Drug taper during long-term video-EEG monitoring: efficiency and safety

Impact of antiseizure medication with a very long half-life on long term video-EEG monitoring in focal epilepsy

PURPOSE

To assess the impact of antiseizure medications (ASMs) with a very long half-life on long term video-EEG monitoring (LTM) in people with focal epilepsy (FE).

METHODS

In this retrospective cohort study, we searched our local database for people with FE who underwent ASM reduction during LTM at the University Hospital of 'La Fe', Valencia, from January 2013 to December 2019. Taking into account the half-life of the ASM, people with FE were divided into two groups: Group A contained individuals who were taking at least one ASM with a very long half-life at admission, and Group B consisted of those not taking very long half-life ASMs. Using multivariable analysis to control for important confounders, we compared the following outcomes between both groups: seizure rates per day, time to first seizure, and LTM duration.

RESULTS

Three hundred seventy individuals were included in the study (154 in Group A and 216 in Group B). The median recorded seizure rates (1.3 seizures/day, range 0-15.3 vs.1.3 seizures/day, range 0-9.3, p-value=0.68), median time to the first seizure (24 h, range 2-119 vs. 24 h, range 2-100, p-value=0.92), and median LTM duration (4 days, range 2-5 vs. 4 days, range 2-5, p-value=0.94) were similar in both groups. Multivariable analysis did not reveal any significant differences in the three outcomes between the two groups (all p-values>0.05).

CONCLUSION

ASMs with a very long half-life taken as co-medication do not significantly affect important LTM outcomes, including recorded seizure rates, time to the first seizure, or LTM duration. Therefore, in general, there is no need to discontinue ASMs with a very long half-life prior to LTM.

Predicting seizure clustering in the epilepsy monitoring unit: A multivariable analysis

INTRODUCTION

Seizure clustering, is the most frequently reported adverse event in epilepsy monitoring unit (EMU) safety studies which, can also potentiate other adverse events, such as falls, status epilepticus, and increased length of stay. The purpose of this study is to determine variables associated with increased risk of seizure clustering among patients admitted to the EMU.

METHODS

A retrospective review of patients admitted to the EMU over a two-year period was completed. Data collected included patient demographics, types of epilepsy, seizure frequency, anti-seizure medications (ASMs) and hospital and EMU course including incidence of seizure clustering.

RESULTS

Two hundred seven patients were included in our study; of these, ninety patients experienced two or more seizures in a 24-hour period (24SC), and 68 patients experienced two or more seizures in a 4-hour period (4SC). Logistic regression analysis associated the absence of long-acting ASM with increased clustering within the 4SC group (p = 0.038). For every additional ASM taken by a patient at home, the odds of seizure clustering increased by 81% in the 4SC group (p = 0.009) and by 61% in the 24SC group (p = 0.022). In addition, patients with a diagnosis of temporal lobe epilepsy had some association with clustering in the 24SC group (p = 0.061).

CONCLUSION

Our data showed that long-acting ASMs can be protective against seizure clustering. Furthermore, patients with temporal lobe epilepsy, and those on increased numbers of ASMs, were more likely to experience seizure clustering when undergoing medication withdrawal during an EMU evaluation.

Safety and efficacy of rapid withdrawal of anti-seizure medication during long-term video-EEG monitoring

Objective

Anti-seizure medications (ASMs) are often withdrawn during long-term video-EEG monitoring (LTM) to allow pre-surgical evaluation. Herein, we evaluated the safety and efficacy of ultra-rapid withdrawal (URW) and rapid withdrawal (RW) of ASMs in an epilepsy monitoring unit (EMU).

Methods

This retrospective study examined all consecutive patients admitted to our EMU between May 2021 and October 2022. Patients were classified into the URW and RW groups according to the way ASMs were withdrawn. We compared the efficacy and safety of the procedures used in the groups in terms of duration of LTM, latency to the first seizure, and incidence of focal to bilateral tonic-clonic seizures (FBTCS), seizure clusters (SC), and status epilepticus (SE).

Results

Overall, 110 patients (38 women) were included. The mean age of patients at the time of LTM was 29 years. All medications were stopped on admission for monitoring in the URW group (n = 75), while in the RW group (n = 35) ASMs were withdrawn within 1 day. In both groups, the duration of LTM was approximately 3 days: URW group (2.9 ± 0.5 days) and RW group (3.1 ± 0.8 days). The latency to the first seizure was significantly different between the two groups; however, there were no differences between the two groups in terms of the distribution of FBTCS, SC, or SE, number of seizures, and the requirement for intravenous rescue medication was low.

Conclusion

The rapid withdrawal of ASMs to provoke seizures during monitoring for pre-surgical evaluation following the URW protocol was as effective and safe as with RW. Ultra-rapid ASM withdrawal has the benefits of reducing LTM duration and shortening the time to first seizure compared to rapid medication tapering.

A pharmacokinetic model of antiseizure medication load to guide care in the epilepsy monitoring unit

OBJECTIVE

Evaluating patients with drug-resistant epilepsy often requires inducing seizures by tapering antiseizure medications (ASMs) in the epilepsy monitoring unit (EMU). The relationship between ASM taper strategy, seizure timing, and severity remains unclear. In this study, we developed and validated a pharmacokinetic model of total ASM load and tested its association with seizure occurrence and severity in the EMU.

METHODS

We studied 80 patients who underwent intracranial electroencephalographic recording for epilepsy surgery planning. We developed a first order pharmacokinetic model of the ASMs administered in the EMU to generate a continuous metric of overall ASM load. We then related modeled ASM load to seizure likelihood and severity. We determined the association between the rate of ASM load reduction, the length of hospital stay, and the probability of having a severe seizure. Finally, we used modeled ASM load to predict oncoming seizures.

RESULTS

Seizures occurred in the bottom 50th percentile of sampled ASM loads across the cohort (p < .0001, Wilcoxon signed-rank test), and seizures requiring rescue therapy occurred at lower ASM loads than seizures that did not require rescue therapy (logistic regression mixed effects model, odds ratio = .27, p = .01). Greater ASM decrease early in the EMU was not associated with an increased likelihood of having a severe seizure, nor with a shorter length of stay.

SIGNIFICANCE

A pharmacokinetic model can accurately estimate ASM levels for patients in the EMU. Lower modeled ASM levels are associated with increased seizure likelihood and seizure severity. We show that ASM load, rather than ASM taper speed, is associated with severe seizures. ASM modeling has the potential to help optimize taper strategy to minimize severe seizures while maximizing diagnostic yield.

Reliability of patient self-report of cognition, awareness, and consciousness during seizures

Objective

Clinicians rely on patient self‐report of impairment during seizures for decisions including driving eligibility. However, the reliability of patient reports on cognitive and behavioral functions during seizures remains unknown.

Methods

We administered a daily questionnaire to epilepsy patients undergoing continuous video‐EEG monitoring, asking about responsiveness, speech, memory, awareness, and consciousness during seizures in the preceding 24 hours. We also administered a questionnaire upon admission about responsiveness, speech, and awareness during seizures. Subjective questionnaire answers were compared with objective behavioral ratings on video review. Criteria for agreement were Cohen’s kappa >0.60 and proportions of positive and negative agreement both >0.75.

Results

We analyzed 86 epileptic seizures in 39 patients. Memory report on the daily questionnaire met criteria for agreement with video review (κ = 0.674 for early, 0.743 for late recall). Subjective report of awareness also met agreement criteria with video ratings of memory (κ = 0.673 early, 0.774 late). Concordance for speech was relatively good (κ = 0.679) but did not meet agreement criteria, nor did responsiveness or consciousness. On the admission questionnaire, agreement criteria were met for subjective report of awareness versus video ratings of memory (κ = 0.814 early, 0.806 late), but not for other comparisons.

Interpretation

Patient self‐report of memory or awareness showed the best concordance with objective memory impairment during seizures. Self‐report of impairment in other categories was less reliable. These findings suggest that patient reports about impaired memory during seizures may be most reliable, and otherwise determining functional impairments should be based on objective observations.

Minimum standards for inpatient long-term video-EEG monitoring: A clinical practice guideline of the international league against epilepsy and international federation of clinical neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility.

Minimum standards for inpatient long-term video-electroencephalographic monitoring: A clinical practice guideline of the International League Against Epilepsy and International Federation of Clinical Neurophysiology

The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events. For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and to establish its clinical utility.

Risk of seizure clusters and status epilepticus following rapid and ultra-rapid medication discontinuation during intracranial EEG monitoring

OBJECTIVE

Anti-seizure medications (ASMs) are discontinued in the course of intracranial EEG (iEEG) monitoring for presurgical evaluation. The ASM withdrawal facilitates an emergence of seizures but may also precipitate seizure clusters (SC) and status epilepticus (SE). The aim of this study was to compare the rates of SC and SE during the ultra-rapid withdrawal (URW) and rapid withdrawal (RW) of ASMs during iEEG.

METHODS

We performed a retrospective observational study of all consecutive patients with drug resistant epilepsy who completed iEEG at our comprehensive epilepsy center from 2012-2018. SC was defined as three or more seizures in 24 h with a return to baseline between the events. SE was defined as ≥ 5 min of clinical seizure or ≥ 10 min of ictal electrographic activity or series of seizures with no return to the neurological baseline between the events.

RESULTS

Of 107 patients who completed iEEG with intracranial grid or strip electrodes, 46 (43%) were male. Median age at the time of iEEG was 35.4 years (interquartile range [IQR], 26.4 - 44.9). Ninety patients (84.1%) had all AEDs held on admission, while 16 patients (15%) underwent a rapid taper. The median time to first seizure was 15.1 (8.2 - 22.6) h. Sixty-two patients (57.9%) developed SC, while 10 (9.4%) developed SE. Twenty-six patients (36.1%) with these complications required intravenous lorazepam or other rescue ASMs, while the remaining patients had spontaneous resolution of seizures; intubations were not required. While there were differences in the proportions in patients who experienced SC, SE, or neither in the URW and RW groups, these differences were not significant at the 0.05 alpha level.

SIGNIFICANCE

Ultra-rapid and rapid ASM withdrawal are accompanied by SC and SE the majority of which terminate spontaneously. These data support the use of either approach of the medication taper for seizure provocation in iEEG.

Benefits, safety and outcomes of long-term video EEG monitoring in pediatric patients

OBJECTIVE

To investigate benefits of in-hospital, long-term video EEG monitoring (LVEM) for pediatric patients, from a therapeutic perspective and from the perspectives of patients and their families.

METHODS

A monocentric retrospective cohort study was conducted. Patients aged 0-18 years who underwent LVEM for epilepsy surgery eligibility, epilepsy syndrome clarification, or medication adjustment were evaluated regarding paroxysmal event type, change in seizure frequency and patients' benefits using a standardized evaluation protocol.

RESULTS

A total of 163 (88 boys and 75 girls, mean age 10.9 years) pediatric patients underwent 178 LVEM sessions, with a mean duration of 5.4 days. The rate of habitual event detection was 69.1%. Epilepsy diagnosis was confirmed in 147 patients and excluded in 16 patients (9.8%). LVEM results altered the diagnosis of 37.4% of patients. Diagnosis remained unchanged in 49.1% of patients and was specified in 13.5% of patients. Epilepsy surgery was performed in 32 patients, and 64% of epilepsy patients deemed ineligible for epilepsy surgery underwent medication adjustments. Patients or their families found LVEM helpful in 75% of cases. Significant seizure reductions and improvements in the disease course were reported by 45% of epilepsy patients. Three episodes of non-convulsive status epilepticus occurred, representing 1.7% of admissions and 1.9% of patients diagnosed with epilepsy, while no injuries were observed.

CONCLUSIONS

LVEM is beneficial for pediatric patients from both a medical perspective and from the perspective of patients and their families, even if patients are ineligible for epilepsy surgery. LVEM is well-tolerated with a low risk of status epilepticus and injuries.

Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning

STUDY OBJECTIVES

Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings.

METHODS

Using a clinical dataset of polysomnograms from 6,431 patients (MGH-PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm's inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH-PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24-72 h) scalp EEG recordings from 112 patients (scalpEEG dataset).

RESULTS

The algorithm achieved a Cohen's kappa of 0.74 on the MGH-PSG holdout testing set and cross-validated Cohen's kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen's kappa ~ 0.75 ± 0.11). The algorithm's performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities.

CONCLUSION

We developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.

Antiepileptic drug withdrawal and seizure severity in the epilepsy monitoring unit

OBJECTIVE

The goal of this study was to identify a strategy for antiepileptic drug (AED) reduction to allow efficient recording of focal seizures (FS) in patients undergoing video-electroencephalography (EEG) in an epilepsy monitoring unit (EMU) while avoiding the risk of complications associated with more severe seizure types.

METHODS

We retrospectively reviewed consecutive patients admitted to our institution's EMU from July 1, 2016 to December 31, 2017. We included 114 presurgical patients who had AEDs reduced and at least one seizure during the admission. We compared AED dosages at which FS versus focal to bilateral tonic-clonic seizures (f-BTCS), seizure clusters, and lorazepam administration occurred. We also examined rate of AED reduction and seizure types. We used a receiver-operating characteristic (ROC) curve to identify a dose maximizing FS and minimizing other seizure types.

RESULTS

Antiepileptic drug withdrawal rates ranged from 0 to 100% in the first 24 h (mean: 20%, standard deviation: 20%). Focal to bilateral tonic-clonic seizures and lorazepam administration occurred at a lower median AED dose than did FS (0%, 7.2%, and 43.8%, respectively, expressed as a percentage of the patient's outpatient daily AED dose; p < 0.001). A daily EMU-administered dose of one-third of the patient's outpatient AED dose allowed 55.0% of FS to occur while avoiding 82.0% of more severe seizure types. The seizure types had no difference in rate of AED withdrawal in the first 24 h of EMU stay.

CONCLUSIONS

Focal seizures occurred at a higher AED dose than did f-BTCS. This may imply that a low minimally effective dose of AED could allow FS to be recorded while providing protection against f-BTCS. This strategy could improve efficacy and safety in the EMU.

Drug withdrawal in the epilepsy monitoring unit - The patsalos table

Investigation of possible candidates for epilepsy surgery will usually require inpatient EEG to capture seizures and allow full operative planning. Withdrawal of antiepileptic drugs increases the yield of this valuable diagnostic information and the benefits of this should justify any increase in the risk of harm associated with these seizures This paper outlines our opinion on what would constitute proposed best practice for management of antiepileptic drug (AED) dosing when patients are admitted for monitoring of seizures to an epilepsy monitoring unit (EMU). In the vast majority of cases EMU admissions are safe and, even if seizures occur, will pass off without complication. Previous guidance has concentrated on ensuring practice around technical aspects of EEG monitoring itself and staffing within the unit. In this guidance we aim to outline optimally safe ways of ensuring that EMUs ensure the minimisation of risk to the patients admitted under their care. We propose an algorithm for enhancing the safety of AED withdrawal in VT admissions while ensuring adequate seizure yields. Risk minimisation requires planned management of drug dosing (with reduction if appropriate), provision of adequate rescue medication, and adequate supervision to allow rapid response to generalised seizures. This algorithm is accompanied by a table which uses knowledge of the clinical and pharmacological properties of each AED to ensure dose withdrawal and reduction is timely and safe taking into account the severity and frequency of the individual's seizures.

Safety and efficiency of medication withdrawal at home prior to long-term EEG video-monitoring

PURPOSE

Long-term video-EEG monitoring (LTM) is frequently used for diagnostic purposes and in the workup of epilepsy surgery to determine the seizure onset zone. Different strategies are applied to provoke seizures during LTM, of which withdrawal of anti-epileptic drugs (AED) is most effective. Remarkably, there is no standardized manner of AED withdrawal. For instance, the majority of clinics taper medication during clinical admission, whereas we prefer to taper medication at home prior to admission. Our aim was to study the advantages (efficiency and diagnostic yield) and disadvantages (safety and complication rates) of predominantly tapering of medication at home.

METHOD

We report a retrospective observational cohort of 273 patients who had a LTM at our tertiary epilepsy center from 2005 until 2011. Provocation methods to induce seizures were determined on individual basis. Success rate (duration of admittance, time to first seizure, efficiency and diagnostic yield) and complications and serious adverse events were assessed.

RESULTS

AED were tapered in 180 (66%) patients, in 93 (24%) of these patients with additional (partial) sleep deprivation. In all of these patients tapering started at home one to four weeks prior to admission. In the other patients, only (partial) sleep deprivation or none provocation method at all was applied. Seizure recordings were successful in 79,9% of patients. Complications occurred in 19 patients (10.9%) of which 3 had (1.7%) serious adverse events (status epilepticus (SE)) with AED withdrawal. These complications only occurred during admittance, not at home.

CONCLUSIONS

AED withdrawal at home prior to LTM is an efficient and convenient method to increase the diagnostic yield of LTM and appears relatively safe.