within the IBM MarketScan research database. Seizure recurrence based on a composite expert-derived definition occurred in 57% of patients at Boston Children's Hospital and 63% of patients within IBM MarketScan. Large language models with additional domain-specific and location-specific pre-training on patients excluded from the study (F1-score 0·826 [95% CI 0·817-0·835], AUC 0·897 [95% CI 0·875-0·913]) performed best. All large language models, including the base model without additional pre-training (F1-score 0·739 [95% CI 0·738-0·741], AUROC 0·846 [95% CI 0·826-0·861]) outperformed models trained with structured data. With structured data only, XGBoost outperformed logistic regression and XGBoost models trained with the Boston Children's Hospital EMR (logistic regression: F1-score 0·650 [95% CI 0·643-0·657], AUC 0·694 [95% CI 0·685-0·705], XGBoost: F1-score 0·679 [0·676-0·683], AUC 0·725 [0·717-0·734]) performed similarly to models trained on the IBM MarketScan database (logistic regression: F1-score 0·596 [0·590-0·601], AUC 0·670 [0·664-0·675], XGBoost: F1-score 0·678 [0·668-0·687], AUC 0·710 [0·703-0·714]).

INTERPRETATION

Physician's clinical notes about an initial seizure-like event include substantial signals for prediction of seizure recurrence, and additional domain-specific and location-specific pre-training can significantly improve the performance of clinical large language models, even for specialised cohorts.

FUNDING

UCB, National Institute of Neurological Disorders and Stroke (US National Institutes of Health).

Long-term effectiveness and seizure recurrence risk factors of ketogenic diet for pediatric refractory epilepsy: Experience from a tertiary care center in China

OBJECTIVE

This study aimed to assess the long-term effectiveness and seizure recurrence risk in children with drug-resistant epilepsy who achieved seizure freedom on a ketogenic diet (KD). Predictors associated with seizure recurrence were also evaluated.

METHODS

Patients with drug-resistant epilepsy who received KD therapy for at least 3 months between May 2011 and April 2020 were included. The clinical efficacy of the KD was evaluated. Patients who achieved seizure freedom for at least 3 months on the KD were focused. Multivariate Cox regression models were used to explore the risk factors of seizure relapse in patients who achieved seizure freedom.

RESULTS

This study included 288 patients (163 males, 125 females). The seizure-free rates of the KD at 3, 6, 12, and 24 months were 9.7%, 16.7%, 14.2%, and 9.0%, respectively. Additionally, the seizure reduction rates between 50% and 99% were 46.5%, 39.9%, 30.2%, and 20.5%, respectively. Patients with Angelman syndrome (AS) showed the highest efficacy rate, followed by those with Dravet syndrome (DS). 51 patients achieved at least 3 months of seizure freedom on the KD. Seizures recurred in 24 (47.1%) patients. None of the patients with AS relapsed, while those with DS had the highest recurrence rate. The etiology of epilepsy, KD maintenance treatment period, and electroencephalography (EEG) abnormalities during follow-up differed significantly between patients with and without recurrence. However, multivariate Cox regression analysis indicated that a KD maintenance treatment period of less than 12 months and the presence of EEG abnormalities during follow-up were significantly correlated with a higher risk of relapse. Epilepsy control was restored in 3 of the 24 (12.5%) patients who experienced relapse.

SIGNIFICANCE

KD appears to be effective in children with various types of drug-resistant epilepsy. A short KD maintenance treatment period and EEG abnormalities during follow-up were associated with an increased risk of seizure recurrence.

The pharmacological treatment of epilepsy in adults

The pharmacological treatment of epilepsy entails several critical decisions that need to be based on an individual careful risk-benefit analysis. These include when to initiate treatment and with which antiseizure medication (ASM). With more than 25 ASMs on the market, physicians have opportunities to tailor the treatment to individual patients´ needs. ASM selection is primarily based on the patient's type of epilepsy and spectrum of ASM efficacy, but several other factors must be considered. These include age, sex, comorbidities, and concomitant medications to mention the most important. Individual susceptibility to adverse drug effects, ease of use, costs, and personal preferences should also be taken into account. Once an ASM has been selected, the next step is to decide on an individual target maintenance dose and a titration scheme to reach this dose. When the clinical circumstances permit, a slow titration is generally preferred since it is associated with improved tolerability. The maintenance dose is adjusted based on the clinical response aiming at the lowest effective dose. Therapeutic drug monitoring can be of value in efforts to establish the optimal dose. If the first monotherapy fails to control seizures without significant adverse effects, the next step will be to gradually switch to an alternative monotherapy, or sometimes to add another ASM. If an add-on is considered, combining ASMs with different modes of action is usually recommended. Misdiagnosis of epilepsy, non-adherence and suboptimal dosing are frequent causes of treatment failure and should be excluded before a patient is regarded as drug-resistant. Other treatment modalities, including epilepsy surgery, neuromodulation, and dietary therapies, should be considered for truly drug-resistant patients. After some years of seizure freedom, the question of ASM withdrawal often arises. Although successful in many, withdrawal is also associated with risks and the decision needs to be based on careful risk-benefit analysis.

Video-EEG-monitoring to guide antiseizure medication withdrawal

BACKGROUND

Discontinuing anti-seizure medication (ASM) should be considered in persons with epilepsy with long-term seizure freedom. Clinicians should also pursue ASM withdrawal in persons with one-time seizures without increased recurrence risk and those with suspected non-epileptic events. However, ASM withdrawal is associated with the risk of recurring seizures. Monitored ASM withdrawal in an epilepsy monitoring unit (EMU) could help better evaluate the risk of seizure recurrence. Here, we investigate the practice of EMU-guided ASM withdrawal, assess its indications, and aim to determine positive and negative predictors for successful withdrawal.

METHODS

We screened the medical records of all patients admitted to our EMU between November 1, 2019, and October 31, 2021, and included patients of at least 18 years admitted with the aim of permanent ASM withdrawal. We defined four groups of withdrawal indications: (1) long-term seizure freedom; (2) suspected non-epileptic events; (3) history of epileptic seizures but not fulfilling diagnostic criteria of epilepsy; and (4) seizure-freedom after epilepsy surgery. Successful withdrawal was defined according to the following criteria: no recoding of (sub)clinical seizure activity during VEM (groups 1, 2, and 3), patients did not meet the International League Against Epilepsy (ILAE) definition of epilepsy (groups 2 and 3) [14], and patients were discharged without ongoing ASM treatment (all groups). We also evaluated the prediction model by Lamberink et al. (LPM) for the risk of seizure recurrence in groups 1 and 3.

RESULTS

55/651 (8.6%) patients fulfilled the inclusion criteria. Withdrawal indications were distributed as follows; group 1: 2/55 (3.6%); group 2: 44/55 (80%); group 3: 9/55 (16,4%); group 4: 0/55. Overall, ASM withdrawal was successful in 90.9%. The sensitivity of the LPM for a 2-year 50% relapse risk threshold was 75%, the specificity 33.3%; for a 5-year relapse risk respectively 12.5% and 33.3%, suggesting that the model is not suitable for risk assessment in patients with one-time seizures or acute-symptomatic seizures, who constituted most of the evaluated patients.

CONCLUSIONS

Our study suggests that EMU-guided ASM withdrawal could be a helpful tool to support clinical decision-making and improve patient safety. Prospective, randomized trials should further evaluate this method in the future.

ROS/Electro Dual-Reactive Nanogel for Targeting Epileptic Foci to Remodel Aberrant Circuits and Inflammatory Microenvironment

Medicinal treatment against epilepsy is faced with intractable problems, especially epileptogenesis that cannot be blocked by clinical antiepileptic drugs (AEDs) during the latency of epilepsy. Abnormal circuits of neurons interact with the inflammatory microenvironment of glial cells in epileptic foci, resulting in recurrent seizures and refractory epilepsy. Herein, we have selected phenytoin (PHT) as a model drug to derive a ROS-responsive and consuming prodrug, which is combined with an electro-responsive group (sulfonate sodium, SS) and an epileptic focus-recognizing group (α-methyl-l-tryptophan, AMT) to form hydrogel nanoparticles (i.e., a nanogel). The nanogel will target epileptic foci, release PHT in response to a high concentration of reactive oxygen species (ROS) in the microenvironment, and inhibit overexcited circuits. Meanwhile, with the clearance of ROS, the nanogel can also reduce oxidative stress and alleviate microenvironment inflammation. Thus, a synergistic regulation of epileptic lesions will be achieved. Our nanogel is expected to provide a more comprehensive strategy for antiepileptic treatment.

Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models

The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, they suffer from major shortcomings. First, long-term EEG is usually highly contaminated with artefacts. Second, changes in the EEG signal over long intervals, known as concept drift, are often neglected. We evaluate the influence of these problems on deep neural networks using EEG time series and on shallow neural networks using widely-used EEG features. Our patient-specific prediction models were tested in 1577 hours of continuous EEG, containing 91 seizures from 41 patients with temporal lobe epilepsy who were undergoing pre-surgical monitoring. Our results showed that cleaning EEG data, using a previously developed artefact removal method based on deep convolutional neural networks, improved prediction performance. We also found that retraining the models over time reduced false predictions. Furthermore, the results show that although deep neural networks processing EEG time series are less susceptible to false alarms, they may need more data to surpass feature-based methods. These findings highlight the importance of robust data denoising and periodic adaptation of seizure prediction models.

Should antiseizure medications be withdrawn after an extended period of seizure freedom in individuals with adult-onset epilepsy?

Unlike several epilepsies with onset in pediatric age, adult-onset epilepsies do not typically have a time course that is predictably self-remitting in the large majority of people. Still, about one-half of individuals with adult-onset epilepsy who have been seizure-free for an extended period (two years or longer) on antiseizure medications (ASMs) will remain in remission when their drug therapy is discontinued. Although a number of predictors of outcome have been identified (including specific adult-onset syndromes associated with a low probability of spontaneous remission), in most cases, the only way to establish whether the epilepsy has remitted in a given individual is to gradually withdraw ASMs. ASM withdrawal can be beneficial, particularly when the currently used treatment is not well tolerated, or could lead to adverse outcomes in the future (i.e., teratogenic effects should pregnancy occur in a female of childbearing potential). However, the risks associated with ASM withdrawal are significant. Relapse of seizures can have major adverse psychosocial consequences and also may carry a risk of morbidity and mortality. Most importantly, evidence suggests that in about 20% of individuals whose seizure relapsed following ASM withdrawal, re-institution of pharmacological therapy may not readily restore seizure control. Ultimately, management decisions should prioritize the preference of the well-informed person with epilepsy. Particularly, when adverse drug effects are a concern, options to be discussed should include not only withdrawal or continuation of the current treatment but also dose reduction or substitution with a different ASM.

Withdrawal of antiseizure medications - for whom, when, and how?

INTRODUCTION

Epilepsy is a chronic disorder of the brain characterized by an enduring predisposition to generate epileptic seizures. Most patients can achieve complete seizure control (seizure freedom) with antiseizure medications (ASMs). In some of them, the withdrawal of ASMs can be considered. Guidance is required to identify patients in whom drug discontinuation can be safely attempted and to inform when and how ASM withdrawal can be done.

AREAS COVERED

In this perspective, the authors discuss the evidence on ASM withdrawal in epilepsy patients who are seizure-free and provide some suggestions on how to do it effectively in clinical practice, minimizing the risk of seizure recurrence.

EXPERT OPINION

The decision of discontinuing ASMs in epilepsy patients should rely on an accurate estimate of seizure recurrence risk. Whenever possible, such a risk should be assessed on an individual basis. The decision should also consider the psychosocial and personal consequences of seizure relapse. No robust evidence is available on the safest tapering regimen.

Antiseizure Medication Withdrawal Practice Patterns: A Survey Among Members of the American Academy of Neurology and EpiCARE

Background and Objectives

To describe neurologist practice patterns, challenges, and decision support needs pertaining to withdrawal of antiseizure medications (ASMs) in patients with well-controlled epilepsy.

Methods

We sent an electronic survey to (1) US and (2) European physician members of the American Academy of Neurology and (3) members of EpiCARE, a European Reference Network for rare and complex epilepsies. Analyses included frequencies and percentages, and we showed distributions through histograms and violin plots.

Results

We sent the survey to 4,923 individuals; 463 consented, 411 passed eligibility questions, and 287 responded to at least 1 of these questions. Most respondents indicated that they might ever consider ASM withdrawal, with respondents treating mostly children being more likely ever to consider withdrawal (e.g., medical monotherapy: children 96% vs adults 81%; p < 0.05). The most important factors when making decisions included seizure probability (83%), consequences of seizures (73%), and driving (74%). The top challenges when making decisions included unclear seizure probability (81%), inadequate guidelines (50%), and difficulty communicating probabilities (45%). Respondents would consider withdrawal after a median of 2-year seizure freedom, but also responded that they would begin withdrawal on average only when the postwithdrawal seizure relapse risk in the coming 2 years was less than 15%-30%. Wide variation existed in the use of words or numbers in respondents' counsel methods, for example, percentages vs frequencies or probability of seizure freedom vs seizure. The most highly rated point-of-care methods to inform providers of calculated risk were Kaplan-Meier curves and showing percentages only, rather than pictographs or text recommendations alone.

Discussion

Most surveyed neurologists would consider withdrawing ASMs in seizure-free individuals. Seizure probability was the largest factor driving decisions, yet estimating seizure probabilities was the greatest challenge. Respondents on average indicated that they may withdraw ASM after a minimum seizure-free duration of 2 years, yet also on average were willing to withdraw when seizure risk decreased below 15%-30%, which is lower than most patients' postwithdrawal risk at 2-year seizure freedom and lower than the equivalent even of a first seizure of life. These findings will inform future efforts at developing decision support tools aimed at optimizing ASM withdrawal decisions.

Frequency of and factors associated with antiseizure medication discontinuation discussions and decisions in patients with epilepsy: A multicenter retrospective chart review

OBJECTIVE

Guidelines suggest considering antiseizure medication (ASM) discontinuation in patients with epilepsy who become seizure-free. Little is known about how discontinuation decisions are being made in practice. We measured the frequency of, and factors associated with, discussions and decisions surrounding ASM discontinuation.

METHODS

We performed a multicenter retrospective cohort study at the University of Michigan (UM) and two Dutch centers: Wilhelmina Children's Hospital (WCH) and Stichting Epilepsie Instellingen Nederland (SEIN). We screened all children and adults with outpatient epilepsy visits in January 2015 and included those with at least one visit during the subsequent 2 years where they were seizure-free for at least one year. We recorded whether charts documented (1) a discussion with the patient about possible ASM discontinuation and (2) any planned attempt to discontinue at least one ASM. We conducted multilevel logistic regressions to determine factors associated with each outcome.

RESULTS

We included 1058 visits from 463 patients. Of all patients who were seizure-free at least one year, 248/463 (53%) had documentation of any discussion and 98/463 (21%) planned to discontinue at least one ASM. Corresponding frequencies for patients who were seizure-free at least 2 years were 184/285 (65%) and 74/285 (26%). The probability of discussing or discontinuing increased with longer duration of seizure freedom. Still, even for patients who were 10 years seizure-free, our models predicated that in only 49% of visits was a discontinuation discussion documented, and in only 16% of visits was it decided to discontinue all ASMs. Provider-to-provider variation explained 18% of variation in whether patients discontinued any ASM.

SIGNIFICANCE

Only approximately half of patients with prolonged seizure freedom had a documented discussion about ASM discontinuation. Discontinuation was fairly rare even among low-risk patients. Future work should further explore barriers to and facilitators of counseling and discontinuation attempts.

Risk factors for seizure recurrence after initial withdrawal of anti-seizure medications in children with epilepsy at Aga Khan University Hospital, Nairobi, Kenya

Objectives

We sought to determine risk factors associated with seizure recurrence following initial withdrawal of anti-seizure medications (ASM) among children with epilepsy.

Methods

This was a retrospective observational study of children aged between 2 and 18 years with a diagnosis of epilepsy who underwent withdrawal of anti-seizure medication following remission of seizures. All eligible medical records between January 2011 and December 2019 were included. Demographic, clinical, imaging and electroencephalography data of all eligible patients were analyzed against seizure remission within 24 months after withdrawal of ASM, using appropriate parametric and non-parametric tests.

Results

A total of 49 records of children who underwent withdrawal of ASM out of a total of 613 patients on follow up during the same period were included. The median age at ASM withdrawal was 70 months (IQR 52-112 months) and 14 (28.6%) were female. Thirteen patients (26.5%) had seizure recurrence within 24 months following withdrawal of ASM. Focal onset seizure type was associated with significant risk of seizure recurrence (OR 13.7; 95% CI 0.97, 193.54; P value = 0.011). Age at epilepsy diagnosis, abnormal EEG at initiation of treatment and at the time of de-escalation, abnormal MRI findings, first or second degree relative with epilepsy, history of developmental delay, seizure burden, use of 2 or more ASMs and duration of seizure-freedom before de-escalation of ASM were not associated with increased risk of relapse.

Conclusion

Focal onset seizure type is associated with increased with risk of seizure recurrence in this cohort.

Serum Proteins Associated with Blood-Brain Barrier as Potential Biomarkers for Seizure Prediction

As 30% of epileptic patients remain drug-resistant, seizure prediction is vital. Induction of epileptic seizure is a complex process that can depend on factors such as intrinsic neuronal excitability, changes in extracellular ion concentration, glial cell activity, presence of inflammation and activation of the blood−brain barrier (BBB). In this study, we aimed to assess if levels of serum proteins associated with BBB can predict seizures. Serum levels of MMP-9, MMP-2, TIMP-1, TIMP-2, S100B, CCL-2, ICAM-1, P-selectin, and TSP-2 were examined in a group of 49 patients with epilepsy who were seizure-free for a minimum of seven days and measured by ELISA. The examination was repeated after 12 months. An extensive medical history was taken, and patients were subjected to a follow-up, including a detailed history of seizures. Serum levels of MMP-2, MMP-9, TIMP-1, CCL-2, and P-selectin differed between the two time points (p < 0.0001, p < 0.0001, p < 0.0001, p < 0.0001, p = 0.0035, respectively). General linear model analyses determined the predictors of seizures. Levels of MMP-2, MMP-9, and CCL-2 were found to influence seizure count in 1, 3, 6, and 12 months of observation. Serum levels of MMP-2, MMP-9, and CCL-2 may be considered potential biomarkers for seizure prediction and may indicate BBB activation.

Risk of seizure relapse and long-term outcomes after discontinuation of antiseizure medication in children with epilepsy

OBJECTIVES

The aims of this study were to evaluate the demographic and clinical characteristics of children with epilepsy who discontinued their antiseizure medication (ASM), to determine potential predictors of seizure relapse, to calculate the rate of seizure relapse, and to detect long-term seizure outcomes.

METHODS

A total of 269 seizure-free children with epilepsy who were decided to discontinue their ASM and were followed up for at least 18 months after ASM withdrawal were retrospectively evaluated.

RESULTS

The enrolled children had been followed up for a median of 46 months (range 18-126 months; IQR: 29-61) after ASM withdrawal and 90 (33.5%) of their seizures relapsed. The median time to seizure relapse was 8 months (range 0.23-117 months; IQR: 2-25). Seizure relapse occurred in 16.7% of the 90 children at 1 month, 45.6% at 6 months, 62.2% at 1 year, 74.4% at 2 years, and 94.4% at 5 years. Univariate logistic regression analyses revealed six predictors significantly related to relapse: age at first seizure, age at diagnosis of epilepsy, intellectual disability, EEG findings after ASM withdrawal, ASM tapering time, and number of seizures on ASM. In multivariate logistic regression analyses, age at first seizure, intellectual disability, and ASM tapering time were not significantly associated anymore. The other three remained independently predictive. Pharmacological control of seizures with monotherapy was restored in 93.3% of the children with seizure relapse.

CONCLUSION

This study evaluated potential predictors of seizure relapse, some of which have rarely been evaluated in previous studies. Adolescent age at diagnosis, abnormal EEG findings after ASM withdrawal, and high number of seizures on ASM were associated with a higher risk of seizure relapse. Abnormal MRI findings such as malformations of cortical development and hydrocephalus may be potential biomarkers for the risk of seizure relapse.

Incidence of and predictors for antiseizure medication gaps in Medicare beneficiaries with epilepsy: a retrospective cohort study

BACKGROUND

For the two-thirds of patients with epilepsy who achieve seizure remission on antiseizure medications (ASMs), patients and clinicians must weigh the pros and cons of long-term ASM treatment. However, little work has evaluated how often ASM discontinuation occurs in practice. We describe the incidence of and predictors for sustained ASM fill gaps to measure discontinuation in individuals potentially eligible for ASM withdrawal.

METHODS

This was a retrospective cohort of Medicare beneficiaries. We included patients with epilepsy by requiring International Classification of Diseases codes for epilepsy/convulsions plus at least one ASM prescription each year 2014-2016, and no acute visit for epilepsy 2014-2015 (i.e., potentially eligible for ASM discontinuation). The main outcome was the first day of a gap in ASM supply (30, 90, 180, or 360 days with no pills) in 2016-2018. We displayed cumulative incidence functions and identified predictors using Cox regressions.

RESULTS

Among 21,819 beneficiaries, 5191 (24%) had a 30-day gap, 1753 (8%) had a 90-day gap, 803 (4%) had a 180-day gap, and 381 (2%) had a 360-day gap. Predictors increasing the chance of a 180-day gap included number of unique medications in 2015 (hazard ratio [HR] 1.03 per medication, 95% confidence interval [CI] 1.01-1.05) and epileptologist prescribing physician (≥25% of that physician's visits for epilepsy; HR 2.37, 95% CI 1.39-4.03). Predictors decreasing the chance of a 180-day gap included Medicaid dual eligibility (HR 0.75, 95% CI 0.60-0.95), number of unique ASMs in 2015 (e.g., 2 versus 1: HR 0.37, 95% CI 0.30-0.45), and greater baseline adherence (> 80% versus ≤80% of days in 2015 with ASM pill supply: HR 0.38, 95% CI 0.32-0.44).

CONCLUSIONS

Sustained ASM gaps were rarer than current guidelines may suggest. Future work should further explore barriers and enablers of ASM discontinuation to understand the optimal discontinuation rate.

Caring for women with epilepsy in Palestine: A qualitative study of the current status

OBJECTIVE

This qualitative study was conducted to explore the current status of caring for women with epilepsy (WWE) in the Palestinian healthcare system.

METHODS

This study used an explorative qualitative design. A purposive sampling technique was used to recruit the participants. Semi-structured in-depth interviews were conducted with neurologists (n = 6), gynecologists (n = 5), psychiatrists (n = 3), an internist (n = 1), and clinical pharmacists (n = 5). The interpretive description methodology was used to thematically analyze the qualitative data.

RESULTS

A total of 745 min (12.4 h) of interview time were analyzed. The qualitative data collected in this study were categorized under 3 major themes and multiple subthemes. The 3 major themes were: (1) diagnosis and care for patients with epilepsy, (2) general issues in caring for patients with epilepsy, and (3) consideration of women's issues in the pharmacotherapy of epilepsy. Formally adopted protocols/criteria for the diagnosis and care for WWE were lacking.

CONCLUSION

Findings of this qualitative study showed a need to formally adopt uniform guidelines that can guide the diagnosis and care of WWE in the Palestinian healthcare system. The findings of this study might be informative to healthcare providers, decision-makers in healthcare authorities, WWE, and patient advocacy groups who could be interested in improving and benchmarking healthcare services provided to WWE. Future studies are still needed to quantitatively measure adherence to the international guidelines in caring for WWE.

Reappraisal of the Medical Research Council Antiepileptic Drug Withdrawal Study: contamination‐adjusted and dose‐response re‐analysis

Objective

The 1991 Medical Research Council (MRC) Study compared seizure relapse for seizure‐free patients randomized to withdraw vs continue of antiseizure medications (ASMs). We re‐analyzed this trial to account for crossover between arms using contamination‐adjusted intention to treat (CA ITT) methods, to explore dose‐response curves, and to validate predictions against external data. ITT assesses the effect of being randomized to withdraw, as‐treated analysis assesses the confounded effect of withdrawing, but CA ITT assesses the unconfounded effect of actually withdrawing.

Methods

CA ITT involves two stages. First, we used randomized arm to predict whether patients withdrew their ASM (logistic) or total daily ASM dose (linear). Second, we used those values to predict seizure occurrence (logistic).

Results

The trial randomized 503 patients to withdraw and 501 patients to continue ASMs. We found that 316 of 376 patients (88%) who were randomized to withdraw decreased their dose at every pre‐seizure visit, compared with 35 of 424 (8%) who were randomized to continue (p < .01). Adjusted odds ratios of a 2‐year seizure for those who withdrew vs those who did not was 1.3 (95% confidence interval [CI] 0.9–1.9) in the as‐treated analysis, 2.5 (95% CI 1.9–3.4) comparing those randomized to withdraw vs continue for ITT, and 3.1 (95% CI 2.1–4.5) for CA ITT. Probabilities (withdrawal vs continue) were 28% vs 24% (as‐treated), 40% vs 22% (ITT), and 43% vs 21% (CA ITT). Differences between ITT and CA ITT were greater when varying the predictor (reaching zero ASMs) or outcome (1‐year seizures). As‐treated dose‐response curves demonstrated little to no effects, but larger effects in CA ITT analysis. MRC data overpredicted risk in Lossius data, with moderate discrimination (areas under the curve ~0.70).

Significance

CA ITT results (the effect of actually withdrawing ASMs on seizures) were slightly greater than ITT effects (the effect of recommend withdrawing ASMs on seizures). How these findings affect clinical practice must be individualized.

Recurrence rates and risk factors for seizure recurrence following antiseizure medication withdrawal in adolescent patients with genetic generalized epilepsy

Objective

This study aimed to identify the recurrence rate of genetic generalized epilepsy (GGE) and risk factors for recurrence after antiseizure medication (ASM) withdrawal in adolescent patients.

Methods

We retrospectively reviewed medical records of patients with GGE who were included in the registry at the Department of Child Neurology, National Hospital Organization Nishiniigata Chuo Hospital from 2000 through 2020. The eligibility criteria were as follows: onset of epileptic seizures at <15 years of age, treatment with an ASM, and attempted treatment withdrawal at 10‐19 years of age. The rates of seizure recurrence after drug withdrawal were evaluated. Moreover, several variables were evaluated as predictors of recurrence.

Results

In total, 77 patients with GGE (21, 13, and 43 patients with juvenile myoclonic epilepsy [JME], juvenile absence epilepsy [JAE], and epilepsy with generalized tonic–clonic seizures alone [EGTCSA], respectively) were included in this study. Recurrence was detected in 68% of patients with GGE (86%, 31%, and 70% of patients with JME, JAE, and EGTCSA, respectively). Recurrence rates for patients who developed epilepsy at ≥13 years of age, those who started dose reduction at ≥16 years of age, those who exhibited a seizure‐free period of <36 months before withdrawal, and those who chose to discontinue treatment at their own discretion were significantly higher than those for their counterparts. Multivariate analysis revealed that initiation of dose reduction at ≥16 years of age was associated with increased recurrence risk. Meanwhile, a diagnosis of JAE was associated with decreased recurrence risk. All patients with JAE were treated with valproic acid.

Significance

Antiseizure medication withdrawal at ≥16 years of age and a diagnosis other than JAE may be independent risk factors for seizure recurrence after drug withdrawal in adolescent patients.

Scalp HFO rates decrease after successful epilepsy surgery and are not impacted by the skull defect resulting from craniotomy

Epilepsy surgery can achieve seizure freedom in selected pediatric candidates, but reliable postsurgical predictors of seizure freedom are missing. High frequency oscillations (HFO) in scalp EEG are a new and promising biomarker of treatment response. However, it is unclear if the skull defect resulting from craniotomy interferes with HFO detection in postsurgical recordings. We considered 14 children with focal lesional epilepsy who underwent presurgical evaluation, epilepsy surgery, and postsurgical follow-up of ≥ 1 year. We identified the nearest EEG electrodes to the skull defect in the postsurgical MRI. We applied a previously validated automated HFO detector to determine HFO rates in presurgical and postsurgical EEG. Overall, HFO rates showed a positive correlation with seizure frequency (p < 0.001). HFO rates in channels over the HFO area decreased following successful epilepsy surgery, irrespective of their proximity to the skull defect (p = 0.005). HFO rates in channels outside the HFO area but near the skull defect showed no increase following surgery (p = 0.091) and did not differ from their contralateral channels (p = 0.726). Our observations show that the skull defect does not interfere with postsurgical HFO detection. This supports the notion that scalp HFO can predict postsurgical seizure freedom and thus guide therapy management in focal lesional epilepsy.