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

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.

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.

Can Big Data guide prognosis and clinical decisions in epilepsy?

Big Data is no longer a novel concept in health care. Its promise of positive impact is not only undiminished, but daily enhanced by seemingly endless possibilities. Epilepsy is a disorder with wide heterogeneity in both clinical and research domains, and thus lends itself to Big Data concepts and techniques. It is therefore inevitable that Big Data will enable multimodal research, integrating various aspects of "-omics" domains, such as phenome, genome, microbiome, metabolome, and proteome. This scope and granularity have the potential to change our understanding of prognosis and mortality in epilepsy. The scale of new discovery is unprecedented due to the possibilities promised by advances in machine learning, in particular deep learning. The subsequent possibilities of personalized patient care through clinical decision support systems that are evidence-based, adaptive, and iterative seem to be within reach. A major objective is not only to inform decision-making, but also to reduce uncertainty in outcomes. Although the adoption of electronic health record (EHR) systems is near universal in the United States, for example, advanced clinical decision support in or ancillary to EHRs remains sporadic. In this review, we discuss the role of Big Data in the development of clinical decision support systems for epilepsy care, prognostication, and discovery.

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.

Hyperperfusion in the thalamus on arterial spin labelling indicates non-convulsive status epilepticus

Non-convulsive status epilepticus describes the syndrome of unexplained impaired consciousness in critically ill patients. Non-convulsive status epilepticus is very likely to lead to delayed diagnosis and poor outcomes because of the absence of convulsive symptoms. EEG is essential for the diagnosis of non-convulsive status epilepticus to establish the association between periodic discharges and rhythmic delta activity in addition to ictal epileptiform discharges according to the Salzburg criteria. Arterial spin labelling, a type of perfusion MRI, has been applied for rapid and non-invasive evaluation of the ictal state. Ictal cerebral cortical hyperperfusion is the most common finding to demonstrate focal onset seizures. Hyperperfusion of the thalamus on single photon emission computed tomography was found in patients with impaired awareness seizures. We hypothesized that thalamocortical hyperperfusion on arterial spin labelling identifies non-convulsive status epilepticus and such thalamic hyperperfusion specifically associates with periodic/rhythmic discharges producing impaired consciousness without convulsion. We identified 27 patients (17 females; age, 39-91 years) who underwent both arterial spin labelling and EEG within 24 h of suspected non-convulsive status epilepticus. We analysed 28 episodes of suspected non-convulsive status epilepticus and compared hyperperfusion on arterial spin labelling with periodic/rhythmic discharges. We evaluated 21 episodes as a positive diagnosis of non-convulsive status epilepticus according to the Salzburg criteria. We identified periodic discharges in 15 (12 lateralized and 3 bilateral independent) episodes and rhythmic delta activity in 13 (10 lateralized, 1 bilateral independent and 2 generalized) episodes. Arterial spin labelling showed thalamic hyperperfusion in 16 (11 unilateral and 5 bilateral) episodes and cerebral cortical hyperperfusion in 24 (20 unilateral and 4 bilateral) episodes. Thalamic hyperperfusion was significantly associated with non-convulsive status epilepticus (P = 0.0007; sensitivity, 76.2%; specificity, 100%), periodic discharges (P < 0.0001; 93.3%; 84.6%), and rhythmic delta activity (P = 0.0006; 92.3%; 73.3%). Cerebral cortical hyperperfusion was significantly associated with non-convulsive status epilepticus (P = 0.0017; 100%; 57.1%) and periodic discharges (P = 0.0349; 100%; 30.8%), but not with rhythmic delta activity. Thalamocortical hyperperfusion could be a new biomarker of non-convulsive status epilepticus according to the Salzburg criteria in critically ill patients. Specific thalamic hyperexcitability might modulate the periodic discharges and rhythmic delta activity associated with non-convulsive status epilepticus. Impaired consciousness without convulsions could be caused by predominant thalamic hyperperfusion together with cortical hyperperfusion but without ictal epileptiform discharges.

Prediction of EEG Seizures in Critically Ill Children

In a prospective observational study, investigators from the Children’s Hospital of Philadelphia devised a predictive model for capturing electrographic seizures in critically ill pediatric patients.

Continuous EEG in ICU: Not a Luxury After All

Continuous vs Routine Electroencephalogram in Critically Ill Adults With Altered Consciousness and No Recent Seizure: A Multicenter Randomized Clinical Trial

Rossetti AO, Schindler K, Sutter R, Rüegg S, Zubler F, Novy J, Oddo M, Warpelin-Decrausaz L, Alvarez V. JAMA Neurol. 2020;77(10):1-8. doi:10.1001/jamaneurol.2020.2264

Importance:

In critically ill patients with altered consciousness, continuous electroencephalogram (cEEG) improves seizure detection but is resource-consuming compared with routine EEG (rEEG). It is also uncertain whether cEEG has an effect on outcome.

Objective:

To assess whether cEEG is associated with reduced mortality compared with rEEG.

Design, Setting, and Participants:

The pragmatic multicenter Continuous EEG Randomized Trial in Adults was conducted between 2017 and 2018, with follow-up of 6 months. Outcomes were assessed by interviewers blinded to interventions. The study took place at 4 tertiary hospitals in Switzerland (intensive and intermediate care units). Depending on investigators’ availability, we pragmatically recruited critically ill adults having Glasgow Coma Scale scores of 11 or less or Full Outline of Responsiveness score of 12 or less, without recent seizures or status epilepticus. They had cerebral (eg, brain trauma, cardiac arrest, hemorrhage, or stroke) or noncerebral conditions (eg, toxic-metabolic or unknown etiology), and EEG was requested as part of standard care. An independent physician provided emergency informed consent.

Interventions:

Participants were randomized 1:1 to cEEG for 30 to 48 hours versus 2 rEEGs (20 minutes each), interpreted according to standardized American Clinical Neurophysiology Society guidelines.

Main Outcomes and Measures:

Mortality at 6 months represented the primary outcome. Secondary outcomes included interictal and ictal features detection and change in therapy.

Results:

We analyzed 364 (33% women; mean [SD] age, 63 [15] years) patients. At 6 months, mortality was 89 of 182 in those with cEEG and 88 of 182 in those with rEEG (adjusted relative risk [RR]: 1.02; 95% CI: 0.83-1.26; P = .85). Exploratory comparisons within subgroups stratifying patients according to age, premorbid disability, comorbidities on admission, deeper consciousness reduction, and underlying diagnoses revealed no significant effect modification. Continuous EEG was associated with increased detection of interictal features and seizures (adjusted RR: 1.26; 95% CI: 1.08-1.15; P = .004 and 3.37; 95% CI: 1.63-7.00; P = .001, respectively) and more frequent adaptations in anti-seizure therapy (RR: 1.84; 95% CI: 1.12-3.00; P = .01).

Conclusions and Relevance:

This pragmatic trial shows that in critically ill adults with impaired consciousness and no recent seizure, cEEG leads to increased seizure detection and modification of anti-seizure treatment but is not related to improved outcome compared with repeated rEEG. Pending larger studies, rEEG may represent a valid alternative to cEEG in centers with limited resources.

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.

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.