How much time is enough? Establishing an optimal duration of recording for ambulatory video EEG

Clinical findings of long-term ambulatory video EEG following routine EEG

PURPOSE

This study aims to assess the diagnostic yield of routine EEG (rEEG) followed by long-term ambulatory EEG (aEEG) in a retrospective cohort, focusing on the rates of abnormal EEG findings, and overall event capture.

METHODS

Data were retrospectively collected from deidentified clinical reports of patients who underwent both rEEG and subsequent aEEG, with both modalities including video recordings. The study included 95 patients, with demographic, clinical information, and EEG findings extracted for analysis. Statistical analyses included chi-squared proportion tests and Wilcoxon rank-sum tests to assess the influence of variables such as age, sex, referral source, and aEEG duration on outcomes. Bayes factors were calculated to evaluate the power of the statistical tests.

RESULTS

Among the 95 patients, 33 % were 16 years old or younger. The median duration of aEEG was 3.9 days. Abnormal EEG findings increased from 18 % with rEEG to 33 % with aEEG. Epileptic seizures were captured in 3 % of rEEG and 8 % of aEEG, while non-epileptic events were captured in 35 % of aEEG compared to none in rEEG. Younger age was associated with higher rates of abnormal findings, but this was not adequately powered. Females had a higher likelihood of event capture on aEEG, though this finding was also underpowered. The majority of adult and paediatric patients with a normal rEEG went on to have a normal aEEG.

CONCLUSION

Ambulatory EEG significantly improves the diagnostic yield for both epileptic and non-epileptic events compared to routine EEG, particularly in adults. This study supports the broader use of aEEG for comprehensive epilepsy evaluation and suggests further research to optimise its clinical utility.

Optimizing electroencephalogram duration for efficient detection of epileptiform abnormalities in diverse patient groups: a retrospective study

BACKGROUND

There is no standardized EEG duration guideline for detecting epileptiform abnormalities in patients, and research on this topic is scarce. This study aims to determine an optimal EEG duration for efficient detection of epileptiform abnormalities across different patient groups.

METHODS

Retrospective analysis was performed on EEG recordings and clinical data of patients with the first seizure and epilepsy. Patients were categorized based on various factors, including the interval time since the last seizure, use of anti-seizure medication (ASM), and seizure frequency. The detection ratio (DR) of epileptiform abnormalities and latency time for their discovery were calculated. Statistical analyses, including chi-square tests, logistic regression, and survival analysis were utilized to illustrate DR and latency times.

RESULTS

In whole-night EEG recordings, the DR was 37.6% for the first seizure group and 57.4% for the epilepsy group. Although the maximum latency times were 720 min in both two groups, DR in the first seizure group was distinctly decreased beyond 300 min. Significant factors influencing the DR included the use of ASM in the first seizure group (P < 0.05) and seizure frequency in the epilepsy group (P < 0.001). For epilepsy patients who experience a seizure at least once a month or undergo timely EEG recordings (within 24 h after a seizure), the DR significantly increases, and the maximum latency time is reduced to 600 min (P < 0.001). Additionally, the DR was significantly reduced after 240 min in epilepsy patients who had been seizure-free for more than one year.

CONCLUSIONS

In this retrospective study, we observed a maximum latency of 720 min for detecting epileptiform abnormalities in whole-night EEG recordings. Notably, epilepsy patients with a higher seizure frequency or timely EEG recordings demonstrated both a higher detection ratio and a shorter maximum latency time. For patients exhibiting a low detection ratio, such as those experiencing their first seizure or those with epilepsy who have been seizure-free for more than a year, a shorter EEG duration is recommended. These findings underscore the importance of implementing customized EEG strategies to meet the specific needs of different patient groups.

Economic aspects of prolonged home video-EEG monitoring: a simulation study

INTRODUCTION

Video EEG monitoring (VEM) is an important tool for characterizing clinical events suspected as seizures. It is also used for pre-surgical workups in patients with drug-resistant epilepsy (DRE). In-hospital VEM high cost, long admission waiting periods and some other inconveniences led to an interest in home VEM (HVEM). However, because antiseizure medications cannot be reduced at home, HVEM may require longer monitoring. While the economic aspect is one of the main motivations for HVEM, the cost of HVEM lasting several weeks has not been assessed.

METHODS

We modeled the cost of HVEM for 8 weeks and compared it to the cost of 1-week in-hospital VEM. Additionally, we modeled the per-patient cost for a combination of HVEM and in-hospital VEM, considering that if in a proportion of patients HVEM fails to achieve its goal, they should undergo in-hospital VEM with drug reduction.

RESULTS

The average cost of HVEM up to 4-6 weeks of monitoring was lower than that for the 1-week in-hospital VEM. Combining the 3-week HVEM with 1-week in-hospital VEM (if needed) reduced the per-patient cost by 6.6-28.6% as compared to the situation when all the patients with DRE were referred to the in-hospital VEM.

CONCLUSIONS

A prolonged intermittent HVEM can be cost-effective, especially if the minimal seizure frequency is about one seizure per week. The study findings support directing efforts into clinical trials and technology development.

Over- and underreporting of seizures: How big is the problem?

OBJECTIVE

Clinical decisions on managing epilepsy patients rely on patient accuracy regarding seizure reporting. Studies have noted disparities between patient-reported seizures and electroencephalographic (EEG) findings during video-EEG monitoring periods, chiefly highlighting underreporting of seizures, a well-recognized phenomenon. However, seizure overreporting is a significant problem discussed within the literature, although not in such a large cohort. Our aim is to quantify the over- and underreporting of seizures in a large cohort of ambulatory EEG patients.

METHODS

We performed a retrospective data analysis on 3407 patients referred to a diagnostic service for ambulatory video-EEG between 2020 and 2022. Both patient-reported events and events discovered on review of the video-EEG were analyzed and classified as epileptic, psychogenic (typically clinical motor events, without accompanying EEG change), or noncorrelated events (NCEs; without perceivable clinical or EEG change). Events were analyzed by state of arousal and indication for referral. Subgroup analysis was performed in patients with focal and generalized epilepsies.

RESULTS

A total of 21 024 events were recorded by 3407 patients. Fifty-eight percent of reported events were NCEs, whereas 27% of all events were epileptic. Sixty-four percent of epileptic seizures were not reported by the patient but discovered by the clinical service on review of the recording. NCEs were in the highest proportion in the awake and drowsy arousal states and were the most common event type for the majority of referral indications. Subgroup analysis found a significantly higher proportion of NCEs in the patients with focal epilepsy (23%) compared to generalized epilepsy (10%; p < .001, chi-squared proportion test).

SIGNIFICANCE

Our results reaffirm the phenomenon of underreporting and highlight the prevalence of overreporting. Overreporting likely represents irrelevant symptoms or electrographic discharges not represented on scalp electrodes, identification of which has important clinical relevance. Future studies should analyze events by risk factors to elucidate relationships clinicians can use and investigate the etiology of NCEs.

Diagnostic utility of prolonged ambulatory video-electroencephalography monitoring

OBJECTIVES

Ambulatory video-electroencephalography (video-EEG) represents a low-cost, convenient and accessible alternative to inpatient video-EEG monitoring, however few studies have examined their diagnostic yield. In this large-scale retrospective study conducted in Australia, we evaluated the efficacy of prolonged ambulatory video-EEG recordings in capturing diagnostic events and resolving the referring question.

METHODS

Sequential adult and paediatric ambulatory video-EEG reports from April 2020 to June 2021 were reviewed retrospectively. Data collection included patient demographics, clinical information, and details of events and EEG abnormalities. Clinical utility was assessed by examining i) time to first diagnostic event, and ii) ability to resolve the referring questions - seizure localisation, quantification, classification, and differentiation (differentiating seizures from non-epileptic events).

RESULTS

Of the 600 reports analysed, 49 % captured at least one event, and 45 % captured interictal abnormalities (epileptiform or non-epileptiform). Seizures, probable psychogenic events (mostly non-convulsive), and other non-epileptic events occurred in 13 %, 23 % and 21 % of recordings respectively, with overlap. Unreported events were captured in 53 (9 %) recordings, and unreported seizures represented more than half of all seizures captured (51 %, 392/773). Nine percent of events were missing clinical, video or electrographic data. A diagnostic event occurred in 244 (41 %) recordings, of which 14 % were captured between the fifth and eighth day of recording. Reported event frequency ≥ 1/week was the only significant predictor of diagnostic event capture. In recordings with both seizures and psychogenic events, unrecognized seizures were frequent, and seizures may be missed if recording is terminated early. The referring question was resolved in 85 % of reports with at least one event, and 53 % of all reports. Specifically, this represented 46 % of reports (235/512) for differentiation of events, and 75 % of reports (27/36) for classification of seizures.

CONCLUSION

Ambulatory video-EEG recordings are of high diagnostic value in capturing clinically relevant events and resolving the referring clinical questions.

Ambulatory EEG-video

Hospital based EEG recordings have been the norm to assist in the diagnosis and management of patients with unclassified events and known drug resistant epilepsy. Ambulatory EEG (AEEG) is a tool that comes to serve the needs for a portable testing that can be done at home, often with higher accessibility compared to an epilepsy monitoring unit and with lower cost. The current technology provides good quality EEG tracing and can be done with video when needed. In this review we discuss how AEEG should be performed and the preferred indications in which this test may be of utmost help. The advent of ultra-long ambulatory recording may be the future for selected patients as this technology evolves.

Get Out the Door: Ambulatory EEG Trumps Routine EEG in the Detection of Interictal Epileptiform Abnormalities After a First Unprovoked Seizure

Diagnostic Accuracy of Ambulatory EEG vs Routine EEG in Patients With First Single Unprovoked Seizure Hernandez-Ronquillo L, Thorpe L, Feng C, Hunter G, Dash D, Hussein T, Dolinsky C, Waterhouse K, Roy P, Jette N. Neurol Clin Pract . 2023;13(3). doi:10.1212/CPJ.0000000000200160 Background and Objective: To evaluate the diagnostic accuracy of the ambulatory EEG (aEEG) at detecting interictal epileptiform discharges (IEDs)/seizures compared with routine EEG (rEEG) and repetitive/second rEEG in patients with a first single unprovoked seizure (FSUS). We also evaluated the association between IED/seizures on aEEG and seizure recurrence within 1 year of follow-up. Methods: We prospectively evaluated 100 consecutive patients with FSUS at the provincial Single Seizure Clinic. They underwent 3 sequential EEG modalities: first rEEG, second rEEG, and aEEG. Clinical epilepsy diagnosis was ascertained based on the 2014 International League Against Epilepsy definition by a neurologist/epileptologist at the clinic. An EEG-certified epileptologist/neurologist interpreted all 3 EEGs. All patients were followed up for 52 weeks until they had either second unprovoked seizure or maintained single seizure status. Accuracy measures (sensitivity, specificity, negative and positive predictive values, and likelihood ratios), receiver operating characteristic (ROC) analysis, and area under the curve (AUC) were used to evaluate the diagnostic accuracy of each EEG modality. Life tables and the Cox proportional hazard model were used to estimate the probability and association of seizure recurrence. Results: Ambulatory EEG captured IED/seizures with a sensitivity of 72%, compared with 11% for the first rEEG and 22% for the second rEEG. The diagnostic performance of the aEEG was statistically better (AUC: 0.85) compared with the first rEEG (AUC: 0.56) and second rEEG (AUC: 0.60). There were no statistically significant differences between the 3 EEG modalities regarding specificity and positive predictive value. Finally, IED/seizure on the aEEG was associated with more than 3 times the hazard of seizure recurrence. Discussion: The overall diagnostic accuracy of aEEG at capturing IED/seizures in people presenting with FSUS was higher than the first and second rEEGs. We also found that IED/seizures on the aEEG were associated with an increased risk of seizure recurrence. Classification of Evidence: This study provides Class I evidence supporting that, in adults with First Single Unprovoked Seizure (FSUS), 24-h ambulatory EEG has increased sensitivity when compared with routine and repeated EEG.

The Yield of Ambulatory Video-EEG: Predictors of Successful Event Capture

Background and Objectives

The purpose of this study was to assess the likelihood of capturing a patient's typical event in question on ambulatory video-EEG monitoring (AVEM) based on certain baseline patient or event characteristics.

Methods

We retrospectively reviewed 300 studies that resulted between June 2021 and August 2022 ordered by adult epileptologists. Patients were included in event analysis if the study was ordered for the purpose of capturing an event (and excluded for all other purposes).

Results

A total of 149 studies were included in event analysis. Sixty-eight patients (46%) had their typical events captured on AVEM. Diagnosis was an epileptic seizure in 17 patients (25%), psychogenic nonepileptic seizure in 7 (10%), and other nonepileptic events in 44 (65%). Regarding event frequency, for patients who on average had daily events, 84% had events captured, which corresponds to a significantly increased odds ratio (OR 17.90, 95% CI 7.55-42.44, p < 0.001). For those who had events <1 per week to ≥1 per month, only 9% had events captured (OR 0.06, 95% CI 0.02-0.19, p < 0.001). No patients who had events less frequently than once per month had a diagnostic AVEM. Regarding the number of antiseizure medications (ASMs), the odds ratio was increased for those not on ASMs (OR 2.65, 95% CI 1.17 -6.03, p = 0.02) and decreased for those on 1 ASM (OR 0.28, 95% CI 0.13 -0.60, p = 0.001). There was no statistical significance based on event type (motor vs nonmotor), prior seizure diagnosis, history of psychiatric comorbidity, or presence of a focal brain lesion.

Discussion

Certain baseline characteristics can increase or decrease the pretest probability of capturing a typical event on AVEM, particularly the frequency of events and number of ASMs. This can be useful information for clinicians before ordering a study so that resources can be properly allocated.

Ambulatory video EEG extended to 10 days: A retrospective review of a large database of ictal events

OBJECTIVE

This work aims to determine the ambulatory video electroencephalography monitoring (AVEM) duration and number of captured seizures required to resolve different clinical questions, using a retrospective review of ictal recordings.

METHODS

Patients who underwent home-based AVEM had event data analyzed retrospectively. Studies were grouped by clinical indication: differential diagnosis, seizure type classification, or treatment assessment. The proportion of studies where the conclusion was changed after the first seizure was determined, as was the AVEM duration needed for at least 99% of studies to reach a diagnostic conclusion.

RESULTS

The referring clinical question was not answered entirely by the first event in 29.6% (n = 227) of studies. Diagnostic and classification indications required a minimum of 7 days for at least 99% of studies to be answered, whilst treatment-assessment required at least 6 days.

CONCLUSIONS

At least 7 days of monitoring, and potentially multiple events, are required to adequately answer these clinical questions in at least 99% of patients. The widely applied 72 h or single event recording cut-offs may be inadequate to adequately answer these three indications in a substantial proportion of patients.

SIGNIFICANCE

Extended duration of monitoring and capturing multiple events should be considered when attempting to capture seizures on video-EEG.

Does the duration of video-EEG recording influence diagnostic yield in pediatric epilepsy: Results from a single center study

AIM

This exploratory study examines the association of the duration of electroencephalography (EEG) recordings to diagnostic yield in children undergoing evaluation for seizures.

METHOD

Clinical and EEG data on three hundred and ten patients (167 males and 143 females) were reviewed retrospectively. 134 (43.2%) children with focal-onset seizures, and 59 (19%) children with generalized-onset seizures. The mean duration of recordings in studies interpreted as "diagnostic" was compared to studies that were interpreted as "non-diagnostic". EMU recordings were also compared to routine EEG studies to identify the relationship between routine EEG and diagnostic studies.

RESULTS

The principal finding of this study indicates that a longer duration of monitoring is more likely to be associated with a positive diagnostic yield. Mean duration of recording in children with a "non-diagnostic study" was 31.05 hours versus 44.27 hours; p < 0.001 in a "diagnostic study". EMU recordings are likely to be diagnostic with longer epilepsy duration (2.6 years vs 3.7 years; p < 0.01). A diagnostic EEG from a prolonged recording is more likely to be achieved in children with abnormal routine EEG and focal-onset seizures. p < 0.001.

INTERPRETATION

Tailoring the optimal duration of EEG recordings and factoring in confounding variables will reduce the need for repeated studies, improving diagnostic utility and permitting efficient utilization of resources.

Staring Spells: An Age-based Approach Toward Differential Diagnosis

Evaluations to rule out epileptic vs nonepileptic staring spells may entail unnecessary evaluations that can be costly and time consuming. Our study aims to identify common etiologies for staring spells across 3 different pediatric age groups and to propose an age-based clinical guidance to help determine which patients warrant further workup. Methods: This was a single-center retrospective chart analysis of 1496 patients aged 0.0-17.9 years presenting with confirmed staring spell diagnosis from January 2011 to January 2021. The patients were divided into 3 groups based on their age: 0.0-2.9, 3.0-12.9, and 13.0-17.9 years. Patient information collected included demographics, clinical presentation, comorbidities, and final diagnosis. Multilevel likelihood ratios and a receiver operating characteristic curve were determined using 8 of the 11 clinical variables. A total of 1142 patients who met the inclusion criteria were included for the final analysis. The most common final diagnosis was attention-deficit hyperactivity disorder (ADHD) (35%), followed by normal behavior (33%). Generalized and focal epilepsy were diagnosed in 8% and 4% of the patients, respectively. In the 0.0-2.9-year age group, normal behavior was the final diagnosis in 72% patients. In the 3.0-12.9-year and 13.0-17.9-year age groups, ADHD was the most frequent final diagnosis in 46% and 60%, respectively. Overall, ADHD and normal behaviors remain the most common final diagnoses. Multilevel likelihood ratios can be used to develop an age-based guidance to differentiate between epileptic and nonepileptic staring spell diagnoses.

What is the optimal duration of home-video-EEG monitoring for patients with <1 seizure per day? A simulation study

Ambulatory “at home” video-EEG monitoring (HVEM) may offer a more cost-effective and accessible option as compared to traditional inpatient admissions to epilepsy monitoring units. However, home monitoring may not allow for safe tapering of anti-seizure medications (ASM). As a result, longer periods of monitoring may be necessary to capture a sufficient number of the patients' stereotypic seizures. We aimed to quantitatively estimate the necessary length of HVEM corresponding to various diagnostic scenarios in clinical practice. Using available seizure frequency statistics, we estimated the HVEM duration required to capture one, three, or five seizures on different days, by simulating 100,000 annual time-courses of seizure occurrence in adults and children with more than one and <30 seizures per month (89% of adults and 85% of children). We found that the durations of HVEM needed to record 1, 3, or 5 seizures in 80% of children were 2, 5, and 8 weeks (median 2, 12, and 21 days), respectively, and significantly longer in adults −2, 6, and 10 weeks (median 3, 14, and 26 days; p < 10⁻¹⁰ for all comparisons). Thus, longer HVEM than currently used is needed for expanding its clinical value from diagnosis of nonepileptic or very frequent epileptic events to a presurgical tool for patients with drug-resistant epilepsy. Technical developments and further studies are warranted.

How much time is enough? Establishing an optimal duration of recording for ambulatory video EEG

OBJECTIVE

Ambulatory video EEG allows for extended recording of EEG in the comfort of a patient's home. However, the optimal duration of recording to capture clinical events is yet to be established. The current study uses retrospective analyses to identify an optimal recording duration for at-home video EEG.

METHODS

A retrospective review was performed utilizing an anonymized database of ambulatory video EEG recordings performed between March and September 2020 with a national in-home EEG provider. Only completed assessments with neurologists' reads of raw data were reviewed, resulting in 3644 unique studies divided into three age cohorts: pediatrics (n = 941), adult (n = 2020), and geriatric (n = 683). Cohorts were characterized by assessment yield and time to first typical clinical event, as well as subsequent typical events over duration of recording.

RESULTS

Frequency distributions reveal over half of first events are captured within 12 hours, but longer recording durations capture a much wider majority of both first typical events, as well as the mean number of subsequent events (5 clinical events). In 72 hours, over 97% of first events were observed in adult and geriatric patients, as well as over 95% of the mean number of subsequent events. In children, time to first event was significantly earlier than either adult or geriatric samples, with 98% of first events, and 92.8% of the mean number of subsequent events being observed in 48 hours.

SIGNIFICANCE

These results from a large-scale, national dataset of patients using in-home EEG monitoring suggests recording at least 48 hours in duration for children, and at least 72 hours in duration for adult and geriatric samples, is optimal to maximize the likelihood of observing typical clinical events to facilitate diagnosis.