Time-dependent risk of seizures in critically ill patients on continuous electroencephalogram

Seizure Assessment and Forecasting With Efficient Rapid-EEG: A Retrospective Multicenter Comparative Effectiveness Study

BACKGROUND AND OBJECTIVES

Approximately 30% of critically ill patients have seizures, and more than half of these seizures do not have an overt clinical correlate. EEG is needed to avoid missing seizures and prevent overtreatment with antiseizure medications. Conventional-EEG (cEEG) resources are logistically constrained and unable to meet their growing demand for seizure detection even in highly developed centers. Brief EEG screening with the validated 2HELPS2B algorithm was proposed as a method to triage cEEG resources, but it is hampered by cEEG requirements, primarily EEG technologists. Seizure risk-stratification using reduced time-to-application rapid response-EEG (rrEEG) systems (∼5 minutes) could be a solution. We assessed the noninferiority of the 2HELPS2B score on a 1-hour rrEEG compared to cEEG.

METHODS

A multicenter retrospective EEG diagnostic accuracy study was conducted from October 1, 2021, to July 31, 2022. Chart and EEG review performed with consecutive sampling at 4 tertiary care centers, included records of patients ≥18 years old, from January 1, 2018, to June 20, 2022. Monte Carlo simulation power analysis yielded n = 500 rrEEG; for secondary outcomes n = 500 cEEG and propensity-score covariate matching was planned. Primary outcome, noninferiority of rrEEG for seizure risk prediction, was assessed per area under the receiver operator characteristic curve (AUC). Noninferiority margin (0.05) was based on the 2HELPS2B validation study.

RESULTS

A total of 240 rrEEG with follow-on cEEG were obtained. Median age was 64 (interquartile range 22); 42% were female. 2HELPS2B on a 1-hour rrEEG met noninferiority to cEEG (AUC 0.85, 95% CI 0.78-0.90, p = 0.001). Secondary endpoints of comparison with a matched contemporaneous cEEG showed no significant difference in AUC (0.89, 95% CI 0.83-0.94, p = 0.31); in false negative rate for the 2HELPS2B = 0 group (p = 1.0) rrEEG (0.021, 95% CI 0-0.062), cEEG (0.016, 95% CI 0-0.048); nor in survival analyses.

DISCUSSION

2HELPS2B on 1-hour rrEEG is noninferior to cEEG for seizure prediction. Patients with low-risk (2HELPS2B = 0) may be able to forgo prolonged cEEG, allowing for increased monitoring of at-risk patients.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that rrEEG is noninferior to cEEG in calculating the 2HELPS2B score to predict seizure risk.

Periodic Discharges in Critically Ill Children: Predictors and Outcome

OBJECTIVES

We aimed to identify clinical and EEG monitoring characteristics associated with generalized, lateralized, and bilateral-independent periodic discharges (GPDs, LPDs, and BIPDs) and to determine which patterns were associated with outcomes in critically ill children.

METHODS

We performed a prospective observational study of consecutive critically ill children undergoing continuous EEG monitoring, including standardized scoring of GPDs, LPDs, and BIPDs. We identified variables associated with GPDs, LPDs, and BIPDs and assessed whether each pattern was associated with hospital discharge outcomes including the Glasgow Outcome Scale-Extended Pediatric version (GOS-E-Peds), Pediatric Cerebral Performance Category (PCPC), and mortality.

RESULTS

PDs occurred in 7% (91/1,399) of subjects. Multivariable logistic regression indicated that patients with coma (odds ratio [OR], 3.45; 95% confidence interval [CI]: 1.55, 7.68) and abnormal EEG background category (OR, 6.85; 95% CI: 3.37, 13.94) were at increased risk for GPDs. GPDs were associated with mortality (OR, 3.34; 95% CI: 1.24, 9.02) but not unfavorable GOS-E-Peds (OR, 1.93; 95% CI: 0.88, 4.23) or PCPC (OR, 1.64; 95% CI: 0.75, 3.58). Patients with acute nonstructural encephalopathy did not experience LPDs, and LPDs were not associated with mortality or unfavorable outcomes. BIPDs were associated with mortality (OR, 3.68; 95% CI: 1.14, 11.92), unfavorable GOS-E-Peds (OR, 5.00; 95% CI: 1.39, 18.00), and unfavorable PCPC (OR, 5.96; 95% CI: 1.65, 21.46).

SIGNIFICANCE

Patients with coma or more abnormal EEG background category had an increased risk for GPDs and BIPDs, and no patients with an acute nonstructural encephalopathy experienced LPDs. GPDs were associated with mortality and BIPDs were associated with mortality and unfavorable outcomes, but LPDs were not associated with unfavorable outcomes.

Residual Seizure Rate of Intermittent Inpatient EEG Compared to a Continuous EEG Model

BACKGROUND

Subclinical seizures are common in hospitalized patients and require electroencephalography (EEG) for detection and intervention. At our institution, continuous EEG (cEEG) is not available, but intermittent EEGs are subject to constant live interpretation. As part of quality improvement (QI), we sought to estimate the residual missed seizure rate at a typical quaternary Canadian health care center without cEEG.

METHODS

We calculated residual risk percentages using the clinically validated 2HELPS2B score to risk-stratify EEGs before deriving a risk percentage using a MATLAB calculator which modeled the risk decay curve for each recording. We generated a range of estimated residual seizure rates depending on whether a pre-cEEG screening EEG was simulated, EEGs showing seizures were included, or repeat EEGs on the same patient were excluded.

RESULTS

Over a 4-month QI period, 499 inpatient EEGs were scored as low (n = 125), medium (n = 123), and high (n = 251) seizure risk according to 2HELPS2B criteria. Median recording duration was 1:00:06 (interquartile range, IQR 30:40-2:21:10). The model with highest residual seizure rate included recordings with confirmed electrographic seizures (median 20.83%, IQR 20.6-26.6%), while the model with lowest residual seizure rate was in seizure-free recordings (median 10.59%, IQR 4%-20.6%). These rates were significantly higher than the benchmark 5% miss-rate threshold set by 2HELPS2B (p<0.0001).

CONCLUSIONS

We estimate that intermittent inpatient EEG misses 2-4 times more subclinical seizures than the 2HELPS2B-determined acceptable 5% seizure miss-rate threshold for cEEG. Future research is needed to determine the impact of potentially missed seizures on clinical care.

Continuous versus Routine Electroencephalography in the Intensive Care Unit: A Review of Current Evidence

BACKGROUND

Electroencephalography (EEG) has long been used to detect seizures in patients with disorders of consciousness. In recent years, there has been a drastically increased adoption of continuous EEG (cEEG) in the intensive care units (ICUs). Given the resources necessary to record and interpret cEEG, this is still not available in every center and widespread recommendations to use continuous instead of routine EEG (typically lasting 20 min) are still a matter of some debate. Considering recent literature and personal experience, this review offers a rationale and practical advice to address this question.

SUMMARY

Despite the development of increasingly performant imaging techniques and several validated biomarkers, EEG remains central to clinicians in the intensive care unit and has been experiencing expanding popularity for at least 2 decades. Not only does EEG allow seizure or status epilepticus detection, which in the ICU often present without clinical movements, but it is also paramount for the prognostic evaluation of comatose patients, especially after cardiac arrest, and for detecting delayed ischemia after subarachnoid hemorrhage. At the end of the last Century, improvements of technical and digital aspects regarding recording and storage of EEG tracings have progressively led to the era of cEEG and automated quantitative analysis.

KEY MESSAGES

As compared to repeated rEEG, cEEG in comatose patients does not seem to improve clinical prognosis to a relevant extent, despite allowing a more performant of detection ictal events and consequent therapeutic modifications. The choice between cEEG and rEEG must therefore always be patient-tailored.

Validation of a Model for Targeted EEG Monitoring Duration in Critically Ill Children

PURPOSE

Continuous EEG monitoring (CEEG) to identify electrographic seizures (ES) in critically ill children is resource intense. Targeted strategies could enhance implementation feasibility. We aimed to validate previously published findings regarding the optimal CEEG duration to identify ES in critically ill children.

METHODS

This was a prospective observational study of 1,399 consecutive critically ill children with encephalopathy. We validated the findings of a multistate survival model generated in a published cohort ( N = 719) in a new validation cohort ( N = 680). The model aimed to determine the CEEG duration at which there was <15%, <10%, <5%, or <2% risk of experiencing ES if CEEG were continued longer. The model included baseline clinical risk factors and emergent EEG risk factors.

RESULTS

A model aiming to determine the CEEG duration at which a patient had <10% risk of ES if CEEG were continued longer showed similar performance in the generation and validation cohorts. Patients without emergent EEG risk factors would undergo 7 hours of CEEG in both cohorts, whereas patients with emergent EEG risk factors would undergo 44 and 36 hours of CEEG in the generation and validation cohorts, respectively. The <10% risk of ES model would yield a 28% or 64% reduction in CEEG hours compared with guidelines recommending CEEG for 24 or 48 hours, respectively.

CONCLUSIONS

This model enables implementation of a data-driven strategy that targets CEEG duration based on readily available clinical and EEG variables. This approach could identify most critically ill children experiencing ES while optimizing CEEG use.

Clinical impact of carbapenems in critically ill patients with valproic acid therapy: A propensity-matched analysis

Background

Valproic acid (VPA) is one of the most widely used broad-spectrum antiepileptic drugs, and carbapenems (CBPs) remain the drug of choice for severe infection caused by multidrug-resistant bacteria in critically ill patients. The interaction between VPA and CBPs can lead to a rapid depletion of serum VPA level. This may then cause status epilepticus (SE), which is associated with significant mortality. However, the prognostic impact of drug interactions in critically ill patients remains an under-investigated issue.

Objective

The aim of this study was to compare the prognosis of critically ill patients treated with VPA and concomitant CBPs or other broad-spectrum antibiotics.

Methods

Adult patients admitted to a medical center intensive care unit between January 2007 and December 2017 who concomitantly received VPA and antibiotics were enrolled. The risk of reduced VPA serum concentration, seizures and SE, mortality rate, length of hospital stay (LOS), and healthcare expenditure after concomitant administration were analyzed after propensity score matching.

Results

A total of 1,277 patients were included in the study, of whom 264 (20.7%) concomitantly received VPA and CBPs. After matching, the patients who received CBPs were associated with lower VPA serum concentration (15.8 vs. 60.8 mg/L; p &lt; 0.0001), a higher risk of seizures (51.2 vs. 32.4%; adjusted odds ratio [aOR], 2.19; 95% CI, 1.48–3.24; p &lt; 0.0001), higher risk of SE (13.6 vs. 4.7%; aOR, 3.20; 95% CI, 1.51–6.74; p = 0.0014), higher in-hospital mortality rate (33.8 vs. 24.9%; aOR, 1.57; 95% CI, 1.03–2.20; p = 0.036), longer LOS after concomitant therapy (41 vs. 30 days; p &lt; 0.001), and increased healthcare expenditure (US$20,970 vs. US$12,848; p &lt; 0.0001) than those who received other broad-spectrum antibiotics.

Conclusion

The administration of CBPs in epileptic patients under VPA therapy was associated with lower VAP serum concentration, a higher risk of seizures and SE, mortality, longer LOS, and significant utilization of healthcare resources. Healthcare professionals should pay attention to the concomitant use of VPA and CBPs when treating patients with epilepsy. Further studies are warranted to investigate the reason for the poor outcomes and whether avoiding the co-administration of VPA and CBP can improve the outcomes of epileptic 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.

Continuous electroencephalography in the intensive care unit: A critical review and position statement from an Australian and New Zealand perspective

Objectives

This article aims to critically review the literature on continuous electroencephalography (cEEG) monitoring in the intensive care unit (ICU) from an Australian and New Zealand perspective and provide recommendations for clinicians.

Design and review methods

A taskforce of adult and paediatric neurologists, selected by the Epilepsy Society of Australia, reviewed the literature on cEEG for seizure detection in critically ill neonates, children, and adults in the ICU. The literature on routine EEG and cEEG for other indications was not reviewed. Following an evaluation of the evidence and discussion of controversial issues, consensus was reached, and a document that highlighted important clinical, practical, and economic considerations regarding cEEG in Australia and New Zealand was drafted.

Results

This review represents a summary of the literature and consensus opinion regarding the use of cEEG in the ICU for detection of seizures, highlighting gaps in evidence, practical problems with implementation, funding shortfalls, and areas for future research.

Conclusion

While cEEG detects electrographic seizures in a significant proportion of at-risk neonates, children, and adults in the ICU, conferring poorer neurological outcomes and guiding treatment in many settings, the health economic benefits of treating such seizures remain to be proven. Presently, cEEG in Australian and New Zealand ICUs is a largely unfunded clinical resource that is subsequently reserved for the highest-impact patient groups. Wider adoption of cEEG requires further research into impact on functional and health economic outcomes, education and training of the neurology and ICU teams involved, and securement of the necessary resources and funding to support the service.

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.

Rapid response EEG with needle electrodes in an intensive care unit with limited resources

Objective

Continuous EEG (cEEG) is the gold standard for detecting seizures and rhythmic and periodic patterns (RPPs) in critically ill patients but is often not available in health systems with limited resources. The current study aims to determine the feasibility and utility of low-cost, practical, limited montage, sub-dermal needle electrode EEG in a setting where otherwise no EEG would be available.

Methods

The study included all adult patients admitted to the intensive care unit of a single center over a 24-month period. Members of the existing ICU care team, mostly nurses, were trained to place 8 sub-dermal needle EEG electrodes to achieve rapid, limited montage-EEG recording. Clinical outcomes were recorded, including any reported major complications; and the EEG findings documented, including background characterization, RPPs, and seizures.

Results

One hundred twenty-three patients, mean age 68 years, underwent an average of 15.6 min of EEG recording. There were no complications of electrode placement. Overall, 13.0% had seizures (8.1% qualifying as status epilepticus [SE]), 18.7 % had generalized periodic discharges (GPDs), 4.9% had lateralized periodic discharges (LPDs), and 11.4 % sporadic epileptiform discharges (sEDs). Greater mortality was observed in patients with worse background EEGs, seizures, LPDs, or sEDs.

Conclusions

Rapid, limited montage EEG could be achieved safely and inexpensively in a broad population of critically ill patients following minimal training of existing care teams.

Significance

For resource poor centers or centers outside of major metropolitan areas who otherwise have no access to EEG, this may prove a useful method for screening for non-convulsive seizures and status epilepticus.

Macroperiodic Oscillations Are Associated With Seizures Following Acquired Brain Injury in Young Children

PURPOSE

Seizures occur in 10% to 40% of critically ill children. We describe a phenomenon seen on color density spectral array but not raw EEG associated with seizures and acquired brain injury in pediatric patients.

METHODS

We reviewed EEGs of 541 children admitted to an intensive care unit between October 2015 and August 2018. We identified 38 children (7%) with a periodic pattern on color density spectral array that oscillates every 2 to 5 minutes and was not apparent on the raw EEG tracing, termed macroperiodic oscillations (MOs). Internal validity measures and interrater agreement were assessed. We compared demographic and clinical data between those with and without MOs.

RESULTS

Interrater reliability yielded a strong agreement for MOs identification (kappa: 0.778 [0.542-1.000]; P < 0.0001). There was a 76% overlap in the start and stop times of MOs among reviewers. All patients with MOs had seizures as opposed to 22.5% of the general intensive care unit monitoring population ( P < 0.0001). Macroperiodic oscillations occurred before or in the midst of recurrent seizures. Patients with MOs were younger (median of 8 vs. 208 days; P < 0.001), with indications for EEG monitoring more likely to be clinical seizures (42 vs. 16%; P < 0.001) or traumatic brain injury (16 vs. 5%, P < 0.01) and had fewer premorbid neurologic conditions (10.5 vs. 33%; P < 0.01).

CONCLUSIONS

Macroperiodic oscillations are a slow periodic pattern occurring over a longer time scale than periodic discharges in pediatric intensive care unit patients. This pattern is associated with seizures in young patients with acquired brain injuries.

Electrographic Seizures in the Critically Ill

Identifying and treating critically ill patients with seizures can be challenging. In this article, the authors review the available data on patient populations at risk, seizure prognostication with tools such as 2HELPS2B, electrographic seizures and the various ictal-interictal continuum patterns with their latest definitions and associated risks, ancillary testing such as imaging studies, serum biomarkers, and invasive multimodal monitoring. They also illustrate 5 different patient scenarios, their treatment and outcomes, and propose recommendations for targeted treatment of electrographic seizures in critically ill patients.

Electroencephalogram in the intensive care unit: a focused look at acute brain injury

Over the past decades, electroencephalography (EEG) has become a widely applied and highly sophisticated brain monitoring tool in a variety of intensive care unit (ICU) settings. The most common indication for EEG monitoring currently is the management of refractory status epilepticus. In addition, a number of studies have associated frequent seizures, including nonconvulsive status epilepticus (NCSE), with worsening secondary brain injury and with worse outcomes. With the widespread utilization of EEG (spot and continuous EEG), rhythmic and periodic patterns that do not fulfill strict seizure criteria have been identified, epidemiologically quantified, and linked to pathophysiological events across a wide spectrum of critical and acute illnesses, including acute brain injury. Increasingly, EEG is not just qualitatively described, but also quantitatively analyzed together with other modalities to generate innovative measurements with possible clinical relevance. In this review, we discuss the current knowledge and emerging applications of EEG in the ICU, including seizure detection, ischemia monitoring, detection of cortical spreading depolarizations, assessment of consciousness and prognostication. We also review some technical aspects and challenges of using EEG in the ICU including the logistics of setting up ICU EEG monitoring in resource-limited settings.

Epileptiform Abnormalities in Acute Ischemic Stroke: Impact on Clinical Management and Outcomes

PURPOSE

Studies examining seizures (Szs) and epileptiform abnormalities (EAs) using continuous EEG in acute ischemic stroke (AIS) are limited. Therefore, we aimed to describe the prevalence of Sz and EA in AIS, its impact on anti-Sz drug management, and association with discharge outcomes.

METHODS

The study included 132 patients with AIS who underwent continuous EEG monitoring >6 hours. Continuous EEG was reviewed for background, Sz and EA (lateralized periodic discharges [LPD], generalized periodic discharges, lateralized rhythmic delta activity, and sporadic epileptiform discharges). Relevant clinical, demographic, and imaging factors were abstracted to identify risk factors for Sz and EA. Outcomes included all-cause mortality, functional outcome at discharge (good outcome as modified Rankin scale of 0-2 and poor outcome as modified Rankin scale of 3-6) and changes to anti-Sz drugs (escalation or de-escalation).

RESULTS

The frequency of Sz was 7.6%, and EA was 37.9%. Patients with Sz or EA were more likely to have cortical involvement (84.6% vs. 67.5% P = 0.028). Among the EAs, the presence of LPD was associated with an increased risk of Sz (25.9% in LPD vs. 2.9% without LPD, P = 0.001). Overall, 21.2% patients had anti-Sz drug changes because of continuous EEG findings, 16.7% escalation and 4.5% de-escalation. The presence of EA or Sz was not associated with in-hospital mortality or discharge functional outcomes.

CONCLUSIONS

Despite the high incidence of EA, the rate of Sz in AIS is relatively lower and is associated with the presence of LPDs. These continuous EEG findings resulted in anti-Sz drug changes in one-fifth of the cohort. Epileptiform abnormality and Sz did not affect mortality or discharge functional outcomes.

Prognostic indicators of non-convulsive status epilepticus in intensive care unit

BACKGROUND

To determine the rate of non-convulsive status epilepticus with/without prominent motor phenomena (SE-PM/ NCSE) and predictive value of electroclinical findings of continious electroencephalography (cEEG) monitoring of these patients and its association with prognosis in intensive care units (ICU).

METHODS

We retrospectively collected data of 218 patients whose cEEG was performed in ICU between 2016 and 2018. The cEEG for NCSE diagnosis was evaluated according to Salzburg Consensus Criteria (SCC).

RESULTS

The mean age of patients was 57.09 ± 18.9 (16-95) years and 49.1% (107) were female. Of 218 patients, 32 (14.7%) had SE-PM/NCSE. According to SCC the rate of NCSE (NCSE + possible NCSE) was 9.6% (n = 21). Prior to cEEG recording, 38.9% (n = 85) of overall patients had a history of seizure/convulsion, and 22.7% (n = 21) of these patients diagnosed with NCSE based on cEEG. The mortality rates in critically ill patients were 41.3% (30.8%, 42.8%; for SE-PM and NCSE respectively). Prognosis was associated with age, epilepsy diagnosis, having convulsion/seizure history on follow-up, GCS, need for ventilation, kind of drugs, sepsis diagnosis, and minimum frequency of background activity of the cEEG (p = 0.001, 0.002, 0.001, 0.020, 0.001, 0.001, 0.001, 0.0001 respectively).

CONCLUSIONS

NCSE findings are mostly found in patients who were comatose and had seizure/convulsion history on follow-up. Mortality is higher in patients diagnosed with NCSE followed in the ICU compared to SE-PM.

The Past, Present, and Future of Tele-EEG

Tele-electroencephalogram (EEG) has become more pervasive over the last 20 years due to advances in technology, both independent of and driven by personnel shortages. The professionalization of EEG services has both limited growth and controlled the quality of tele-EEG. Growing data on the conditions that benefit from brain monitoring have informed increased critical care EEG and ambulatory EEG utilization. Guidelines that marshal responsible use of still-limited resources and changes in broadband and billing practices have also shaped the tele-EEG landscape. It is helpful to characterize the drivers of tele-EEG to navigate barriers to sustainable growth and to build dynamic systems that anticipate challenges in any of the domains that expand access and enhance quality of these diagnostic services. We explore the historical factors and current trends in tele-EEG in the United States in this review.

New-onset non-lesional aphasic status epilepticus. Clinical description, diagnostic clues, and treatment algorithm

OBJECTIVES

De novo aphasic status epilepticus (ASE) in patients without a previous history of epilepsy and without cerebral lesions (aphasic NOSE) is rare. The aim of the study is to describe its clinical characteristics, etiologies, and outcome.

MATERIALS & METHODS

Single-center study including consecutive patients presenting to the emergency department between 2011 and 2019 with acute aphasia, which was finally diagnosed as aphasic NOSE. Subsequent episodes of aphasia (>5 min) were recorded and divided into confirmed ASE and postictal aphasic episodes (non-ASE). Clinical characteristics of the two types of episodes were compared.

RESULTS

Nineteen patients were included, suffering fifty episodes of epileptic aphasia, episodes per patient 2.6 (range 1-7). Fifteen patients (71.4%) were women, mean age at ASE onset was 66.05 years old (SD 6.3). Nine (47%) patients died, 6 of them (66.7%) during the aphasic episode. Ictal EEG was available in 37 episodes, confirming the diagnosis of ASE in 12 episodes; in 8 episodes, the EEG fulfilled the criteria of possible ASE. The most frequent etiologies were inflammatory and vascular. Comparing ASE with non-ASE episodes, ASE was longer than non-ASE (225 vs 65 h, p .024) and was treated more frequently with BZD (76 vs 24%, p .001) but with a longer delay (22.2 vs 1.5 h, p .06).

CONCLUSIONS

ASE is a treatable, highly relapsing emergency, with the subsequent relapses ASE or postictal aphasia. EEG is diagnostic in half of the patients, while in others imaging techniques are also useful. Benzodiazepines should be administered. Persistent aphasia, of more than 65 hours' duration, is highly suggestive of ASE.

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.

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.

A model of metabolic supply-demand mismatch leading to secondary brain injury

Secondary brain injury (SBI) is defined as new or worsening injury to the brain after an initial neurologic insult, such as hemorrhage, trauma, ischemic stroke, or infection. It is a common and potentially preventable complication following many types of primary brain injury (PBI). However, mechanistic details about how PBI leads to additional brain injury and evolves into SBI are poorly characterized. In this work, we propose a mechanistic model for the metabolic supply demand mismatch hypothesis (MSDMH) of SBI. Our model, based on the Hodgkin-Huxley model, supplemented with additional dynamics for extracellular potassium, oxygen concentration, and excitotoxity, provides a high-level unified explanation for why patients with acute brain injury frequently develop SBI. We investigate how decreased oxygen, increased extracellular potassium, excitotoxicity, and seizures can induce SBI and suggest three underlying paths for how events following PBI may lead to SBI. The proposed model also helps explain several important empirical observations, including the common association of acute brain injury with seizures, the association of seizures with tissue hypoxia and so on. In contrast to current practices which assume that ischemia plays the predominant role in SBI, our model suggests that metabolic crisis involved in SBI can also be nonischemic. Our findings offer a more comprehensive understanding of the complex interrelationship among potassium, oxygen, excitotoxicity, seizures, and SBI.NEW & NOTEWORTHY We present a novel mechanistic model for the metabolic supply demand mismatch hypothesis (MSDMH), which attempts to explain why patients with acute brain injury frequently develop seizure activity and secondary brain injury (SBI). Specifically, we investigate how decreased oxygen, increased extracellular potassium, excitotoxicity, seizures, all common sequalae of primary brain injury (PBI), can induce SBI and suggest three underlying paths for how events following PBI may lead to SBI.

Evaluating the Clinical Impact of Rapid Response Electroencephalography: The DECIDE Multicenter Prospective Observational Clinical Study

OBJECTIVES

To measure the diagnostic accuracy, timeliness, and ease of use of Ceribell rapid response electroencephalography. We assessed physicians' diagnostic assessments and treatment plans before and after rapid response electroencephalography assessment. Primary outcomes were changes in physicians' diagnostic and therapeutic decision making and their confidence in these decisions based on the use of the rapid response electroencephalography system. Secondary outcomes were time to electroencephalography, setup time, ease of use, and quality of electroencephalography data.

DESIGN

Prospective multicenter nonrandomized observational study.

SETTING

ICUs in five academic hospitals in the United States.

SUBJECTS

Patients with encephalopathy suspected of having nonconvulsive seizures and physicians evaluating these patients.

INTERVENTIONS

Physician bedside assessment of sonified electroencephalography (30 s from each hemisphere) and visual electroencephalography (60 s) using rapid response electroencephalography.

MEASUREMENTS AND MAIN RESULTS

Physicians (29 fellows or residents, eight attending neurologists) evaluated 181 ICU patients; complete clinical and electroencephalography data were available in 164 patients (average 58.6 ± 18.7 yr old, 45% females). Relying on rapid response electroencephalography information at the bedside improved the sensitivity (95% CI) of physicians' seizure diagnosis from 77.8% (40.0%, 97.2%) to 100% (66.4%, 100%) and the specificity (95% CI) of their diagnosis from 63.9% (55.8%, 71.4%) to 89% (83.0%, 93.5%). Physicians' confidence in their own diagnosis and treatment plan were also improved. Time to electroencephalography (median [interquartile range]) was 5 minutes (4-10 min) with rapid response electroencephalography while the conventional electroencephalography was delayed by several hours (median [interquartile range] delay = 239 minutes [134-471 min] [p < 0.0001 using Wilcoxon signed rank test]). The device was rated as easy to use (mean ± SD: 4.7 ± 0.6 [1 = difficult, 5 = easy]) and was without serious adverse effects.

CONCLUSIONS

Rapid response electroencephalography enabled timely and more accurate assessment of patients in the critical care setting. The use of rapid response electroencephalography may be clinically beneficial in the assessment of patients with high suspicion for nonconvulsive seizures and status epilepticus.

Outcomes of seizures, status epilepticus, and EEG findings in critically ill patient with COVID-19

OBJECTIVE

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has a myriad of neurological manifestations and its effects on the nervous system are increasingly recognized. Seizures and status epilepticus (SE) are reported in the novel coronavirus disease (COVID-19), both new onset and worsening of existing epilepsy; however, the exact prevalence is still unknown. The primary aim of this study was to correlate the presence of seizures, status epilepticus, and specific critical care EEG patterns with patient functional outcomes in those with COVID-19.

METHODS

This is a retrospective, multicenter cohort of COVID-19-positive patients in Southeast Michigan who underwent electroencephalography (EEG) from March 12th through May 15th, 2020. All patients had confirmed nasopharyngeal PCR for COVID-19. EEG patterns were characterized per 2012 ACNS critical care EEG terminology. Clinical and demographic variables were collected by medical chart review. Outcomes were divided into recovered, recovered with disability, or deceased.

RESULTS

Out of the total of 4100 patients hospitalized with COVID-19, 110 patients (2.68%) had EEG during their hospitalization; 64% were male, 67% were African American with mean age of 63 years (range 20-87). The majority (70%) had severe COVID-19, were intubated, or had multi-organ failure. The median length of hospitalization was 26.5 days (IQR = 15 to 44 days). During hospitalization, of the patients who had EEG, 21.8% had new-onset seizure including 7% with status epilepticus, majority (87.5%) with no prior epilepsy. Forty-nine (45%) patients died in the hospital, 46 (42%) recovered but maintained a disability and 15 (14%) recovered without a disability. The EEG findings associated with outcomes were background slowing/attenuation (recovered 60% vs recovered/disabled 96% vs died 96%, p < 0.001) and normal (recovered 27% vs recovered/disabled 0% vs died 1%, p < 0.001). However, these findings were no longer significant after adjusting for severity of COVID-19.

CONCLUSION

In this large multicenter study from Southeast Michigan, one of the early COVID-19 epicenters in the US, none of the EEG findings were significantly correlated with outcomes in critically ill COVID-19 patients. Although seizures and status epilepticus could be encountered in COVID-19, the occurrence did not correlate with the patients' functional outcome.

Pharmacotherapy for Nonconvulsive Seizures and Nonconvulsive Status Epilepticus

Most seizures in critically ill patients are nonconvulsive. A significant number of neurological and medical conditions can be complicated by nonconvulsive seizures (NCSs) and nonconvulsive status epilepticus (NCSE), with brain infections, hemorrhages, global hypoxia, sepsis, and recent neurosurgery being the most prominent etiologies. Prolonged NCSs and NCSE can lead to adverse neurological outcomes. Early recognition requires a high degree of suspicion and rapid and appropriate duration of continuous electroencephalogram (cEEG) monitoring. Although high quality research evaluating treatment with antiseizure medications and long-term outcome is still lacking, it is probable that expeditious pharmacological management of NCSs and NCSE may prevent refractoriness and further neurological injury. There is limited evidence on pharmacotherapy for NCSs and NCSE, although a few clinical trials encompassing both convulsive and NCSE have demonstrated similar efficacy of different intravenous (IV) antiseizure medications (ASMs), including levetiracetam, valproate, lacosamide and fosphenytoin. The choice of specific ASMs lies on tolerability and safety since critically ill patients frequently have impaired renal and/or hepatic function as well as hematological/hemodynamic lability. Treatment frequently requires more than one ASM and occasionally escalation to IV anesthetic drugs. When multiple ASMs are required, combining different mechanisms of action should be considered. There are several enteral ASMs that could be used when IV ASM options have been exhausted. Refractory NCSE is not uncommon, and its treatment requires a very judicious selection of ASMs aiming at reducing seizure burden along with management of the underlying condition.

Neurological complications of cancer immunotherapy (CAR T cells)

Chimeric antigen receptor (CAR) T cell therapy has become an indispensable tool in the treatment of advanced malignancy, however, it is associated with significant neurologic toxicity. The pathophysiology of CAR T-cell associated neurotoxicity is incompletely understood, and the specific risk factors have only recently begun to be characterized. Despite a growing clinical experience with CAR T cell therapy, the unpredictability of neurologic symptoms remains a source of great anxiety for patients and practitioners alike, and a major limitation for more widespread adoption of this important treatment modality. The purpose of this review is to familiarize clinicians with the typical clinical manifestations and salient features of CAR T cell associated neurotoxicity. We place an emphasis on highlighting the clinical and laboratory markers that may be helpful for predicting clinical course, allowing teams to anticipate necessary supportive measures. We will also review the appropriate diagnostic workup for CAR T cell neurotoxicity and current treatment recommendations.

What's new on EEG monitoring in the ICU

Continuous video-EEG (cEEG, lasting hours to several days) is increasingly used in ICU patients, as it is more sensitive than routine video-EEG (rEEG, lasting 20-30 min) to detect seizures or status epilepticus, and allows more frequent changes in therapeutic regimens. However, cEEG is more resource-consuming, and its relationship to outcome compared to repeated rEEG has only been formally assessed very recently in a randomized controlled trial, which did not show any significant difference in terms of long-term mortality or functional outcome. Awaiting more refined trials, it seems therefore that using repeated rEEG in ICU patients may represent a reasonable alternative in resource-limited settings. Prolonged EEG has been used recently in patients with severe COVID-19 infection, the proportion of seizures seems albeit relatively low, and similar to ICU patients with medical conditions. As in any case a timely EEG recording is recommended in the ICU, r ecent technical developments may ease its use in clinical practice.

Machine learning models to predict electroencephalographic seizures in critically ill children

OBJECTIVE

To determine whether machine learning techniques would enhance our ability to incorporate key variables into a parsimonious model with optimized prediction performance for electroencephalographic seizure (ES) prediction in critically ill children.

METHODS

We analyzed data from a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy who underwent clinically-indicated continuous EEG monitoring (CEEG). We implemented and compared three state-of-the-art machine learning methods for ES prediction: (1) random forest; (2) Least Absolute Shrinkage and Selection Operator (LASSO); and (3) Deep Learning Important FeaTures (DeepLIFT). We developed a ranking algorithm based on the relative importance of each variable derived from the machine learning methods.

RESULTS

Based on our ranking algorithm, the top five variables for ES prediction were: (1) epileptiform discharges in the initial 30 minutes, (2) clinical seizures prior to CEEG initiation, (3) sex, (4) age dichotomized at 1 year, and (5) epileptic encephalopathy. Compared to the stepwise selection-based approach in logistic regression, the top variables selected by our ranking algorithm were more informative as models utilizing the top variables achieved better prediction performance evaluated by prediction accuracy, AUROC and F1 score. Adding additional variables did not improve and sometimes worsened model performance.

CONCLUSION

The ranking algorithm was helpful in deriving a parsimonious model for ES prediction with optimal performance. However, application of state-of-the-art machine learning models did not substantially improve model performance compared to prior logistic regression models. Thus, to further improve the ES prediction, we may need to collect more samples and variables that provide additional information.

EEG Correlates of Delirium in Children and Young Adults With CD19-Directed CAR T Cell Treatment-Related Neurotoxicity

INTRODUCTION

EEG patterns in chimeric antigen receptor T cell treatment-associated neurotoxicity (immune effector cell-associated neurotoxicity syndrome) have not yet been systematically studied. We tested the hypothesis that EEG background abnormalities in immune effector cell-associated neurotoxicity syndrome correlate with clinical signs of neurotoxicity. In addition, we describe ictal and interictal EEG patterns to better understand the natural history of immune effector cell-associated neurotoxicity syndrome-associated seizures.

METHODS

EEGs were obtained in 19 of 100 subjects in a prospective cohort study of children and young adults undergoing CD19-directed chimeric antigen receptor T cell therapy. We classified the EEG background on a severity scale of 0 to 5 during 30-minute epochs. EEG grades were compared with neurotoxicity scored by Common Terminology Criteria for Adverse Events and Cornell Assessment of Pediatric Delirium scores. Descriptive analysis was conducted for ictal and interictal EEG abnormalities.

RESULTS

EEG background abnormality scores correlated well with Common Terminology Criteria for Adverse Events neurotoxicity scores (P = 0.0022) and Cornell Assessment of Pediatric Delirium scores (P = 0.0085). EEG was better able to differentiate the severity of coma patterns compared with the clinical scores. The EEG captured electroclinical seizures in 4 of 19 subjects, 3 of whom had additional electrographic-only seizures. Seizures most often arose from posterior head regions. Interictal epileptiform discharges were focal, multifocal, or lateralized periodic discharges. No seizures or interictal epileptiform abnormalities were seen in subjects without previous clinical seizures.

CONCLUSIONS

Continuous EEG monitoring is high yield for seizure detection in high-risk chimeric antigen receptor T cell patients, and electrographic-only seizures are common. Increasing severity of EEG background abnormalities correlates with increasing neurotoxicity grade.

Does continuous electroencephalography influence therapeutic decisions in neurocritical care?

OBJECTIVES

In the neurocritical care unit (neuro-ICU), the impact of continuous EEG (cEEG) on therapeutic decisions and prognostication, including outcome prediction using the Status Epilepticus Severity Score (STESS), is poorly investigated. We studied to what extent cEEG contributes to treatment decisions, and how this relates to clinical outcome and the use of STESS in neurocritical care.

METHODS

We included patients admitted to the neuro-ICU or neurological step-down unit of a tertiary referral hospital between 05/2013 and 06/2015. Inclusion criteria were ≥20 h of cEEG monitoring and age ≥15 years. Exclusion criteria were primary epileptic and post-cardiac arrest encephalopathies.

RESULTS

Ninety-eight patients met inclusion criteria, 80 of which had status epilepticus, including 14 with super-refractory status. Median length of cEEG monitoring was 50 h (range 21-374 h). Mean STESS was lower in patients with favorable outcome 1 year after discharge (modified Rankin Scale [mRS] 0-2) compared to patients with unfavorable outcome (mRS 3-6), albeit not statistically significant (mean STESS 2.3 ± 2.1 vs 3.6 ± 1.7, p = 0.09). STESS had a sensitivity of 80%, a specificity of 42%, and a negative predictive value of 93% for outcome. cEEG results changed treatment decisions in 76 patients, including escalation of antiepileptic treatment in 65 and reduction in 11 patients.

CONCLUSION

Status Epilepticus Severity Score had a high negative predictive value but low sensitivity, suggesting that STESS should be used cautiously. Of note, cEEG results altered clinical decision-making in three of four patients, irrespective of the presence or absence of status epilepticus, confirming the clinical value of cEEG in neurocritical care.

Association of Epileptiform Abnormality on Electroencephalography with Development of Epilepsy After Acute Brain Injury

BACKGROUND/OBJECTIVES

Epileptiform abnormalities (EA) on continuous electroencephalography (cEEG) are associated with increased risk of acute seizures; however, data on their association with development of long-term epilepsy are limited. We aimed to investigate the association of EA in patients with acute brain injury (ABI): ischemic or hemorrhagic stroke, traumatic brain injury, encephalitis, or posterior reversible encephalopathy syndrome, and subsequent development of epilepsy.

METHODS

This was a retrospective, single-center study of patients with ABI who had at least 6 hours of cEEG during the index admission between 1/1/2017 and 12/31/2018 and at least 12 months of follow-up. We compared patients with EAs; defined as lateralized periodic discharges (LPDs), lateralized rhythmic delta activity (LRDA), generalized periodic discharges (GPDs), and sporadic interictal epileptiform discharges (sIEDs) to patients without EAs on cEEG. The primary outcome was the new development of epilepsy, defined as the occurrence of spontaneous clinical seizures following hospital discharge. Secondary outcomes included time to development of epilepsy and use of anti-seizure medications (ASMs) at the time of last follow-up visit.

RESULTS

One hundred and one patients with ABI met study inclusion criteria. Thirty-one patients (30.7%) had EAs on cEEG. The median (IQR) time to cEEG was 2 (1-5) days. During a median (IQR) follow-up period of 19.1 (16.2-24.3) months, 25.7% of patients developed epilepsy; the percentage of patients who developed epilepsy was higher in those with EAs compared to those without EAs (41.9% vs. 18.6%, p = 0.025). Patients with EAs were more likely to be continued on ASMs during follow-up compared to patients without EAs (67.7% vs. 38.6%, p = 0.009). Using multivariable Cox regression analysis, after adjusting for age, mental status, electrographic seizures on cEEG, sex, ABI etiology, and ASM treatment on discharge, patients with EAs had a significantly increased risk of developing epilepsy compared to patients without EA (hazard ratio 3.39; 95% CI 1.39-8.26; p = 0.007).

CONCLUSIONS

EAs on cEEG in patients with ABI are associated with a greater than three-fold increased risk of new-onset epilepsy. cEEG findings in ABI may therefore be a useful risk stratification tool for assessing long-term risk of seizures and serve as a biomarker for new-onset epilepsy.

EEG Monitoring After Convulsive Status Epilepticus

After convulsive status epilepticus, patients of all ages may have ongoing EEG seizures identified by continuous EEG monitoring. Furthermore, high EEG seizure exposure has been associated with unfavorable neurobehavioral outcomes. Thus, recent guidelines and consensus statements recommend many patients with persisting altered mental status after convulsive status epilepticus undergo continuous EEG monitoring. This review summarizes the available epidemiologic data and related recommendations provided by recent guidelines and consensus statements.

Diagnostic Value of Electroencephalography with Ten Electrodes in Critically Ill Patients

BACKGROUND

In critical care settings, electroencephalography (EEG) with reduced number of electrodes (reduced montage EEG, rm-EEG) might be a timely alternative to the conventional full montage EEG (fm-EEG). However, past studies have reported variable accuracies for detecting seizures using rm-EEG. We hypothesized that the past studies did not distinguish between differences in sensitivity from differences in classification of EEG patterns by different readers. The goal of the present study was to revisit the diagnostic value of rm-EEG when confounding issues are accounted for.

METHODS

We retrospectively collected 212 adult EEGs recorded at Massachusetts General Hospital and reviewed by two epileptologists with access to clinical, trending, and video information. In Phase I of the study, we re-configured the first 4 h of the EEGs in lateral circumferential montage with ten electrodes and asked new readers to interpret the EEGs without access to any other ancillary information. We compared their rating to the reading of hospital clinicians with access to ancillary information. In Phase II, we measured the accuracy of the same raters reading representative samples of the discordant EEGs in full and reduced configurations presented randomly by comparing their performance to majority consensus as the gold standard.

RESULTS

Of the 95 EEGs without seizures in the selected fm-EEG, readers of rm-EEG identified 92 cases (97%) as having no seizure activity. Of 117 EEGs with "seizures" identified in the selected fm-EEG, none of the cases was labeled as normal on rm-EEG. Readers of rm-EEG reported pathological activity in 100% of cases, but labeled them as seizures (N = 77), rhythmic or periodic patterns (N = 24), epileptiform spikes (N = 7), or burst suppression (N = 6). When the same raters read representative epochs of the discordant EEG cases (N = 43) in both fm-EEG and rm-EEG configurations, we found high concordance (95%) and intra-rater agreement (93%) between fm-EEG and rm-EEG diagnoses.

CONCLUSIONS

Reduced EEG with ten electrodes in circumferential configuration preserves key features of the traditional EEG system. Discrepancies between rm-EEG and fm-EEG as reported in some of the past studies can be in part due to methodological factors such as choice of gold standard diagnosis, asymmetric access to ancillary clinical information, and inter-rater variability rather than detection failure of rm-EEG as a result of electrode reduction per se.

Ictal-interictal continuum: a review of recent advancements

Continuous electroencephalogram (cEEG) has become an indispensable technique in the management of critically ill patients for early detection and treatment of non-convulsive seizures (NCS) and non-convulsive status epilepticus (NCSE). It has also brought about a renaissance in a wide range of rhythmic and periodic patterns with heterogeneous frequency and morphology. These patterns share the rhythmic and sharp appearances of electrographic seizures, but often lack the necessary frequency, spatiotemporal evolution and clinical accompaniments to meet the definitive criteria for ictal patterns. They may be associated with cerebral metabolic crisis and neuronal injury, therefore not clearly interictal either, but lie along an intervening spectrum referred to as ictal-interictal continuum (IIC). Generally speaking, rhythmic and periodic patterns are categorized as interictal patterns when occurring at a rate of <1Hz, and are categorized as NCS and NCSE when occurring at a rate of >2.5 Hz with spatiotemporal evolution. As such, IIC commonly includes the rhythmic and periodic patterns occurring at a rate of 1–2.5 Hz without spatiotemporal evolution and clinical correlates. Currently there are no evidence-based guidelines on when and if to treat patients with IIC patterns, and particularly how aggressively to treat, presenting a challenging electrophysiological and clinical conundrum. In practice, a diagnostic trial with preferably a non-sedative anti-seizure medication (ASM) can be considered with the end point being both clinical and electrographic improvement. When available and necessary, correlation of IIC with biomarkers of neuronal injury, such as neuronal specific enolase (NSE), neuroimaging, depth electrode recording, cerebral microdialysis and oxygen measurement, can be assessed for the consideration of ASM treatment. Here we review the recent advancements in their clinical significance, risk stratification and treatment algorithm.

EEG monitoring duration to identify electroencephalographic seizures in critically ill children

OBJECTIVES

To determine the optimal duration of continuous EEG monitoring (CEEG) for electrographic seizure (ES) identification in critically ill children.

METHODS

We performed a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy. We evaluated baseline clinical risk factors (age and prior clinically evident seizures) and emergent CEEG risk factors (epileptiform discharges and ictal-interictal continuum patterns) using a multistate survival model. For each subgroup, we determined the CEEG duration for which the risk of ES was <5% and <2%.

RESULTS

ES occurred in 184 children (26%). Patients achieved <5% risk of ES after (1) 6 hours if ≥1 year without prior seizures or EEG risk factors; (2) 1 day if <1 year without prior seizures or EEG risks; (3) 1 day if ≥1 year with either prior seizures or EEG risks; (4) 2 days if ≥1 year with prior seizures and EEG risks; (5) 2 days if <1 year without prior seizures but with EEG risks; and (6) 2.5 days if <1 year with prior seizures regardless of the presence of EEG risks. Patients achieved <2% risk of ES at the same durations except patients without prior seizures or EEG risk factors would require longer CEEG (1.5 days if <1 year of age, 1 day if ≥1 year of age).

CONCLUSIONS

A model derived from 2 baseline clinical risk factors and emergent EEG risk factors would allow clinicians to implement personalized strategies that optimally target limited CEEG resources. This would enable more widespread use of CEEG-guided management as a potential neuroprotective strategy.

CLINICALTRIALSGOV IDENTIFIER

NCT03419260.

Epileptic Seizures in Critically Ill Patients: Diagnosis, Management, and Outcomes

Epileptic seizures in critically ill patients represent a major source of concern, because they are associated with significant mortality and morbidity rates. Despite recent advances that have enabled a better understanding of the global epidemiology of this entity, epileptic seizures in critically ill patients remain associated with a high degree of uncertainty and numerous questions remain unanswered. The present Special Issue aims to invite authors to contribute original research articles as well as review articles related to all aspects of epileptic seizures in critically ill patients, diagnosis, management, and outcomes.

Assessment of the Validity of the 2HELPS2B Score for Inpatient Seizure Risk Prediction

Importance

Seizure risk stratification is needed to boost inpatient seizure detection and to improve continuous electroencephalogram (cEEG) cost-effectiveness. 2HELPS2B can address this need but requires validation.

Objective

To use an independent cohort to validate the 2HELPS2B score and develop a practical guide for its use.

Design, Setting, and Participants

This multicenter retrospective medical record review analyzed clinical and EEG data from patients 18 years or older with a clinical indication for cEEG and an EEG duration of 12 hours or longer who were receiving consecutive cEEG at 6 centers from January 2012 to January 2019. 2HELPS2B was evaluated with the validation cohort using the mean calibration error (CAL), a measure of the difference between prediction and actual results. A Kaplan-Meier survival analysis was used to determine the duration of EEG monitoring to achieve a seizure risk of less than 5% based on the 2HELPS2B score calculated on first- hour (screening) EEG. Participants undergoing elective epilepsy monitoring and those who had experienced cardiac arrest were excluded. No participants who met the inclusion criteria were excluded.

Main Outcomes and Measures

The main outcome was a CAL error of less than 5% in the validation cohort.

Results

The study included 2111 participants (median age, 51 years; 1113 men [52.7%]; median EEG duration, 48 hours) and the primary outcome was met with a validation cohort CAL error of 4.0% compared with a CAL of 2.7% in the foundational cohort (P = .13). For the 2HELPS2B score calculated on only the first hour of EEG in those without seizures during that hour, the CAL error remained at less than 5.0% at 4.2% and allowed for stratifying patients into low- (2HELPS2B = 0; <5% risk of seizures), medium- (2HELPS2B = 1; 12% risk of seizures), and high-risk (2HELPS2B, ≥2; risk of seizures, >25%) groups. Each of the categories had an associated minimum recommended duration of EEG monitoring to achieve at least a less than 5% risk of seizures, a 2HELPS2B score of 0 at 1-hour screening EEG, a 2HELPS2B score of 1 at 12 hours, and a 2HELPS2B score of 2 or greater at 24 hours.

Conclusions and Relevance

In this study, 2HELPS2B was validated as a clinical tool to aid in seizure detection, clinical communication, and cEEG use in hospitalized patients. In patients without prior clinical seizures, a screening 1-hour EEG that showed no epileptiform findings was an adequate screen. In patients with any highly epileptiform EEG patterns during the first hour of EEG (ie, a 2HELPS2B score of ≥2), at least 24 hours of recording is recommended.

Validation of an algorithm of time-dependent electro-clinical risk stratification for electrographic seizures (TERSE) in critically ill patients

OBJECTIVE

The clinical implementation of continuous electroencephalography (CEEG) monitoring in critically ill patients is hampered by the substantial burden of work that it entails for clinical neurophysiologists. Solutions that might reduce this burden, including by shortening the duration of EEG to be recorded, would help its widespread adoption. Our aim was to validate a recently described algorithm of time-dependent electro-clinical risk stratification for electrographic seizure (ESz) (TERSE) based on simple clinical and EEG features.

METHODS

We retrospectively reviewed the medical records and EEG recordings of consecutive patients undergoing CEEG between October 1, 2015 and September, 30 2016 and assessed the sensitivity of TERSE for seizure detection, as well as the reduction in EEG time needed to be reviewed.

RESULTS

In a cohort of 407 patients and compared to full CEEG review, the model allowed the detection of 95% of patients with ESz and 97% of those with electrographic status epilepticus. The amount of CEEG to be recorded to detect ESz was reduced by two-thirds, compared to the duration of CEEG taht was actually recorded.

CONCLUSIONS

TERSE allowed accurate time-dependent ESz risk stratification with a high sensitivity for ESz detection, which could substantially reduce the amount of CEEG to be recorded and reviewed, if applied prospectively in clinical practice.

SIGNIFICANCE

Time-dependent electro-clinical risk stratification, such as TERSE, could allow more efficient practice of CEEG and its more widespread adoption. Future studies should aim to improve risk stratification in the subgroup of patients with acute brain injury and absence of clinical seizures.

Electrographic seizures and ictal-interictal continuum (IIC) patterns in critically ill patients

Critical care long-term continuous electroencephalogram (cEEG) monitoring has expanded dramatically in the last several decades spurned by technological advances in EEG digitalization and several key clinical findings: 1-Seizures are relatively common in the critically ill-large recent observational studies suggest that around 20% of critically ill patients placed on cEEG have seizures. 2-The majority (~75%) of patients who have seizures have exclusively "electrographic seizures", that is, they have no overt ictal clinical signs. Along with the discovery of the unexpectedly high incidence of seizures was the high prevalence of EEG patterns that share some common features with archetypical electrographic seizures but are not uniformly considered to be "ictal". These EEG patterns include lateralized periodic discharges (LPDs) and generalized periodic discharges (GPDs)-patterns that at times exhibit ictal-like behavior and at other times behave more like an interictal finding. Dr. Hirsch and colleagues proposed a conceptual framework to describe this spectrum of patterns called the ictal-interictal continuum (IIC). In the following years, investigators began to answer some of the key pragmatic clinical concerns such as which patients are at risk of seizures and what is the optimal duration of cEEG use. At the same time, investigators have begun probing the core questions for critical care EEG-what is the underlying pathophysiology of these patterns, at what point do these patterns cause secondary brain injury, what are the optimal treatment strategies, and how do these patterns affect clinical outcomes such as neurological disability and the development of epilepsy. In this review, we cover recent advancements in both practical concerns regarding cEEG use, current treatment strategies, and review the evidence associating IIC/seizures with poor clinical outcomes.

Sensitivity of Continuous Electroencephalography to Detect Ictal Activity After Cardiac Arrest

IMPORTANCE

Epileptiform electroencephalographic (EEG) patterns are common after resuscitation from cardiac arrest, are associated with patient outcome, and may require treatment. It is unknown whether continuous EEG monitoring is needed to detect these patterns or if brief intermittent monitoring is sufficient. If continuous monitoring is required, the necessary duration of observation is unknown.

OBJECTIVE

To quantify the time-dependent sensitivity of continuous EEG for epileptiform event detection, and to compare continuous EEG to several alternative EEG-monitoring strategies for post-cardiac arrest outcome prediction.

DESIGN, SETTING, AND PARTICIPANTS

This observational cohort study was conducted in 2 academic medical centers between September 2010 and January 2018. Participants included 759 adults who were comatose after being resuscitated from cardiac arrest and who underwent 24 hours or more of EEG monitoring.

MAIN OUTCOMES AND MEASURES

Epileptiform EEG patterns associated with neurological outcome at hospital discharge, such as seizures likely to cause secondary injury.

RESULTS

Overall, 759 patients were included in the analysis; 281 (37.0%) were female, and the mean (SD) age was 58 (17) years. Epileptiform EEG activity was observed in 414 participants (54.5%), of whom only 26 (3.4%) developed potentially treatable seizures. Brief intermittent EEG had an estimated 66% (95% CI, 62%-69%) to 68% (95% CI, 66%-70%) sensitivity for detection of prognostic epileptiform events. Depending on initial continuity of the EEG background, 0 to 51 hours of monitoring were needed to achieve 95% sensitivity for the detection of prognostic epileptiform events. Brief intermittent EEG had a sensitivity of 7% (95% CI, 4%-12%) to 8% (95% CI, 4%-12%) for the detection of potentially treatable seizures, and 0 to 53 hours of continuous monitoring were needed to achieve 95% sensitivity for the detection of potentially treatable seizures. Brief intermittent EEG results yielded similar information compared with continuous EEG results when added to multivariable models predicting neurological outcome.

CONCLUSIONS AND RELEVANCE

Compared with continuous EEG monitoring, brief intermittent monitoring was insensitive for detection of epileptiform events. Monitoring EEG results significantly improved multimodality prediction of neurological outcome, but continuous monitoring appeared to add little additional information compared with brief intermittent monitoring.

Validation of the 2HELPS2B Seizure Risk Score in Acute Brain Injury Patients

BACKGROUND AND OBJECTIVE

Seizures are common after traumatic brain injury (TBI), aneurysmal subarachnoid hemorrhage (aSAH), subdural hematoma (SDH), and non-traumatic intraparenchymal hemorrhage (IPH)-collectively defined herein as acute brain injury (ABI). Most seizures in ABI are subclinical, meaning that they are only detectable with EEG. A method is required to identify patients at greatest risk of seizures and thereby in need of prolonged continuous EEG monitoring. 2HELPS2B is a simple point system developed to address this need. 2HELPS2B estimates seizure risk for hospitalized patients using five EEG findings and one clinical finding (pre-EEG seizure). The initial 2HELPS2B study did not specifically assess the ABI subpopulation. In this study, we aim to validate the 2HELPS2B score in ABI and determine its relative predictive accuracy compared to a broader set of clinical and electrographic factors.

METHODS

We queried the Critical Care EEG Monitoring Research Consortium database for ABI patients age ≥ 18 with > 6 h of continuous EEG monitoring; data were collected between February 2013 and November 2018. The primary outcome was electrographic seizure. Clinical factors considered were age, coma, encephalopathy, ABI subtype, and acute suspected or confirmed pre-EEG clinical seizure. Electrographic factors included 18 EEG findings. Predictive accuracy was assessed using a machine-learning paradigm with area under the receiver operator characteristic (ROC) curve as the primary outcome metric. Three models (clinical factors alone, EEG factors alone, EEG and clinical factors combined) were generated using elastic-net logistic regression. Models were compared to each other and to the 2HELPS2B model. All models were evaluated by calculating the area under the curve (AUC) of a ROC analysis and then compared using permutation testing of AUC with bootstrapping to generate confidence intervals.

RESULTS

A total of 1528 ABI patients were included. Total seizure incidence was 13.9%. Seizure incidence among ABI subtype varied: IPH 17.2%, SDH 19.1%, aSAH 7.6%, TBI 9.2%. Age ≥ 65 (p = 0.015) and pre-cEEG acute clinical seizure (p < 0.001) positively affected seizure incidence. Clinical factors AUC = 0.65 [95% CI 0.60-0.71], EEG factors AUC = 0.82 [95% CI 0.77-0.87], and EEG and clinical factors combined AUC = 0.84 [95% CI 0.80-0.88]. 2HELPS2B AUC = 0.81 [95% CI 0.76-0.85]. The 2HELPS2B AUC did not differ from EEG factors (p = 0.51), or EEG and clinical factors combined (p = 0.23), but was superior to clinical factors alone (p < 0.001).

CONCLUSIONS

Accurate seizure risk forecasting in ABI requires the assessment of EEG markers of pathologic electro-cerebral activity (e.g., sporadic epileptiform discharges and lateralized periodic discharges). The 2HELPS2B score is a reliable and simple method to quantify these EEG findings and their associated risk of seizure.

Development of a model to predict electroencephalographic seizures in critically ill children

OBJECTIVE

Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource-intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone.

METHODS

We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver-operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross-validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG.

RESULTS

The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P < .01). At a 0.10 cutoff selected to emphasize sensitivity, the optimal model had a sensitivity of 92%, specificity of 37%, positive predictive value of 34%, and negative predictive value of 93%. If applied, the model would limit 29% of patients from undergoing CEEG while failing to identify 8% of patients with ES.

SIGNIFICANCE

A model employing readily available clinical and EEG variables could target limited CEEG resources to critically ill children at highest risk for ES, making CEEG-guided management a more viable neuroprotective strategy.

Time to Detection of the First Seizure in Patients With Nonconvulsive Status Epilepticus in the Neurological Intensive Care Unit

Video-EEG monitoring is often used to detect nonconvulsive status epilepticus (NCSE) in critical care patients. Short recording durations may fail to detect seizures. In this study, we investigated the time required to record the first ictal event, and whether it could be correlated with some clinical or EEG parameters. Video-EEG recordings of patients who were followed up in our neurological intensive care unit were evaluated retrospectively. The EEG recordings of patients with NCSE were reviewed to determine the timing of the first seizure occurrence. Demographic data and EEG findings were obtained from patient charts and EEG reports. Possible correlations between the presence of periodic discharges (PD), Glasgow Coma Scale (GCS) score and early seizure detection (defined as a seizure within the first hour of recording) were explored statistically. Out of 200 patients who underwent video-EEG monitoring, we identified 30 cases (15%; 18 male, 12 female; age 24-86 years; mean recording duration 99 hours) with NCSE. The first seizure was recorded within 0 to 1 hour in 22 patients (73%) and within 1 to 12 hours in 6 patients (22%). Interictal PDs were identified in 19 patients (63%). GCS score was ≤8 in 16 patients (53%). There was no correlation between early seizure detection and PDs (p=1.0) or GCS score (P = .22). In our study, >90% of the seizures were captured within 12 hours. This finding suggests that most of the NCSE cases can be identified even in centers with limited resources. The presence or absence of PDs or GCS score does not predict the timing of the first seizure.

Determinants and outcome of repeat continuous electroencephalogram monitoring-A case-control study

OBJECTIVE

A retrospective, single-center study to analyze the determinants of a repeat continuous EEG (cEEG) monitoring during hospitalization and its outcomes using a matched case-control study design.

METHODS

Adults with a repeat cEEG session (cases) were matched by age (±3 years), gender, and mental status to patients with a single cEEG (controls) during hospitalization. Several clinical and EEG characteristics were analyzed to identify predictors of repeat cEEG. Repeat cEEG outcomes were analyzed based on its yield of electrographic seizure. We investigated the predictors of finding increased epileptic potential (degree of association with electrographic seizures) on the repeat cEEG, a marker for possible anti-epileptic drugs (AEDs) management change.

RESULTS

A total of 213 (8.6% of all unique cEEG patients) cases were included. A multivariable conditional logistic regression model comparing cases and controls showed that the presence of acute brain insult [odds ratio (OR) = 3.36, 95% CI = 1.26-8.94, P = .015], longer hospital admission (OR = 1.11, 95% CI = 1.07-1.15, P < .001) and being on AEDs at the end of index cEEG (OR = 4.0, 95% CI = 1.8-8.87, P < .001) was determinants of a repeat cEEG. Among cases, 17 (8%) had electrographic seizures on repeat cEEG. Increased epileptic potential on repeat cEEG was noted in 34 (16%) cases. The latter is associated with change in etiology after the index cEEG (P = .03) and duration of repeat cEEG (P = .003) based on multivariable logistic regression model. AEDs were changed in 46 (21.6%) patients based on repeat cEEG findings.

SIGNIFICANCE

Repeat cEEG is not an uncommon practice. It leads to the diagnosis of electrographic seizures in a significant percentage of patients. With the potential of impacting AED management in 16%-21% patients, it should be considered in high-risk patients suffering acute brain insults undergoing prolonged hospitalization.

Nonconvulsive seizures and nonconvulsive status epilepticus in the neuro ICU should or should not be treated aggressively: A debate

This article presents a "debate" about the appropriate level of aggressiveness of treatment for nonconvulsive status epilepticus (NCSE), held at the International Congress of Clinical Neurophysiology in Washington D.C. on 4 May 2018. The proposition for discussion was "Nonconvulsive seizures and status epilepticus in the intensive care unit should be treated aggressively." Dr. Andrea O. Rossetti from Lausanne, Switzerland, spoke in support of the proposition and Dr. Lawrence J. Hirsch from New Haven, Connecticut, discussed reasons for rejecting the proposal. Dr. Frank W. Drislane from Boston, Massachusetts, was asked by the conference organizers to add comments and perspective.

Coma and Brain Death

PURPOSE OF REVIEW

This article discusses the diagnostic and therapeutic approach to patients who are comatose and reviews the current knowledge on prognosis from various causes of coma. This article also provides an overview of the principles for determination of brain death as well as advice on how to avoid common pitfalls.

RECENT FINDINGS

Technologic advances have refined our understanding of the physiology of consciousness and the spectrum of disorders of consciousness; they also promise to improve our prognostic accuracy. Yet the clinical principles for the evaluation and treatment of coma remain unaltered. The clinical standards for determination of death by neurologic criteria (ie, brain death) are also well established, although variabilities in local protocols and legal requirements remain a problem to be resolved.

SUMMARY

Effective evaluation of coma demands a systematic approach relying on clinical information to ensure rational use of laboratory and imaging tests. When the cause of coma is deemed irreversible in the setting of a catastrophic brain injury and no clinical evidence exists for brain and brainstem function, patients should be evaluated for the possibility of brain death by following the clinical criteria specified in the American Academy of Neurology guidelines.

Comparison of machine learning models for seizure prediction in hospitalized patients

Objective

To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1‐h screening EEG to identify low‐risk patients (<5% seizures risk in 48 h).

Methods

The Critical Care EEG Monitoring Research Consortium (CCEMRC) multicenter database contains 7716 continuous EEGs (cEEG). Neural networks (NN), elastic net logistic regression (EN), and sparse linear integer model (RiskSLIM) were trained to predict seizures. RiskSLIM was used previously to generate 2HELPS2B model of seizure predictions. Data were divided into training (60% for model fitting) and evaluation (40% for model evaluation) cohorts. Performance was measured using area under the receiver operating curve (AUC), mean risk calibration (CAL), and negative predictive value (NPV). A secondary analysis was performed using Monte Carlo simulation (MCS) to normalize all EEG recordings to 48 h and use only the first hour of EEG as a “screening EEG” to generate predictions.

Results

RiskSLIM recreated the 2HELPS2B model. All models had comparable AUC: evaluation cohort (NN: 0.85, EN: 0.84, 2HELPS2B: 0.83) and MCS (NN: 0.82, EN; 0.82, 2HELPS2B: 0.81) and NPV (absence of seizures in the group that the models predicted to be low risk): evaluation cohort (NN: 97%, EN: 97%, 2HELPS2B: 97%) and MCS (NN: 97%, EN: 99%, 2HELPS2B: 97%). 2HELPS2B model was able to identify the largest proportion of low‐risk patients.

Interpretation

For seizure risk stratification of hospitalized patients, the RiskSLIM generated 2HELPS2B model compares favorably to the complex NN and EN generated models. 2HELPS2B is able to accurately and quickly identify low‐risk patients with only a 1‐h screening EEG.

Update in Neurocritical Care: a summary of the 2018 Paris international conference of the French Society of Intensive Care

The 2018 Paris Intensive Care symposium entitled "Update in Neurocritical Care" was organized in Paris, June 21-22, 2018, under the auspices of the French Intensive Care Society. This 2-day post-graduate educational symposium comprised several chapters, aiming first to provide all-board intensivists with current standards for the clinical assessment of altered consciousness states (including coma and delirium) and peripheral nervous system in critically ill patients, monitoring of brain function (specifically, electro-encephalography) and best practices for sedation-analgesia-delirium management. An update on the treatment of specific severe brain pathologies-including ischaemic/haemorrhagic stroke, cerebral venous thrombosis, hypoxic-ischaemic brain injury, immune-mediated and infectious encephalitis and refractory status epilepticus-was also provided. Finally, we discuss how to approach some difficult decisions, namely the role of decompressive craniectomy and prognostication models in patients with head injury. For each chapter, the scope of the present review was to provide important issues and key messages, provide most recent and relevant literature in the field, and briefly describe new developments in the field.