Epilepsy Monitoring Unit Admission Research Articles

Ex-SPECTing success: Predictors of successful SPECT radiotracer injection during presurgical video-EEG admissions.

To determine predictors of successful ictal single photon emission computed tomography (SPECT) injections during Epilepsy Monitoring Unit (EMU) admissions for patients undergoing presurgical evaluation for drug-resistant focal epilepsy. In this retrospective study, consecutive EMU admissions were analyzed at a single center between 2019 and 2021. All seizures that occurred during the admission were reviewed. "Injectable seizures" occurred during hours when the radiotracer was available. EMU-level data were analyzed to identify factors predictive of an EMU admission with a successful SPECT injection (successful admission). Seizure-level data were analyzed to identify factors predictive of an injectable seizure receiving a SPECT injection during the ictal phase (successful injection). A multivariate generalized linear model was used to identify predictive variables. 125 EMU admissions involving 103 patients (median 37 years, IQR 27.0-45.5) were analyzed. 38.8% of seizures that were eligible for SPECT (n = 134) were successfully injected; this represented 17.4% of all seizures (n = 298) that occurred during admission. Unsuccessful admissions were most commonly due to a lack of seizures during EMU-SPECT (19.3%) or no injectable seizures (62.3%). Successful EMU-SPECT was associated with baseline seizure frequency >1 per week (95% CI 2.1-3.0, P < 0.001) and focal PET hypometabolism (95% CI 2.0-3.7, P < 0.001). On multivariate analysis, the only factor associated with successful injection was patients being able to indicate they were having a seizure to staff (95% CI 1.0-4.4, P = 0.038). Completing a successful ictal SPECT study remains challenging. A baseline seizure frequency of >1 per week, a PET hypometabolic focus, and a patient's ability to indicate seizure onset were identified as predictors of success. These findings may assist EMUs in optimizing their SPECT protocols, patient selection, and resource allocation.

Utility of Tilt Table Testing in Management of Seizure-like Activity After Inconclusive Epilepsy Monitoring Unit Admission (P6-1.014)

Utility of Tilt Table Testing in Management of Seizure-like Activity After Inconclusive Epilepsy Monitoring Unit Admission (P6-1.014)

Sleep physiology in patients with epilepsy: Influence of seizures on rapid eye movement (REM) latency and REM duration.

A well-established bidirectional relationship exists between sleep and epilepsy. Patients with epilepsy tend to have less efficient sleep and shorter rapid eye movement (REM) sleep. Seizures are far more likely to arise from sleep transitions and non-REM sleep compared to REM sleep. Delay in REM onset or reduction in REM duration may have reciprocal interactions with seizure occurrence. Greater insight into the relationship between REM sleep and seizure occurrence is essential to our understanding of circadian patterns and predictability of seizure activity. We assessed a cohort of adults undergoing evaluation of drug-resistant epilepsy to examine whether REM sleep prior to or following seizures is delayed in latency or reduced in quantity. We used a spectrogram-guided approach to review the video-electroencephalograms of patients' epilepsy monitoring unit admissions for sleep scoring to determine sleep variables. In our cohort of patients, we found group- and individual-level delay of REM latency and reduced REM duration when patients experienced a seizure before the primary sleep period (PSP) of interest or during the PSP of interest. A significant increase in REM latency and decrease in REM quantity were observed on nights where a seizure occurred within 4 h of sleep onset. No change in REM variables was found when investigating seizures that occurred the day after the PSP of interest. Our study is the first to provide insight about a perisleep period, which we defined as 4-h periods before and after the PSP. Our results demonstrate a significant relationship between seizures occurring prior to the PSP, during the PSP, and in the 4-h perisleep period and a delay in REM latency. These findings have implications for developing a biomarker of seizure detection as well as longer term seizure risk monitoring.

Length of Stay Does Not Predict Change in Epilepsy Monitoring Unit Comfort Questionnaire Scores.

BACKGROUND: The epilepsy monitoring unit (EMU) is a clinical setting designed to help diagnose and analyze the nature behind a patient's seizures in a hospitalized unit. Patients admitted to an EMU may experience sleep deprivation, withdrawal of antiepileptic medications, and the use of a continuous electroencephalogram. The purpose of this study was to explore change in patient comfort during an EMU admission. METHODS: The Epilepsy Monitoring Unit Comfort Questionnaire (EMUCQ) was used to evaluate the initial stress level of EMU patients on their date of admission versus their fourth day on the unit. RESULTS: The average EMUCQ score from the admission date was 196.6 (26.28), whereas the mean EMUCQ score on the fourth day was 197.8 (24.79). The P value of .802 and t value of 0.25 indicated that the scores were not statistically significantly different. CONCLUSION: Although some scores indicated there was a large change between baseline and follow-up, these scores could not be readily attributed to the patient's length of stay in the unit. Future studies should examine the role of specific variables hypothesized to impact comfort in the EMU.

Distinction between epileptic and non-epileptic arousal by heart rate change

ObjectiveWe investigated the difference in heart rate (HR) change between epileptic and non-epileptic arousals in adult patients with epilepsy (PWE). MethodsThis is a case-control study conducted at the University Hospitals of Cleveland Medical Center. Inclusion criteria are (1) adult (≥18 years old) PWE who had arousal related to a focal aware or impaired awareness automatism seizure with or without focal to bilateral tonic-clonic seizure during an Epilepsy Monitoring Unit (EMU) admission between January 2009 and January 2021 or (2) adult PWE who had a non-epileptic arousal during an EMU admission between July 2020 and January 2021. Outcomes are (1) a percent change in baseline HR within 60 s after arousal and (2) the highest percent change in baseline HR within a 10-s sliding time window within 60 s after arousal. ResultsWe included 20 non-epileptic arousals from 20 adult PWE and 29 epileptic arousals with seizures from 29 adult PWE. Within 60 s after arousal, HR increased by a median of 86.7% (interquartile range (IQR), 52.7%–121.3%) in the epileptic arousal group compared to a median of 26.1% (12.9%–43.3%) in the non-epileptic arousal group (p < 0.001). The cut-off value was 48.7%. The area under the curve (AUC), sensitivity, and specificity were 0.85, 0.79, and 0.80, respectively. More than 70.1% was only in the epileptic arousals, with 100% specificity. Within 10 s of the greatest change, HR increased by 36.5 (18.7%–48.4%) in the epileptic arousal group compared to 17.7 (10.9%–23.7%) in the non-epileptic arousal group (p < 0.001). The cut-off value was 36.5%. The AUC, sensitivity, and specificity were 0.79, 0.52, and 0.95, respectively. More than 48.1% was only in the epileptic arousals, with 100% specificity. SignificanceTachycardia during epileptic arousals was significantly higher and more robust compared to tachycardia during non-epileptic arousals.

The impact of the time to last seizure before admission to the epilepsy monitoring unit (EMU) on epilepsy classifications

Introduction and backgroundThe impact of the timing of the last seizure (TTLS) prior to admission to the epilepsy monitoring unit (EMU) on epilepsy classification is unclear for which we conducted this study. MethodsWe reviewed patients with epilepsy admitted to EMU between January 2021 and April 2022 and identified TTLS before EMU admission. We considered EMU yield as whether; it confirmed epilepsy classification, added new knowledge to the classification, or failed to classify epilepsy. ResultsWe studied 156 patients. There were 72 (46%) men, with a mean age of 30. TTLS was divided according to a one- or three-month cutoff. We confirmed the pre-EMU epilepsy classification in 52 (33%) patients, learned new findings on epilepsy classification in 80 (51%) patients, and failed to classify epilepsy in 24 (15%) patients. Patients with “confirmed epilepsy classifications” reported seizures sooner to EMU admission than other groups (0.7 vs. 2.3 months, p-value = 0.02, 95% CI; −1.8, −1.3). Also, the odds of confirming epilepsy classification were more than two times in patients with TTLS within a month compared to those with TTLS of more than a month (OR = 2.4, p-value = 0.04, 95% CI; 1.1, 5.9). The odds were also higher when the 3-month TTLS cutoff was considered (OR = 6.2, p-value = 0.002, 95% CI; 1.6, 40.2). Confirming epilepsy classification was also associated with earlier seizures recorded at one- or three-month cutoff (OR = 2.1 and OR = 2.3, respectively, p-value = 0.05). We did not observe similar findings when we modified the classification or failed to reach a classification. ConclusionsThe timing of the last seizure before EMU admission appeared to influence the yield of EMU and enhanced the confirmation of epilepsy classifications. Such findings can improve the utilization of EMU in the presurgical evaluation of patients with epilepsy.

Short versus Long video-EEG in Diagnosis of Psychogenic Non-Epileptic Spells (PNES): Yield and Cost-Effectiveness (P1-1.002)

<h3>Objective:</h3> Comparing the yield and cost effectiveness of short video electroencephalogram (SVEEG) vs long video electroencephalogram (LVEEG) in diagnosis of PNES and finding out the contribution of verbal suggestion in induction of PNES. <h3>Background:</h3> LVEEG at the epilepsy monitoring unit (EMU) is the gold standard test for diagnosis of PNES. In hospitals at resource-limited countries, EMUs are not available and routine EEG studies are used instead. <h3>Design/Methods:</h3> A retrospective observational study, conducted at a tertiary hospital at northern of Jordan, included adult patients above 16 years who underwent VEEG for spell classification. VEEGs were either SVEEG done in the outpatient lab, or overnight LVEEG done in the sleep lab. EEGs were read by American board epileptologists. The two groups were compared in terms of demographic data, comorbidities, features of epilepsy/PNES, and results of monitoring. <h3>Results:</h3> A total of 143 patients were included, all of them were evaluated using EEG for spell classification. 27 patients (18.8% of total cohort) were evaluated using LVEEG. 116 patients (81.1% of the total cohort) were evaluated using SVEEG. PNES were captured in 10 out of the 27 patients (37%) who underwent LVEEG, 7 of them (70%) had typical habitual events and 3 out of them (30%) were suggestible. On the other hand, 77 patients had PNES out of the 116 patients who underwent SVEEG (66%), 62 of them (80.5%) had typical habitual events and 28 out of them (36%) were suggestible. SVEEG costs around 200 dollars and LVEEG costs around 1000 dollars in our institution. EMU admission would cost 2000 dollars or more for a few days admission. <h3>Conclusions:</h3> SVEEG is highly effective in diagnosing PNES with better time and cost effectiveness in comparison to LVEEG. We encourage its use in centers with limited resources. Verbal suggestion is highly effective in the induction of PNES. <b>Disclosure:</b> Ms. Al Refa’i has nothing to disclose. Dr. Yassin has nothing to disclose. Dr. AL Anbar has nothing to disclose. Mr. Alsabbah has nothing to disclose. Dr. Bashayreh has nothing to disclose. Khalid El-Salim has nothing to disclose.

Possible post-COVID epilepsy: A review of epilepsy monitoring unit admissions during the two years of COVID-19 pandemic.

Large scale healthcare data shows that new-onset epilepsy is noted in 0.3% patients within 6months of COVID-19 infection. We analyzed diagnostic epilepsy monitoring unit (EMU) evaluations to identify and report such cases. We thoroughly reviewed our EMU database and identified patients having "COVID" or "Corona" virus mention in their medical record from 03/15/2020 to 02/28/2022. Patients with epilepsy prior to COVID infection were excluded. Among 62 patients without prior epilepsy evaluated in the EMU for new-onset spells after confirmed COVID-19 infection, three patients were diagnosed with focal epilepsy. These three women without epilepsy risk factors had seizure onset at the time of, or within one to three months of, COVID-19 diagnosis. Their 3T MRI imaging was non-lesional but revealed bilateral enlarged perivascular spaces. The video EEG monitoring was consistent with temporal or fronto-temporal lobe epilepsy in all three patients. Two of them developed drug-resistant epilepsy within six months of seizure onset. Our thorough analysis of diagnostic EMU evaluations during the two years of pandemic reveals three cases of post-COVID-19 epilepsy after non-symptomatic to mild disease. Although coincidental epilepsy onset cannot be ruled out, larger multicenter or national database investigations are needed to further analyze the possibility of post-COVID epilepsy.

Seizure count forecasting to aid diagnostic testing in epilepsy.

Epilepsy monitoring unit (EMU) admissions are critical for presurgical evaluation of drug-resistant epilepsy but may be nondiagnostic if an insufficient number of seizures are recorded. Seizure forecasting algorithms have shown promise for estimating the likelihood of seizures as a binary event in individual patients, but methods to predict how many seizures will occur remain elusive. Such methods could increase the diagnostic yield of EMU admissions and help patients mitigate seizure-related morbidity. Here, we evaluated the performance of a state-space method that uses prior seizure count data to predict future counts. A Bayesian negative-binomial dynamic linear model (DLM) was developed to forecast daily electrographic seizure counts in 19 patients implanted with a responsive neurostimulation (RNS) device. Holdout validation was used to evaluate performance in predicting the number of electrographic seizures for forecast horizons ranging 1-7 days ahead. One-day-ahead prediction of the number of electrographic seizures using a negative-binomial DLM resulted in improvement over chance in 73.1% of time segments compared to a random chance forecaster and remained >50% for forecast horizons of up to 7 days. Superior performance (mean error=.99) was obtained in predicting the number of electrographic seizures in the next day compared to three traditional methods for count forecasting (integer-valued generalized autoregressive conditional heteroskedasticity model or INGARCH, 1.10; Croston, 1.06; generalized linear autoregressive moving average model or GLARMA, 2.00). Number of electrographic seizures in the preceding day and laterality of electrographic pattern detections had highest predictive value, with greater number of electrographic seizures and RNS magnet swipes in the preceding day associated with a higher number of electrographic seizures the next day. This study demonstrates that DLMs can predict the number of electrographic seizures a patient will experience days in advance with above chance accuracy. This study represents an important step toward the translation of seizure forecasting methods into the optimization of EMU admissions.

Differences in elective epilepsy monitoring unit admission rates by race/ethnicity and primary payer in New Jersey

Elective admission to the epilepsy monitoring unit (EMU) is an essential service provided by epilepsy centers, particularly for those with drug-resistant epilepsy. Given previously characterized racial and socioeconomic healthcare disparities in the management of epilepsy, we sought to understand access and utilization of this service in New Jersey (NJ). We examined epilepsy hospitalizations in NJ between 2014 and 2016 using state inpatient and emergency department (ED) databases. We stratified admissions by race/ethnicity and primary payer and used these to estimate and compare (1) admission rates per capita in NJ, as well as (2) admission rates per number of ED visits for each group. Patients without insurance underwent elective EMU admission at the lowest rates across all racial/ethnic groups and payer types studied. Black patients with Medicaid and private insurance were admitted at disproportionately low rates relative to their number of ED visits. Hispanic/Latino and Asian/Pacific Islanders with private insurance, Hispanic/Latinos with Medicaid, and Asian/Pacific Islanders with Medicare were also admitted at low rates per capita within each respective payer category. Future studies should focus on addressing causal factors driving healthcare disparities in epilepsy, particularly for patients without adequate health insurance coverage and those who have been historically underserved by the healthcare system.

Epileptic seizures and Epilepsy Monitoring Unit admission disclose latent cardiac electrical instability

BackgroundSudden cardiac arrest results from cardiac electrical instability and is 3-fold more frequent in patients with chronic epilepsy than in the general population. We hypothesized that focal to bilateral tonic-clonic seizures (FTBTCS) would acutely impact T-wave alternans (TWA), a marker of cardiac electrical instability linked to an elevated risk for sudden cardiac death, more than focal seizures (FS) [focal aware seizures (FAS) and focal with impaired awareness seizures (FIAS)], due to their greater sympathetic stimulation of the heart. Since stress has been shown to cause significant TWA elevations in patients with heart disease, we also hypothesized that the early days of an inpatient admission to an epilepsy monitoring unit (EMU) would be associated with higher TWA levels compared to later hospital days in patients with chronic epilepsy, presumably due to stress. Design/MethodsWe analyzed the acute effects of seizures [FAS, FIAS, FTBTCS, and nonepileptic seizures (NES)] and day of hospital stay on TWA in 18 patients admitted to the EMU using high-resolution wireless electrocardiographic (ECG) patch monitors. ResultsA total of 5 patients had FTBTCS, 7 patients had FS (2 FAS, 5 FIAS), and 3 patients had NES only during the index hospital stay. Four patients did not have any electroclinical seizures or NES. FTBTCS resulted in marked acute increases in ictal TWA from baseline (2 ± 0.3 µV) to ictal maximum (70 ± 6.1 µV, p < 0.0001), the latter exceeding the 60 µV cut point defined as severely abnormal. By comparison, while FAS and FIAS also provoked significant increases in TWA (from 2 ± 0.5 µV to 30 ± 3.3 µV, p < 0.0001), maximum ictal TWA levels did not reach the 47 µV cut point defined as abnormal. Heart rate increases during FTBTCS from baseline (62 ± 5.8 beats/min) to ictal maximum (134 ± 8.6 beats/min, an increase of 72 ± 7.2 beats/min, p < 0.02) were also greater (p = 0.014) than heart rate increases during FS (from 70 ± 5.2 beats/min to 118 ± 6.2 beats/min, an increase of 48 ± 2.6 beats/min, p < 0.03). In 3 patients with NES, TWA rose mildly during the patients’ typical episodes (from 2 ± 0.6 µV to 14 ± 2.6 µV, p < 0.0004), well below the cut point of abnormality, while heart rate increases were observed (from 75 ± 1.3 to 112 ± 8.7 beats/min, an increase of 37 ± 8.9 beats/min, p = 0.03). Patients with EEG-confirmed electroclinical seizures recorded while in the EMU exhibited significantly elevated interictal TWA maxima (61 ± 3.4 µV) on EMU admission day which were similar in magnitude to ictal maxima seen during FTBTCS (70 ± 6.1 µV, p = 0.21). During subsequent days of hospitalization, daily interictal TWA maxima showed gradual habituation in patients with both FS and FTBTCS but not in patients with NES only. ConclusionsThis is the first study to our knowledge demonstrating that FTBTCS acutely provoke highly significant increases in TWA to levels that have been associated with heightened risk for sudden cardiac death in other patient populations. We speculate that mortality temporally associated with FTBTCS may, in some cases, be due to sudden cardiac death rather than respiratory failure. In patients with EEG-confirmed epilepsy, hospital admission is associated with interictal TWA maxima that approach those seen during FTBTCS, presumably related to stress during the early phase of hospitalization compared to later in the hospitalization, indicating cardiac electrical instability and potential vulnerability to sudden cardiac death related to stress independent of temporal relationships to seizures. The elevated heart rates observed acutely with seizures and on hospital Day 1 are consistent with a hyperadrenergic state and the effect of elevated sympathetic output on a vulnerable cardiac substrate, a phenomenon termed “the Epileptic Heart.”

Factors associated with comorbid epilepsy in patients with psychogenic nonepileptic seizures: A large cohort study

ObjectiveComorbid epilepsy and psychogenic nonepileptic seizures (PNES) occur in 12–22% of cases and the diagnosis of both simultaneous disorders is challenging. We aimed to identify baseline characteristics that may help distinguish patients with PNES-only from those with comorbid epilepsy. MethodsWe performed a longitudinal cohort study on those patients diagnosed with PNES in our epilepsy monitoring unit (EMU) between May 2001 and February 2011, prospectively followed up until September 2016. Patients were classified into PNES-only, PNES + possible or probable epilepsy, and PNES + definite epilepsy based on the clinical, vEEG, and neuroimaging data. Demographic and basal clinical data were obtained from chart review. Multiple regression models were performed to identify significant predictors of PNES + definite epilepsy, excluding patients with only possible or probable epilepsy for this specific analysis. ResultsOne-hundred and ninety four patients with PNES-only, 30 with PNES + possible or probable epilepsy and 47 with PNES + definite epilepsy were included. 73.8% were female and the mean age at EMU admission was 37.4 ± standard deviation 13.5 years.Patients with PNES + definite epilepsy most likely had never worked, had history of febrile seizures, structural brain lesions, developmental disabilities, and maximum reported seizure duration between 0.5 and 2 min.Patients with PNES-only were on fewer anti-seizure medications (ASM), reported more frequently an initial minor head trauma, seizures longer than 10 min, and a higher number of neurological and medical illnesses – being migraine (18.1%), other types of headaches (18.5%), and asthma (15.5%) the most prevalent ones. All p < 0.05.On the hierarchical regression analysis, history of febrile seizures, developmental disabilities, brain lesions, longest reported seizure duration between 0.5 and 2 min, and lack of neurological comorbidity, remained as significant predictors of PNES + epilepsy. The model's performance of a 5-fold cross-validation analysis showed an overall accuracy of 84.7% to classify patients correctly. ConclusionsSome demographic and clinical characteristics may support the presence of comorbid epilepsy in patients with PNES, being unemployment, the presence of brain lesions, developmental disabilities, history of febrile seizures, seizure duration and lack of comorbid headaches the most relevant ones.

Impact of Clinical Pharmacists on Access to Care in an Epilepsy Clinic.

Epilepsy affects about 1% of the world population and is one of the most burdensome diseases in terms of disability-adjusted life-years. The demand for neurologists and epileptologists is expected to exceed current supply by 2025. One potential strategy to increase access to epilepsy care is to utilize clinical pharmacist practitioners (CPPs) with a broad scope of practice. Appointments at the William S. Middleton Memorial Veterans Hospital (WSMVH) epilepsy clinic in Madison, Wisconsin, were reviewed to determine the percentage of appointments completed by a CPP or clinical pharmacy resident from October 2019 to May 2021. Additionally, a retrospective chart review was completed on 446 veterans to identify the types of interventions made by a CPP or clinical pharmacy resident at each appointment from October 2017 to June 2021. The CPP or clinical pharmacy resident held approximately 43% of 591 total appointments and spent a mean 27 minutes with each patient. Medication interventions occurred at 27% of 446 appointments in the retrospective chart review. Half (50.4%) of all patients seen by a CPP completed at least 1 mental health screening. The integration of a CPP WSMVH epilepsy clinic allowed for greater and more timely access to care and allowed for the epileptologists to focus their time on new consults, Epilepsy Monitoring Unit admissions, and higher acuity cases.

Reduced REM sleep: a potential biomarker for epilepsy – a retrospective case-control study

ObjectiveEstablishing the diagnosis of epilepsy can be challenging if interictal epileptic discharges (IEDs) or seizures are undetectable. Many individuals with epilepsy experience sleep disturbances. A reduced percentage of REM sleep (REM%) has been observed following seizures. We aimed to assess differences of REM% in individuals with epilepsy in comparison with differential diagnoses. MethodsWe performed a retrospective, monocentric, two-armed case-control study with 128 age-matched individuals who underwent ≥72 hours of continuous video-EEG monitoring at our epilepsy monitoring unit (EMU) for diagnostic evaluation. We assessed REM% on the first and last night of EMU admission. Logistic regressions models were used to evaluate the predictive value of REM%. ResultsWe included 64 individuals diagnosed with epilepsy and 64 with a differential diagnosis. REM% in the epilepsy group was significantly lower [12.2% (±4.7) vs. 17.2% (±5.2), p<0.001]. We found no significant influence of sex, age, anti-seizure, or other medications. A REM%-based and an IED and seizure-based regression model were not significantly different [area under the curve (AUC) 0.791 (95% confidence interval (CI): 0.713-0.870) vs. 0.853 (95% CI: 0.788-0.919), p=0.23]. A combined model, based on IEDs, seizures, and REM%, was superior to the IED model alone [0.933 (0.891-0.975), p<0.01]. InterpretationOur study shows significantly reduced REM% in individuals with epilepsy. REM%-based models show a good predictive performance. REM% assessment could improve diagnostic accuracy – especially for challenging cases, e.g., when IEDs or seizures are absent and patient history and semiology appear ambiguous. REM% as a biomarker should be evaluated in prospective, multicentric trials.

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study.

ObjectiveSudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls.MethodsThis multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve.ResultsThe logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73–0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction.ConclusionsOur results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.

United States Epilepsy Center Characteristics: A Data Analysis From the National Association of Epilepsy Centers.

Background and ObjectivesPatients with drug-resistant epilepsy (DRE) may benefit from specialized testing and treatments to better control seizures and improve quality of life. Most evaluations and procedures for DRE in the United States are performed at epilepsy centers accredited by the National Association of Epilepsy Centers (NAEC). On an annual basis, the NAEC collects data from accredited epilepsy centers on hospital-based epilepsy monitoring unit (EMU) size and admissions, diagnostic testing, surgeries, and other services. This article highlights trends in epilepsy center services from 2012 through 2019.MethodsWe analyzed data reported in 2012, 2016, and 2019 from all level 3 and level 4 NAEC accredited epilepsy centers. Data were described using frequency for categorical variables and median for continuous variables and were analyzed by center level and center population category. EMU beds, EMU admissions, epileptologists, and aggregate procedure volumes were also described using rates per population per year.ResultsDuring the period studied, the number of NAEC accredited centers increased from 161 to 256, with the largest increases in adult- and pediatric-only centers. Growth in EMU admissions (41%), EMU beds (26%), and epileptologists (109%) per population occurred. Access to specialized testing and services broadly expanded. The largest growth in procedure volumes occurred in laser interstitial thermal therapy (LiTT) (61%), responsive neurostimulation (RNS) implantations (114%), and intracranial monitoring without resection (152%) over the study period. Corpus callosotomies and vagus nerve stimulator (VNS) implantations decreased (−12.8% and −2.4%, respectively), while growth in temporal lobectomies (5.9%), extratemporal resections (11.9%), and hemispherectomies/otomies (13.1%) lagged center growth (59%), leading to a decrease in median volumes of these procedures per center.DiscussionDuring the study period, the availability of specialty epilepsy care in the United States improved as the NAEC implemented its accreditation program. Surgical case complexity increased while aggregate surgical volume remained stable or declined across most procedure types, with a corresponding decline in cases per center. This article describes recent data trends and current state of resources and practice across NAEC member centers and identifies several future directions for driving systematic improvements in epilepsy care.

An Observational Study of an Epilepsy Monitoring Unit in a Level 4 Epilepsy Center, Post-Implementation of the National Association of Epilepsy Centers 2010 Revised Guidelines

Background: Of the 2.7 - 3.4 million Americans estimated to have some form of epilepsy, approximately 25-30% of these individuals do not have adequate seizure control and suffer from intractable epilepsy. The objective of this study was to report outcomes of patients with epilepsy monitoring unit (EMU) admissions using data from a level 4 epilepsy center. Methods: We performed a retrospective review of electronic medical records for 433 EMU patient visits between January 2016 and May 2019 at a level 4 comprehensive epilepsy center. The EMU protocols followed in these admissions were those listed in the guidelines by the National Association of Epilepsy Centers (NAEC). Patients were monitored by a medical team that included electroencephalogram technicians, neurophysiologists, and epileptologists. Results: Of the 433 patients assessed, 384 met inclusion criteria. Mean length of stay in the EMU was 4 days. Of the patients, 73.4% had EMU stays resulting in new information which led to interventions including further diagnostic testing, surgical treatment, and medication changes. The most frequent intervention was a change in medication (68.8% of patients). Of the patients, 90.1% received a definitive diagnosis at the conclusion of their admission, with the most common diagnosis being epileptic seizures (66.7%), followed by non-epileptic physiologic events (14.3%) and psychogenic non-epileptic seizures (8.6%). Conclusions: This study sought to describe outcomes from patients who stayed in our level 4 epilepsy center’s EMU after the implementation of the revised NAEC guidelines made in 2010. We investigated patient demographics as well as diagnosis and/or treatment changes after the EMU stay. We conclude that under the new NAEC guidelines, an EMU admission remains diagnostically useful in identifying if a patient has epilepsy or not. Our goal for this retrospective review is to inform future prospective outcomes studies and add to the body of literature demonstrating an EMU evaluation as a valuable diagnostic tool for epilepsy patients. J Neurol Res. 2021;11(5):87-93 doi: https://doi.org/10.14740/jnr703

An Observational Study of an Epilepsy Monitoring Unit in a Level 4 Epilepsy Center, Post-Implementation of the National Association of Epilepsy Centers 2010 Revised Guidelines

Background: Of the 2.7 - 3.4 million Americans estimated to have some form of epilepsy, approximately 25-30% of these individuals do not have adequate seizure control and suffer from intractable epilepsy. The objective of this study was to report outcomes of patients with epilepsy monitoring unit (EMU) admissions using data from a level 4 epilepsy center. Methods: We performed a retrospective review of electronic medical records for 433 EMU patient visits between January 2016 and May 2019 at a level 4 comprehensive epilepsy center. The EMU protocols followed in these admissions were those listed in the guidelines by the National Association of Epilepsy Centers (NAEC). Patients were monitored by a medical team that included electroencephalogram technicians, neurophysiologists, and epileptologists. Results: Of the 433 patients assessed, 384 met inclusion criteria. Mean length of stay in the EMU was 4 days. Of the patients, 73.4% had EMU stays resulting in new information which led to interventions including further diagnostic testing, surgical treatment, and medication changes. The most frequent intervention was a change in medication (68.8% of patients). Of the patients, 90.1% received a definitive diagnosis at the conclusion of their admission, with the most common diagnosis being epileptic seizures (66.7%), followed by non-epileptic physiologic events (14.3%) and psychogenic non-epileptic seizures (8.6%). Conclusions: This study sought to describe outcomes from patients who stayed in our level 4 epilepsy center’s EMU after the implementation of the revised NAEC guidelines made in 2010. We investigated patient demographics as well as diagnosis and/or treatment changes after the EMU stay. We conclude that under the new NAEC guidelines, an EMU admission remains diagnostically useful in identifying if a patient has epilepsy or not. Our goal for this retrospective review is to inform future prospective outcomes studies and add to the body of literature demonstrating an EMU evaluation as a valuable diagnostic tool for epilepsy patients. J Neurol Res. 2021;11(5):87-93 doi: https://doi.org/10.14740/jnr703

Emergency department visits and readmissions in patients with psychogenic nonepileptic seizures (PNES) at a safety net hospital

IntroductionReadmissions and emergency department (ED) visits after an index admission have been become a quality measure due to associations with poor outcomes and increased healthcare costs. Readmissions and ED encounters have been studied in a variety of conditions including epilepsy but have not been examined exclusively in psychogenic nonepileptic seizures (PNES). In this study we examined the rate of readmissions and ED visits after a discharge from an Epilepsy Monitoring Unit (EMU) in a safety net hospital. We also determined patient phenotypes that are associated with readmissions. Material and methodsThis was a retrospective chart review study with index admission being a discharge from an EMU between January 1 and December 31 2016 with follow-up until August 31 2020. We obtained data regarding demographics, medical and psychiatric history, and social history and treatment interventions. Our outcome variables were both all-cause and seizure-related hospital readmissions and ED visits 30 days following the index discharge and readmissions and ED visits 30 days thereafter. ResultsEleven of 122 patients (9%) had a non-seizure-related ED visit and/or hospitalization within 30 days of index discharge while 45 (37%) had re-contact with the health system thereafter for non-seizure-related issues. Seven of 122 patients (6%) had a seizure-related ED visit or hospital readmission within 30 days of discharge. Twenty-eight (23%) had a seizure-related readmission or ED visit after 30 days. Of these 28, 4 patients had been to an ER within 7 days of EMU discharge. The majority of subsequent encounters with the healthcare system were through the ED (n = 38) as compared to hospital (n = 10) and EMU readmissions (n = 9). On bivariate statistical analysis, charity or self-pay insurance status (p < 0.01), homelessness (p < 0.01), emergent EMU admission on index admission (p < 0.01), history of a psychiatric diagnosis (p < 0.02), and ED encounters 12 months prior to admission (p < 0.01) were significantly associated with readmission; however, on multivariate analysis only charity insurance status was a significant predictor. ConclusionsIn this study of readmissions and ED visits after discharge with a diagnosis of PNES at a safety net hospital, we found a seizure-related readmission rate of approximately 6% in 30 days and 23% thereafter with the majority of re-contact with the hospital being in the ED. On multi-variate analysis insurance status was a significant factor associated with readmission and ED visits. Our future research directions include examining referrals and treatment completion at the hospital’s PNES clinic as well as creating a risk score to better identify patients with PNES at risk of readmission.

Predicting outcome of patients with psychogenic nonepileptic seizures after diagnosis in an epilepsy monitoring unit

ObjectiveTo identify predictors of Psychogenic NonEpileptic Seizure (PNES) improvement and anti-seizure medication (ASM) discontinuation in patients with PNES only. MethodsThis is a retrospective study of a consecutively enrolled cohort of 271 patients diagnosed with PNES by video-EEG (vEEG) telemetry in our Epilepsy Monitoring Unit (EMU) between May 2000 and February 2010. Patients with any possibility of past or present comorbid epilepsy based on clinical, EEG, and neuroimaging, or less than one year of follow-up after discharge were excluded. ResultsA total of 109 subjects were included. The mean age at PNES onset was 33 (range 6–89), mean age at EMU admission was 38.3 (16–89.8), 70.6% were female mean video-EEG length was 6.1 days, and the median time of final follow-up 3.3 (CI 1.6–6.4) years.51/108 patients (47.2%) reported a PNES decrease and 29 (26.8%) experienced PNES resolution. 59/73 (81.9%) subjects on ASM at the time of EMU admission were able to discontinue them by the final visit.On univariate analysis, patients whose PNES frequency improved were significantly younger at time of admission, more likely married or cohabiting, less likely unemployed, less likely to have migraine, and had a higher frequency of PNES. On hierarchical regression analysis, younger age and employment remained significant predictors of PNES improvement and resolution.Patients who achieved ASM discontinuation had significantly more children and subsequent EMU visits, were less likely to have history of minor head trauma immediately preceding PNES onset and structural brain lesions, experienced a greater reduction of ASMs during the EMU admission, and had a greater improvement of their PNES frequency at the final visit (p ≤ 0.05). On hierarchical regression analysis, higher number of children, absence of structural brain lesions, fewer ASMs at EMU discharge, and improvement of PNES frequency remained significant predictors of ASM discontinuation. ConclusionThe outcome of PNES is positively correlated with earlier age of diagnosis in an EMU, especially in patients with better social resources. Furthermore, discontinuation of ASM is more likely if the process is initiated during the EMU stay and in the absence of structural brain lesions.