Comparative analysis of patients with new onset refractory status epilepticus preceded by fever (febrile infection-related epilepsy syndrome) versus without prior fever: An interim analysis.

Febrile infection-related epilepsy syndrome (FIRES) is a subset of new onset refractory status epilepticus (NORSE) that involves a febrile infection prior to the onset of the refractory status epilepticus. It is unclear whether FIRES and non-FIRES NORSE are distinct conditions. Here, we compare 34 patients with FIRES to 30 patients with non-FIRES NORSE for demographics, clinical features, neuroimaging, and outcomes. Because patients with FIRES were younger than patients with non-FIRES NORSE (median = 28 vs. 48 years old, p = .048) and more likely cryptogenic (odds ratio = 6.89), we next ran a regression analysis using age or etiology as a covariate. Respiratory and gastrointestinal prodromes occurred more frequently in FIRES patients, but no difference was found for non-infection-related prodromes. Status epilepticus subtype, cerebrospinal fluid (CSF) and magnetic resonance imaging findings, and outcomes were similar. However, FIRES cases were more frequently cryptogenic; had higher CSF interleukin 6, CSF macrophage inflammatory protein-1 alpha (MIP-1a), and serum chemokine ligand 2 (CCL2) levels; and received more antiseizure medications and immunotherapy. After controlling for age or etiology, no differences were observed in presenting symptoms and signs or inflammatory biomarkers, suggesting that FIRES and non-FIRES NORSE are very similar conditions.

Trends in management of patients with new-onset refractory status epilepticus (NORSE) from 2016 to 2023: An interim analysis.

In response to the evolving treatment landscape for new-onset refractory status epilepticus (NORSE) and the publication of consensus recommendations in 2022, we conducted a comparative analysis of NORSE management over time. Seventy-seven patients were enrolled by 32 centers, from July 2016 to August 2023, in the NORSE/FIRES biorepository at Yale. Immunotherapy was administered to 88% of patients after a median of 3 days, with 52% receiving second-line immunotherapy after a median of 12 days (anakinra 29%, rituximab 25%, and tocilizumab 19%). There was an increase in the use of second-line immunotherapies (odds ratio [OR] = 1.4, 95% CI = 1.1-1.8) and ketogenic diet (OR = 1.8, 95% CI = 1.3-2.6) over time. Specifically, patients from 2022 to 2023 more frequently received second-line immunotherapy (69% vs 40%; OR = 3.3; 95% CI = 1.3-8.9)-particularly anakinra (50% vs 13%; OR = 6.5; 95% CI = 2.3-21.0), and the ketogenic diet (OR = 6.8; 95% CI = 2.5-20.1)-than those before 2022. Among the 27 patients who received anakinra and/or tocilizumab, earlier administration after status epilepticus onset correlated with a shorter duration of status epilepticus (ρ = .519, p = .005). Our findings indicate an evolution in NORSE management, emphasizing the increasing use of second-line immunotherapies and the ketogenic diet. Future research will clarify the impact of these treatments and their timing on patient outcomes.

Impact of acute symptomatic seizures and their management on patient-reported outcomes after stroke.

OBJECTIVE: Acute symptomatic seizures (ASyS) after stroke are not uncommon. However, the impact of ASyS and its management with anti-seizure medications (ASMs) on patient-reported outcome measures (PROMs) remains poorly investigated. The objective of our study is to evaluate the association between PROMs and ASyS and ASMs following stroke. METHODS: We performed a retrospective cohort study of all stroke patients who underwent inpatient continuous EEG (cEEG) monitoring performed due to suspected ASyS, including the ones with observed convulsive ASyS, from 04/01/2012 to 03/31/2018, who completed PROMs within 6 months of hospital discharge. Patient-reported outcome measures, including one Neuro-QoL and six PROMIS v1.0 domain scales, were completed by patients as the standard of care in ambulatory stroke clinics. Since ASMs are sometimes used without clearly diagnosed ASyS, we performed group comparisons based on ASM status at discharge, irrespective of their ASyS status. T-tests or Wilcoxon rank sum tests compared continuous variables across groups and chi-square tests or Fisher's exact tests were used for categorical variables. RESULTS: A total of 508 patients were included in the study [mean age 62.0 ± 14.1 years, 51.6% female; 244 (48.0%) ischemic stroke, 165 (32.5%) intracerebral hemorrhage, and 99 (19.5%) subarachnoid hemorrhage]. A total of 190 (37.4%) patients were discharged on ASMs. At the time of the first PROM, conducted a median of 47 (IQR = 33-78) days after the suspected ASyS, and 162 (31.9%) were on ASMs. ASM use was significantly higher in patients diagnosed with ASyS. Physical Function and Satisfaction with Social Roles and Activities were the most affected health domains. Patient-reported outcome measures were not significantly different between groups based on ASyS (electrographic and/or convulsive), ASM use at hospital discharge, or ASM status on the day of PROM completion. SIGNIFICANCE: There were no differences in multiple domain-specific PROMs in patients with recent stroke according to ASyS status or ASM use suggesting the possible lack of the former's sensitivity to detect their impact. Additional research is necessary to determine if there is a need for developing ASyS-specific PROMs.

Personalized model to predict seizures based on dynamic and static continuous EEG monitoring data.

BACKGROUND/OBJECTIVE: Early recognition of patients who may be at risk of developing acute symptomatic seizures would be useful. We aimed to determine whether continuous electroencephalography (cEEG) data using machine learning techniques such as neural networks and decision trees could predict seizure occurrence in hospitalized patients. METHODS: This was a single center retrospective cohort analysis of cEEG data in patients aged 18-90 years who were admitted and underwent cEEG monitoring between 2010 and 2019 limited to 72 h excluding those who were seizing at the onset of recording. A total of 41,491 patients were reviewed; of these, 3874 were used to develop the static model and 1687 to develop the dynamic model (half with seizure and half without seizure in each cohort). Of these, 80% were randomly selected as derivation cohorts for each model and 20% were randomly selected as validation cohorts. Dynamic and static machine learning models (long short term memory (LSTM) and Extreme Gradient Boosting algorithm (XGBoost)) based on day-to-day dynamic EEG changes and binary static EEG features over the prior 72 h or until seizure, which ever was earlier, were used. RESULTS: The static model was able to predict seizure occurrence based on cEEG data with sensitivity and specificity of 0.81 and 0.59, respectively, with an AUC of 0.70. The dynamic model was able to predict seizure occurrence with sensitivity and specificity of 0.72 and 0.80, respectively, and AUC of 0.81. CONCLUSIONS: Machine learning models could be applied to cEEG data to predict seizure occurrence based on available cEEG data. Dynamic day-to-day EEG data are more useful in predicting seizures than binary static EEG data. These models could potentially be used to determine the need for ongoing cEEG monitoring and to prioritize resources.

Acute Symptomatic Seizures in Critically Ill Patients with COVID-19: Is There an Association?

BACKGROUND: The coronavirus disease of 2019 (COVID-19) emerged as a global pandemic. Historically, the group of human coronaviruses can also affect the central nervous system leading to neurological symptoms; however, the causative mechanisms of the neurological manifestations of COVID-19 disease are not well known. Seizures have not been directly reported as a part of COVID-19 outside of patients with previously known brain injury or epilepsy. We report two cases of acute symptomatic seizures, in non-epileptic patients, associated with severe COVID-19 disease. CASE PRESENTATIONS: Two advanced-age, non-epileptic, male patients presented to our northeast Ohio-based health system with concern for infection in Mid-March 2020. Both had a history of lung disease and during their hospitalization tested positive for SARS-CoV-2. They developed acute encephalopathy days into their hospitalization with clinical and electrographic seizures. Resolution of seizures was achieved with levetiracetam. DISCUSSION: Patients with COVID-19 disease are at an elevated risk for seizures, and the mechanism of these seizures is likely multifactorial. Clinical (motor) seizures may not be readily detected in this population due to the expansive utilization of sedatives and paralytics for respiratory optimization strategies. Many of these patients are also not electrographically monitored for seizures due to limited resources, multifactorial risk for acute encephalopathy, and the risk of cross-contamination. Previously, several neurological symptoms were seen in patients with more advanced COVID-19 disease, and these were thought to be secondary to multi-system organ failure and/or disseminated intravascular coagulopathy-related brain injury. However, these patients may also have an advanced breakdown of the blood-brain barrier precipitated by pro-inflammatory cytokine reactions. The neurotropic effect and neuroinvasiveness of SARS-Coronavirus-2 have not been directly established. CONCLUSIONS: Acute symptomatic seizures are possible in patients with COVID-19 disease. These seizures are likely multifactorial in origin, including cortical irritation due to blood-brain barrier breakdown, precipitated by the cytokine reaction as a part of the viral infection. Patients with clinical signs of seizures or otherwise unexplained encephalopathy may benefit from electroencephalography monitoring and/or empiric anti-epileptic therapy. Further studies are needed to elucidate the risk of seizures and benefit of monitoring in this population.

New onset epilepsy among patients with periodic discharges on continuous electroencephalographic monitoring.

OBJECTIVE: To evaluate the incidence of new onset epilepsy and associated risk factors in patients with periodic patterns on continuous electroencephalography (cEEG) during critical illness. METHODS: The local cEEG database and then medical records were reviewed from January 1, 2013 to June 30, 2013 to find adult patients with no history of epilepsy who had periodic discharges-either lateralized (LPDs) or generalized (GPDs)-or nonperiodic/nonepileptogenic (NP/NE) findings on cEEG and ≥3 months of clinical follow-up. Clinical seizure after discharge was the primary outcome. Chi-square test, Kruskal-Wallis test, and Cox proportional hazards models were used for statistical analysis. RESULTS: A total of 195 patients (median age = 67.8 years) were included. There were 53 (27%), 73 (37%), and 69 (35%) patients with LPDs, GPDs, and NP/NE findings on cEEG, respectively. These three groups did not differ by demographic or clinical variables. A total of 29 (15%) patients (LPDs = 20 [38%], GPDs = 4 [6%], and NP/NE = 5 [7%]) developed epilepsy during a median follow-up of 32.1 (95% confidence interval [CI] = 13.2-42.8) months. The hazard ratio for epilepsy development among LPD patients was 7.7 (95% CI = 2.9-20.7) times compared to the NP/NE group, and the risk further increased to 11.4 (95% CI = 4-31.4) times if they also had electrographic seizures. This association remained significant despite adjusting for each covariate at a time. SIGNIFICANCE: Patients with LPDs on cEEG during critical illness are at least seven times more likely to develop epilepsy compared to patients with NP/NE findings. This risk is further increased if patients with LPDs have electrographic seizures. In comparison, the presence of GPDs does not seem to impact the risk for developing epilepsy. cEEG findings at the time of acute insult have potential to serve as prognostic biomarkers for epilepsy development.

Electro-Radiological Observations of Grade III/IV Hepatic Encephalopathy Patients with Seizures.

BACKGROUND: Neurological complications in liver failure are common. Often under-recognized neurological complications are seizures and status epilepticus. These may go unrecognized without continuous electroencephalography (CEEG). We highlight the observed electro-radiological changes in patients with grade III/IV hepatic encephalopathy (HE) found to have seizures and/or status epilepticus on CEEG and the associated neuroimaging. METHODS: This study was a retrospective review of patients with West Haven grade III/IV HE and seizures/status epilepticus on CEEG. RESULTS: Eleven patients were included. Alcohol was the most common cause of HE (54.5%). All patients were either stuporous/comatose. The most common CEEG pattern was diffuse slowing (100%) followed by generalized periodic discharges (GPDs; 36.4%) and lateralized periodic discharges (LPDs, 36.4%). The subtype of GPDs with triphasic morphology was only seen in 27.3%. All seizures and/or status epilepticus were without clinical signs. Magnetic resonance imaging (MRI) was available in six patients. Cortical hyperintensities on diffusion weighted imaging sequence were seen in all six patients. One patient had CEEG seizure concomitantly with the MRI. Seven patients died prior to discharge. CONCLUSION: Seizures or status epilepticus in the setting of HE were without clinical findings and could go unrecognized without CEEG. The finding of cortical hyperintensity on MRI should lead to further evaluation for unrecognized seizure or status epilepticus.

Incidence of recurrent seizures following hospital discharge in patients with LPDs (PLEDs) and nonconvulsive seizures recorded on continuous EEG in the critical care setting.

PURPOSE: Continuous EEG (cEEG) has helped to identify nonconvulsive seizures (NCS) and nonconvulsive status epilepticus (NCSE) along with lateralized periodic patterns (LPDs or PLEDs) in ICU patients with much higher frequency than previously appreciated, but understanding their implications may be more complex. The aim of this study was to investigate the incidence of recurrent seizures after hospital discharge and their associated factors in patients with PLEDs and NCS in the critical care setting. METHODS: After IRB approval, we used our EEG reporting database to find 200 consecutive patients who had PLEDs and/or NCSs on cEEG. Patients with less than 3 months of follow-up were excluded. Remaining patients were divided into three groups: PLEDs+Seizure (NCS/NCSE), PLEDs only, and Seizures (NCS/NCSE) only. Medical records were reviewed to gather demographical and clinical details. Univariate data analysis was done using JMP 9.0 (Marlow, Buckinghamshire, UK). RESULTS: There were 51 patients in 'PLEDs+Seizure' group, 45 in 'PLEDs only' group, and 22 in 'Seizure only' group. Ischemic stroke, hemorrhage, and tumors were the top three etiologies. Nearly 47% of our study population had postdischarge seizures during a mean follow-up period of 11.9 (+/-6) months. We found that 24.4% of patients in the PLEDs only group had seizures after discharge, which increased to 60.7% if they had seizures as well during their ICU stay. Slightly more than 52% of patients had a postdischarge EEG, of which, 59% was in the form of inpatient cEEG during a rehospitalization, accounting for 30.5% of the total study population. It was an indicator of high readmission rates in this population. CONCLUSION: Almost every other patient with PLEDs and/or NCS on cEEG had seizures after ICU discharge. A quarter of patients on cEEG in the ICU with PLEDs alone had seizures after discharge, and after excluding prior epilepsy, 17% of patients with PLEDs had seizures on follow-up. This was dramatically increased with the recording of PLEDs with NCS, with 60% of patients having seizures after discharge from the ICU and 48% of patients after excluding prior epilepsy. Patients with NCS on cEEG alone had 63% chance of seizure recurrence that dropped to 38% with exclusion of prior epilepsy. Future studies are needed to define the postdischarge outcomes including seizure recurrence in this patient population. This article is part of a Special Issue entitled "Status Epilepticus".

Characteristics and Attendance of Patients Eligible for the PASS Clinic: A Transition of Care Model After Acute Symptomatic Seizures.

Background and Objectives: Most acute symptomatic seizure (ASyS) patients stay on antiseizure medications (ASM) long-term, despite low epilepsy development risk. The Post-Acute Symptomatic Seizure (PASS) clinic is a transition of care model for ASyS patients who individualize ASM management with the goal of a safe deprescription. We evaluated patients discharged on ASMs after a witnessed or suspected ASyS to analyze their PASS clinic visit attendance and its predictors. Methods: A single-center, retrospective cohort study of adults without epilepsy who were discharged from January 1, 2019, to September 30, 2019, on first-time ASMs due to witnessed or suspected ASyS (PASS clinic-eligible). We fit a cause-specific Cox proportional hazards model to analyze factors associated with PASS clinic attendance, which depends on survival in this patient population that has a high early postdischarge mortality (a competing risk). We checked for multicollinearity and the assumption of proportional hazards. Results: Among 307 PASS clinic-eligible patients, 95 (30.9%) attended the clinic and 136 (44.3%) died during a median follow-up of 14 months (interquartile range = 2-34). ASyS occurred in 60.2% (convulsive 47%; electrographic 26.7%) of patients. ASMs were continued in the absence of ASyS or epileptiform abnormalities (EAs) in 27% of patients. Multivariable analysis revealed that the presence of EAs (HR = 1.69, 95% CI 1.10-2.59), PASS clinic appointments provided before discharge (HR = 3.39, 95% CI 2.15-5.33), and less frequently noted ASyS etiologies such as autoimmune encephalitis (HR = 2.03, 95% CI 1.07-3.86) were associated with an increased clinic attendance rate. Medicare/Medicaid insurance (HR = 0.43, 95% CI 0.24-0.78, p = 0.005) and the presence of progressive brain injury (i.e., tumors; HR = 0.55, 95% CI 0.32-0.95, p = 0.032) were associated with reduced rate of PASS clinic attendance. Discussion: Our real-world data highlight the need for appropriate postdischarge follow-up of ASyS patients, which can be fulfilled by the PASS clinic model. Modest PASS clinic attendance can be significantly improved by adhering to a structured discharge planning process whereby appointments are provided before discharge. Future research comparing patient outcomes, specifically safe ASM discontinuation in a PASS clinic model to routine clinical care, is needed.

Indications for continuous electroencephalographic (cEEG) monitoring: What do they tell us?

OBJECTIVE: While studies have explored clinical and EEG predictors of seizures on continuous EEG (cEEG), the role of cEEG indications as predictors of seizures has not been studied. Our study aims to fill this knowledge gap. METHODS: We used the prospective cEEG database at Cleveland Clinic for the 2016 calendar year. Patients ≥ 18 years who underwent cEEG for the indication of altered mental status (AMS) and seizure-like events (SLE: motor or patient-reported events) were included. Baseline characteristics and EEG findings were compared between the two groups. Multivariable regression was used to compare the two groups and identify seizure detection risk factors. RESULTS: Of 2227 patients (mean age 59.4 years) who met the inclusion criteria, 882 (50% females) underwent cEEG for AMS and 1345(51% females) for SLE. SLE patients were younger(OR: 0.988, CI: 0.98-0.99, p < 0.001), had longer monitoring(OR:1.04, CI:1.00-1.07, p = 0.033), were more likely to have epilepsy-related-breakthrough seizures(OR:25.9, CI:0.5.89-115, p < 0.001), psychogenic non-epileptic spells (OR:6.85, CI:1.60-29.3, p = 0.008), were more awake (p < 0.001) and more likely to be on anti-seizure medications(OR:1.60, CI:1.29-1.98, p < 0.001). On multivariable analysis, SLE was an independent predictor of seizure detection (OR: 2.60, CI: 1.77-3.88, p < 0.001). SIGNIFICANCE: Our findings highlight the differences in patients undergoing cEEG for AMS vs. SLE. SLE as a cEEG indication represents an independent predictor of seizures on cEEG and, therefore, deserves special attention. Future multicenter studies are needed to validate our findings.

Outpatient EEG in Routine Clinical Care of Patients With Stroke-Related Acute Symptomatic Seizure Concerns.

PURPOSE: Acute symptomatic seizures (ASyS) after stroke contribute the highest risk to poststroke epilepsy (PSE) development. We investigated the use of outpatient EEG (oEEG) among stroke patients with ASyS concerns. METHODS: Adults with acute stroke, ASyS concerns (underwent cEEG), and outpatient clinical follow-up were included (study population). Patients with oEEG (oEEG cohort) were analyzed for electrographic findings. Univariable and multivariable analyses helped identify predictors of oEEG use in routine clinical care. RESULTS: Among 507 patients, 83 (16.4%) underwent oEEG. The independent predictors of oEEG utilization included age (OR = 1.03 [1.01 to 1.05, P = 0.01]), electrographic ASyS on cEEG (OR 3.9 [1.77 to 8.9], P < 0.001), ASMs at discharge (OR 3.6 [1.9 to 6.6], P < 0.001), PSE development (OR 6.6 [3.5 to 12.6], P < 0.001), and follow-up duration (OR = 1.01 [1.002 to 1.02], P = 0.016). Almost 40% of oEEG cohort developed PSE, but only 12% had epileptiform abnormalities. Close to a quarter (23%) of oEEGs were within normal limits. CONCLUSIONS: One in six patients with ASyS concern after stroke undergoes oEEG. Electrographic ASyS, PSE development, and ASM at discharge are primary drivers of oEEG use. While PSE drives oEEG use, we need systematic, prospective investigation of outpatient EEG's role as prognostic tool for PSE development.

Risk factors that predict delayed seizure detection on continuous electroencephalogram (cEEG) in a large sample size of critically ill patients.

OBJECTIVE: Majority of seizures are detected within 24 hours on continuous EEG (cEEG). Some patients have delayed seizure detection after 24 hours. The purpose of this research was to identify risk factors that predict delayed seizure detection and to determine optimal cEEG duration for various patient subpopulations. METHODS: We retrospectively identified all patients ≥18 years of age who underwent cEEG at Cleveland clinic during calendar year 2016. Clinical and EEG data for all patients and time to seizure detection for seizure patients were collected. RESULTS: Twenty-four hundred and two patients met inclusion criteria. Of these, 316 (13.2%) had subclinical seizures. Sixty-five (20.6%) patients had delayed seizures detection after 24 hours. Seizure detection increased linearly till 36 hours of monitoring, and odds of seizure detection increased by 46% for every additional day of monitoring. Delayed seizure risk factors included stupor (13.2% after 48 hours, P = .031), lethargy (25.9%, P = .013), lateralized (LPDs) (27.7%, P = .029) or generalized periodic discharges (GPDs) (33.3%, P = .022), acute brain insults (25.5%, P = .036), brain bleeds (32.8%, P = .014), especially multiple concomitant bleeds (61.1%, P < .001), altered mental status (34.7%, P = .001) as primary cEEG indication, and use of antiseizure medications (27.8%, P < .001) at cEEG initiation. SIGNIFICANCE: Given the linear seizure detection trend, 36 hours of standard monitoring appears more optimal than 24 hours especially for high-risk patients. For awake patients without epileptiform discharges, <24 hours of monitoring appears sufficient. Previous studies have shown that coma and LPDs predict delayed seizure detection. We found that stupor and lethargy were also associated with delayed seizure detection. LPDs and GPDs were associated with delayed seizures. Other delayed seizure risk factors included acute brain insults, brain bleeds especially multiple concomitant bleeds, altered mental status as primary cEEG indication, and use of ASMs at cEEG initiation. Longer cEEG (≥48 hours) is suggested for these high-risk patients.

Seizure Risk in Patients Undergoing 30-Day Readmission After Continuous EEG Monitoring.

PURPOSE: The use of continuous electroencephalographic (cEEG) monitoring has improved the understanding of the seizure risk during acute hospitalization. However, the immediate posthospitalization seizure risk in these patients remains unknown. Patients undergoing 30-day readmission after initial cEEG monitoring were analyzed to fill this knowledge gap. METHODS: A prospectively maintained cEEG database (January 1, 2015-December 31, 2015) was used to identify adults who underwent a repeat cEEG during their 30-day readmission after cEEG during their index hospitalization (index cEEG). Various demographical, clinical, and cEEG variables were extracted including indication for cEEG: altered mental status and clinical seizure-like events. RESULTS: A total of 57 of the 2,485 (2.3%) adults undergoing index cEEG during the study period had concerns for seizures and underwent repeat cEEG during a 30-day readmission. These patients were almost three times more likely to have suffered electrographic seizure on the index admission (odds ratio, 2.82; 95% confidence interval, 1.54-5.15; P < 0.001) compared with non-readmitted patients. Seizure-like events led to the readmission of 40.4% patients. Close to one in five (19.3%) readmitted patients were found to have an electrographic seizure. Only variable predictive of seizure on readmission was seizure-like events (odds ratio, 6.4; 95% confidence interval, 1.2-33.0; P = 0.02). CONCLUSIONS: A small percentage of patients have clinical presentation concerning for seizures with in 30 days after index cEEG. The risk of electrographic seizures in this patient population is higher than patients who have cEEG monitoring but do not undergo a 30-day readmission requiring repeat cEEG. Future research on early identification of patients at risk of 30-day readmission because of concerns for seizure is needed.

Acute Symptomatic Seizure Associated With Chronic Antiseizure Medication Use After Stroke.

Background and Objective: Patients with acute symptomatic seizures (ASyS) after stroke are discharged on antiseizure medications (ASMs) and stay on them for an extended period. We analyzed the current ASM management practice, 6 months, and at the last follow-up after stroke-related ASyS concerns to identify chronic and long-term ASM use predictors. Methods: A single-center, retrospective cohort study of adults who underwent continuous EEG monitoring for ASyS concerns after stroke (April 1, 2012 to March 31, 2018) with at least 6 months of follow-up was performed. ASM use beyond 6 months after the initial ASyS concern was defined as "chronic" among patients discharged on them. "Long-term" ASM use at the last follow-up in all patients with ASyS concerns was analyzed. Logistic regression and Cox regression multivariable modeling to analyze predictors of "chronic" and "long-term" ASM use, respectively, was performed. Results: A total of 465 (mean age 61.7 ± 13.3 years and 52% female patients) patients (41.9% ischemic stroke, 36.1% intracerebral hemorrhage, and 21.9% subarachnoid hemorrhage) were included. Of the 179 (38.5%) patients discharged on ASMs, 132 (73.7%; 28.4% of study population) had chronic ASM use, despite 90% not experiencing any seizure (poststroke epilepsy [PSE]) during this time. The independent predictors of chronic ASM use were electrographic ASyS (odds ratio [OR] = 9.27, 95% CI = 2.53-60.4) and female sex (OR = 2.2, 95% CI = 1.02-4.83). After a median 61-month (5.1 years) follow-up, 101 (21.7%) patients in the study population were on long-term ASM use, including 67 (14.4%) who developed PSE. Long-term ASM use was associated with NIH Stroke Scale Score (OR = 1.5, 95% CI = 1.015-1.98), cortical involvement (OR = 1.28, 95% CI = 1.02-1.6), convulsive ASyS (OR = 1.46, 95% CI = 1.02-2.09), epileptiform findings on outpatient EEG (OR = 4.03, 95% CI = 1.28-12.76), and PSE development (OR = 7.06, 95% CI = 3.7-13.4). Discussion: Chronic ASM use is highly associated with electrographic, rather than convulsive, ASyS. However, long-term ASM use is independently associated with PSE and its risk factors, including convulsive ASyS. With the ubiquity of stroke-related ASyS concerns in routine clinical practice, comparative effectiveness studies to guide ASM management are needed.

Acute epileptiform abnormalities are the primary predictors of post-stroke epilepsy: a matched, case-control study.

Stroke patients who underwent continuous EEG (cEEG) monitoring within 7 days of presentation and developed post-stroke epilepsy (PSE; cases, n = 36) were matched (1:2 ratio) by age and follow-up duration with ones who did not (controls, n = 72). Variables significant on univariable analysis [hypertension, smoking, hemorrhagic conversion, pre-cEEG convulsive seizures, and epileptiform abnormalities (EAs)] were included in the multivariable logistic model and only the presence of EAs on EEG remained significant PSE predictor [OR = 11.9 (1.75-491.6)]. With acute EAs independently predicting PSE development, accounting for their presence may help to tailor post-acute symptomatic seizure management and aid anti-epileptogenesis therapy trials.

Ictal singing due to left frontal lobe epilepsy: a case report and review of the literature.

Ictal singing has been rarely reported and the neural networks underlying this specific symptom remain unknown. We report a nineteen-year-old man with medically refractory seizures who exhibited ictal singing and laughing. He underwent intracranial stereotactic EEG recording which demonstrated ictal activity in medial and dorsolateral regions of the left frontal lobe in the generation of ictal singing. Thereafter, a left frontal resection of the superior and middle frontal gyri made him seizure-free. Among the previously reported cases of ictal singing, the symptomatogenic zones included bilateral frontal and temporal lobes. The wide variance of ictal onset zones suggests that the mechanism of ictal singing is probably related to the recruitment of music-related neural networks in different regions of both hemispheres rather than activation of a specific cortical region.

Characterization of Postanoxic Tonic Eyelid Opening: A Poorly Recognized Prognostic Sign.

BACKGROUND: Postanoxic myoclonus is a known poor prognostic sign, and other postanoxic spontaneous movements have been reported but poorly described. We aim to describe the electroclinical phenomenon of postanoxic eyelid openings in context of its possible prognostic value. METHODS: We collected clinical data on postcardiac arrest patients with suspicious eyelid movements noted on continuous EEG monitoring. The eyelid movements captured on the video were correlated with the EEG findings and final clinical outcome. Neuroimaging data were reviewed when available. We also conducted a thorough literature review on this topic. RESULTS: A total of 10 patients (5 females) with average age of 56.1 (±14.4) years were included. The mean cardiopulmonary resuscitation duration was 18.9 (±11.3) minutes. Postanoxic eyelid-opening movements occurred at variable intervals (0.5-570 seconds) in each individual. Close examination of eyelid opening (available in 6 patients) revealed them to be tonic movements, lasting an average of 3 (±0.8) seconds and always succeeded the onset of burst of EEG activity in a burst-suppression background. This is a transient phenomenon, lasting a median duration of 30 (interquartile range 7.75-36) hours. MRI findings in 3 patients demonstrated diffuse cortical ischemic injury with relative sparing of the brainstem. All patients died within 2-7 days following cardiac arrest. CONCLUSIONS: Contrary to previous descriptions, the postanoxic tonic eyelid openings (PATEO) are repetitive but nonperiodic, nonmyoclonic movements. Their close and specific temporal correlation with the burst of EEG activity suggests that this could be considered an ictal phenomenon requiring an intact midbrain based on MRI findings.

Long-term electro-clinical profile of sudden cardiac arrest survivors.

OBJECTIVE: Recent research has explored the use of continuous EEG (cEEG) monitoring for prognostication of spontaneous cardiac arrest (SCA). However, there is limited literature on the long-term (post-hospital discharge) electrographic findings among SCA survivors and their clinical correlates. Our study aims to fill this critical knowledge gap. METHODS: We retrospectively used our EEG database to identify adults (≥18 years) with SCA history who underwent an outpatient laboratory-based EEG between 01/01/2011 and 12/31/2018. After electronic medical records (EMR) review, patients with epilepsy history and unclear/poorly documented SCA history were excluded. Outpatient EEGs were reviewed by authors. Acute EEG findings were extracted from the EEG database and EMR. In addition, we extracted data on acute and long-term neuroimaging findings (CT/MRI), post-SCA seizures, and anti-seizure medications (ASM) status. Descriptive analysis and Fisher's exact test were performed. RESULTS: We included 32 SCA survivors (50% women; mean age = 52.1 ± 13.6 years) in the study. During a median clinical follow-up of 28.2 months, 3 patients suffered only clinical seizures, 3 only chronic post-hypoxic myoclonus, and 5 had both [11 (34.4%) in total]. Interictal epileptiform discharges (IEDs) were noted in one-third of the patients, which localized to vertex and frontocentral regions in all but one patient. Five (15.6%) of them did not suffer a clinical seizure despite the presence of EAs. Patients who developed epilepsy were significantly more likely to have abnormal neuroimaging findings [10/11 (90.9%)] during the follow-up compared to the rest of the patients [OR = 25 (95% CI 2.6->100, P = .002)]. Half of the study cohort was taking ASM at the last follow-up. SIGNIFICANCE: Our small study reveals a signature location of IEDs in SCA survivors. Neuroimaging abnormalities seem to be a better indicator of epilepsy development, while EEG may reveal markers of potential epileptogenicity in the absence of clinical seizures. Future, larger studies are needed to confirm our findings.

Long-term continuation of anti-seizure medications after acute stroke.

OBJECTIVE: To investigate the factors associated with the long-term continuation of anti-seizure medications (ASMs) in acute stroke patients. METHODS: We performed a retrospective cohort study of stroke patients with concern for acute symptomatic seizures (ASySs) during hospitalization who subsequently visited the poststroke clinic. All patients had continuous EEG (cEEG) monitoring. We generated a multivariable logistic regression model to analyze the factors associated with the primary outcome of continued ASM use after the first poststroke clinic visit. RESULTS: A total of 507 patients (43.4% ischemic stroke, 35.7% intracerebral hemorrhage, and 20.9% aneurysmal subarachnoid hemorrhage) were included. Among them, 99 (19.5%) suffered from ASySs, 110 (21.7%) had epileptiform abnormalities (EAs) on cEEG, and 339 (66.9%) had neither. Of the 294 (58%) patients started on ASMs, 171 (33.7%) were discharged on them, and 156 (30.3% of the study population; 53.1% of patients started on ASMs) continued ASMs beyond the first poststroke clinic visit [49.7 (±31.7) days after cEEG]. After adjusting for demographical, stroke- and hospitalization-related variables, the only independent factors associated with the primary outcome were admission to the NICU [Odds ratio (OR) 0.37 (95% CI 0.15-0.9)], the presence of ASySs [OR 20.31(95% CI 9.45-48.43)], and EAs on cEEG [OR 2.26 (95% CI 1.14-4.58)]. INTERPRETATION: Almost a third of patients with poststroke ASySs concerns may continue ASMs for the long term, including more than half started on them acutely. Admission to the NICU may lower the odds, and ASySs (convulsive or electrographic) and EAs on cEEG significantly increase the odds of long-term ASM use.