Predictors of follow-up care for critically-ill patients with seizures and epileptiform abnormalities on EEG monitoring.

OBJECTIVE: Post-hospitalization follow-up visits are crucial for preventing long-term complications. Patients with electrographic epileptiform abnormalities (EA) including seizures and periodic and rhythmic patterns are especially in need of follow-up for long-term seizure risk stratification and medication management. We sought to identify predictors of follow-up. METHODS: This is a retrospective cohort study of all patients (age ≥ 18 years) admitted to intensive care units that underwent continuous EEG (cEEG) monitoring at a single center between 01/2016-12/2019. Patients with EAs were included. Clinical and demographic variables were recorded. Follow-up status was determined using visit records 6-month post discharge, and visits were stratified as outpatient follow-up, neurology follow-up, and inpatient readmission. Lasso feature selection analysis was performed. RESULTS: 723 patients (53 % female, mean (std) age of 62.3 (16.4) years) were identified from cEEG records with 575 (79 %) surviving to discharge. Of those discharged, 450 (78 %) had outpatient follow-up, 316 (55 %) had a neurology follow-up, and 288 (50 %) were readmitted during the 6-month period. Discharge on antiseizure medications (ASM), younger age, admission to neurosurgery, and proximity to the hospital were predictors of neurology follow-up visits. Discharge on ASMs, along with longer length of stay, younger age, emergency admissions, and higher illness severity were predictors of readmission. SIGNIFICANCE: ASMs at discharge, demographics (age, address), hospital care teams, and illness severity determine probability of follow-up. Parameters identified in this study may help healthcare systems develop interventions to improve care transitions for critically-ill patients with seizures and other EA.

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.

Immediate and long-term management practices of acute symptomatic seizures and epileptiform abnormalities: A cross-sectional international survey.

OBJECTIVES: Acute symptomatic seizures (ASyS) and epileptiform abnormalities (EAs) on electroencephalography (EEG) are commonly encountered following acute brain injury. Their immediate and long-term management remains poorly investigated. We conducted an international survey to understand their current management. METHODS: The cross-sectional web-based survey of 21 fixed-response questions was based on a common clinical encounter: convulsive or suspected ASyS following an acute brain injury. Respondents selected the option that best matched their real-world practice. Respondents completing the survey were compared with those who accessed but did not complete it. RESULTS: A total of 783 individuals (44 countries) accessed the survey; 502 completed it. Almost everyone used anti-seizure medications (ASMs) for secondary prophylaxis after convulsive or electrographic ASyS (95.4% and 97.2%, respectively). ASM dose escalation after convulsive ASyS depends on continuous EEG (cEEG) findings: most often increased after electrographic seizures (78% of respondents), followed by lateralized periodic discharges (LPDs; 41%) and sporadic epileptiform discharges (sEDs; 17.5%). If cEEG is unrevealing, one in five respondents discontinue ASMs after a week. In the absence of convulsive and electrographic ASyS, a large proportion of respondents start ASMs due to LPD (66.7%) and sED (44%) on cEEG. At hospital discharge, most respondents (85%) continue ASM without dose change. The recommended duration of outpatient ASM use is as follows: 1-3 months (36%), 3-6 months (30%), 6-12 months (13%), >12 months (11%). Nearly one-third of respondents utilized ancillary testing before outpatient ASM taper, most commonly (79%) a <2 h EEG. Approximately half of respondents had driving restrictions recommended for 6 months after discharge. SIGNIFICANCE: ASM use for secondary prophylaxis after convulsive and electrographic ASyS is a universal practice and is continued upon discharge. Outpatient care, particularly the ASM duration, varies significantly. Wide practice heterogeneity in managing acute EAs reflects uncertainty about their significance and management. These results highlight the need for a structured outpatient follow-up and optimized care pathway for patients with ASyS.

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.

Longitudinal Disability, Cognitive Impairment, and Mood Symptoms in Patients With Anti-NMDA Receptor Encephalitis.

BACKGROUND AND OBJECTIVES: Longitudinal outcomes in anti-NMDA receptor encephalitis (anti-NMDARe) are still not fully understood and may not be adequately captured with the modified Rankin Scale (mRS), often the sole reported outcome. We aim to characterize longitudinal outcomes in anti-NMDARe using multiple outcome measures. METHODS: This single-center, retrospective, observational study examined outcome measures (mRS and Clinical Assessment Scale in Autoimmune Encephalitis [CASE]) in adults with NMDA receptor-IgG in CSF at short- and long-term follow-ups using linear and logistic regression modeling. Patients with evaluations for cognitive impairment (Montreal Cognitive Assessment/Mini-Mental State Examination), depression (Patient Health Questionnaire-9), and anxiety (General Anxiety Disorder-7) >6 months from symptom onset were correlated with final CASE scores. RESULTS: Thirty-eight patients (76% female, median disease onset age = 28 years, range = 1-75 years) were included. The majority received first-line immunosuppressants (97%) at a median of 3.9 weeks (interquartile range [IQR] = 2.1-9.7) from symptom onset and 68% received second-line therapies. At baseline, median/mean mRS and CASE were 4 (IQR = 3-5) and 12.9 (SD = 7.2), respectively. At short-term follow-up (median = 10 weeks, IQR = 6-17), factors associated with higher CASE and mRS included dysautonomia, coma/lethargy, seizures/status epilepticus, and intensive care unit admission (p < 0.05). At long-term follow-up (median = 70 weeks, IQR = 51-174), median/mean mRS and CASE were 2 (IQR = 1-3) and 4.4 (SD = 4.2), respectively. Only weakness at symptom onset predicted higher mRS scores (odds ratio = 5.6, 95% confidence interval 1.02-30.9, p = 0.047). Despite both mRS and CASE improving from baseline (p < 0.001), only 9 patients (31%) returned to their premorbid function. Among patients with cognitive and mood evaluations >6 months from onset, moderate-severe cognitive impairment (42%), depression (28%), and anxiety (30%) were frequent. Cognitive and depression measures were associated with final CASE subscores (including memory, language, weakness, and psychiatric). DISCUSSION: Multiple clinical factors influenced short-term outcomes, but only onset weakness influenced long-term mRS, highlighting that mRS is predominantly affected by global motor function. Although mRS and CASE improved over time for most patients, these outcome measures did not capture the full extent of long-term functional impairment in terms of mood, cognition, and the ability to return to premorbid function. This emphasizes the need for increased utilization of more nuanced cognitive and mood outcome measures.

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.

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.

SARS-CoV-2 alters neural synchronies in the brain with more severe effects in younger individuals.

Coronavirus disease secondary to infection by SARS-CoV-2 (COVID19 or C19) causes respiratory illness, as well as severe neurological symptoms that have not been fully characterized. In a previous study, we developed a computational pipeline for the automated, rapid, high-throughput and objective analysis of electroencephalography (EEG) rhythms. In this retrospective study, we used this pipeline to define the quantitative EEG changes in patients with a PCR-positive diagnosis of C19 (n = 31) in the intensive care unit (ICU) of Cleveland Clinic, compared to a group of age-matched PCR-negative (n = 38) control patients in the same ICU setting. Qualitative assessment of EEG by two independent teams of electroencephalographers confirmed prior reports with regards to the high prevalence of diffuse encephalopathy in C19 patients, although the diagnosis of encephalopathy was inconsistent between teams. Quantitative analysis of EEG showed distinct slowing of brain rhythms in C19 patients compared to control (enhanced delta power and attenuated alpha-beta power). Surprisingly, these C19-related changes in EEG power were more prominent in patients below age 70. Moreover, machine learning algorithms showed consistently higher accuracy in the binary classification of patients as C19 versus control using EEG power for subjects below age 70 compared to older ones, providing further evidence for the more severe impact of SARS-CoV-2 on brain rhythms in younger individuals irrespective of PCR diagnosis or symptomatology, and raising concerns over potential long-term effects of C19 on brain physiology in the adult population and the utility of EEG monitoring in C19 patients.

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.

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 6 months 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 3 T 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.

Extreme late onset of genetic generalized epilepsy in older adults and the elderly: A cohort study and literature review.

Rare case reports describe genetic generalized epilepsy (GGE) starting de novo in people ≥50 years of age (older adults and the elderly). We aimed to provide comprehensive detail of electro-clinical findings of this extremely late-onset GGE using a retrospective, single-center cohort design and a systematic review of the literature. People with de novo seizure onset ≥50 years of age with EEG and clinical history consistent with GGE were included. These 12 individuals (9; 75% females) with a median age of 56 years at seizure onset accounted for 7.9% of 152 older adults and the elderly with generalized epilepsy. Three patients only had absence seizures. A family history of epilepsy was present in 5 individuals. They had tried a median of 2 anti-seizure medications. More than 90% (11 of 12) were seizure-free for >1 year at the last follow-up, including four requiring monotherapy. Valproate was used in only two patients and levetiracetam in 75% of them. A systematic literature review revealed six papers with 10 extreme late-onset GGE cases. They similarly had good seizure outcomes but a majority were on valproate. Our study shows that rarely, late-onset epilepsy can be GGE, which mostly has a good prognosis.

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.

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 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.