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

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.

Utility and rationale for continuous EEG monitoring: a primer for the general intensivist

This review offers a comprehensive guide for general intensivists on the utility of continuous EEG (cEEG) monitoring for critically ill patients. Beyond the primary role of EEG in detecting seizures, this review explores its utility in neuroprognostication, monitoring neurological deterioration, assessing treatment responses, and aiding rehabilitation in patients with encephalopathy, coma, or other consciousness disorders. Most seizures and status epilepticus (SE) events in the intensive care unit (ICU) setting are nonconvulsive or subtle, making cEEG essential for identifying these otherwise silent events. Imaging and invasive approaches can add to the diagnosis of seizures for specific populations, given that scalp electrodes may fail to identify seizures that may be detected by depth electrodes or electroradiologic findings. When cEEG identifies SE, the risk of secondary neuronal injury related to the time-intensity "burden" often prompts treatment with anti-seizure medications. Similarly, treatment may be administered for seizure-spectrum activity, such as periodic discharges or lateralized rhythmic delta slowing on the ictal-interictal continuum (IIC), even when frank seizures are not evident on the scalp. In this setting, cEEG is utilized empirically to monitor treatment response. Separately, cEEG has other versatile uses for neurotelemetry, including identifying the level of sedation or consciousness. Specific conditions such as sepsis, traumatic brain injury, subarachnoid hemorrhage, and cardiac arrest may each be associated with a unique application of cEEG; for example, predicting impending events of delayed cerebral ischemia, a feared complication in the first two weeks after subarachnoid hemorrhage. After brief training, non-neurophysiologists can learn to interpret quantitative EEG trends that summarize elements of EEG activity, enhancing clinical responsiveness in collaboration with clinical neurophysiologists. Intensivists and other healthcare professionals also play crucial roles in facilitating timely cEEG setup, preventing electrode-related skin injuries, and maintaining patient mobility during monitoring.

The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials

Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction. Machine learning and artificial intelligence are data-driven techniques integrated with neurodiagnostic monitoring technologies that attempt to accomplish both of those tasks. In this narrative review, we describe both the existing software and hardware approaches for seizure detection and forecasting, as well as the concepts for how to evaluate the performance of new technologies for future application in clinical practice. These technologies include long-term monitoring both with and without electroencephalography (EEG) that report very high sensitivity as well as reduced false positive detections. In addition, we describe the implications of seizure detection and forecasting upon the evaluation of novel treatments for seizures within clinical trials. Based on these existing data, long-term seizure detection and forecasting with machine learning and artificial intelligence could fundamentally change the clinical care of people with seizures, but there are multiple validation steps necessary to rigorously demonstrate their benefits and costs, relative to the current standard.

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

PURPOSE OF REVIEW

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

RECENT FINDINGS

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.

Neurologic Complications of Critical Medical Illness

OBJECTIVE

This article reviews the neurologic complications encountered in patients admitted to non-neurologic intensive care units, outlines various scenarios in which a neurologic consultation can add to the diagnosis or management of a critically ill patient, and provides advice on the best diagnostic approach in the evaluation of these patients.

LATEST DEVELOPMENTS

Increasing recognition of neurologic complications and their adverse impact on long-term outcomes has led to increased neurology involvement in non-neurologic intensive care units. The COVID-19 pandemic has highlighted the importance of having a structured clinical approach to neurologic complications of critical illness as well as the critical care management of patients with chronic neurologic disabilities.

ESSENTIAL POINTS

Critical illness is often accompanied by neurologic complications. Neurologists need to be aware of the unique needs of critically ill patients, especially the nuances of the neurologic examination, challenges in diagnostic testing, and neuropharmacologic aspects of commonly used medications.

Spectrum of Ictal-Interictal Continuum: The Significance of 2HELPS2B Score and Background Suppression

PURPOSE

The aims of this study were to identify (1) the spectrum of ictal-interictal continuum (IIC) using the two dimensions of 2HELPS2B score and background suppression and (2) the response to subsequent anti-seizure drugs depends on the spectrum of IIC.

METHODS

The study prospectively enrolled 62 patients with IIC on EEG. The diagnosis of nonconvulsive status epilepticus was attempted with Salzburg criteria as well as clinical and neuroimaging data. IICs were dichotomized into patients with nonconvulsive status epilepticus and coma-IIC. The 2HELPS2B score was evaluated as the original proposal. The suppression ratio was analyzed with Persyst software.

RESULTS

Forty-seven cases (75.8%) were nonconvulsive status epilepticus-IIC and 15 cases (24.2%) were coma-IIC. Multivariate analysis revealed that the 2HELPS2B score was the only significant variable dichotomizing the spectrum of IIC (odds ratio, 3.0; 95% confidence interval, 1.06-8.6; P = 0.03 for nonconvulsive status epilepticus-IIC). In addition, the suppression ratio was significantly negatively correlated with 2HELPS2B scores (Spearman coefficient = -0.37, P = 0.004 for left hemisphere and Spearman coefficient = -0.3, P = 0.02 for right hemisphere). Furthermore, patients with higher 2HELPS2B score (74% [14/19] in ≥2 points vs. 44% [14/32] in <2 points, P = 0.03 by χ 2 test) and lower suppression ratio (62% [23/37] in ≤2.18 vs. 35% [6/17] in >2.18, P = 0.06 by χ 2 test) seemed to be more responsive to subsequent anti-seizure drug.

CONCLUSIONS

The 2HELPS2B score and background suppression can be used to distinguish the spectrum of IIC and thereby predict the response to subsequent anti-seizure drug.

EEG: Current relevance and promising quantitative analyses

Electroencephalography (EEG) remains an essential tool, characterized by an excellent temporal resolution and offering a real window on cerebral functions. Surface EEG signals are mainly generated by the postsynaptic activities of synchronously activated neural assemblies. EEG is also a low-cost tool, easy to use at bed-side, allowing to record brain electrical activities with a low number or up to 256 surface electrodes. For clinical purpose, EEG remains a critical investigation for epilepsies, sleep disorders, disorders of consciousness. Its temporal resolution and practicability also make EEG a necessary tool for cognitive neurosciences and brain-computer interfaces. EEG visual analysis is essential in clinical practice and the subject of recent progresses. Several EEG-based quantitative analyses may complete the visual analysis, such as event-related potentials, source localizations, brain connectivity and microstates analyses. Some developments in surface EEG electrodes appear also, potentially promising for long term continuous EEGs. We overview in this article some recent progresses in visual EEG analysis and promising quantitative analyses.

Mass Spectrometry-Based Proteomics Analysis of Human Substantia Nigra From Parkinson's Disease Patients Identifies Multiple Pathways Potentially Involved in the Disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra (SN) of the brain. Despite decades of studies, the precise pathogenic mechanism of PD is still elusive. An unbiased proteomic analysis of PD patient's brain allows the identification of critical proteins and molecular pathways that lead to dopamine cell death and α-synuclein deposition and the resulting devastating clinical symptoms. In this study, we conducted an in-depth proteome analysis of human SN tissues from 15 PD patients and 15 healthy control individuals combining Orbitrap mass spectrometry with the isobaric tandem mass tag-based multiplexing technology. We identified 10,040 proteins with 1140 differentially expressed proteins in the SN of PD patients. Pathway analysis showed that the ribosome pathway was the most enriched one, followed by gamma-aminobutyric acidergic synapse, retrograde endocannabinoid signaling, cell adhesion molecules, morphine addiction, Prion disease, and PD pathways. Strikingly, the majority of the proteins enriched in the ribosome pathway were mitochondrial ribosomal proteins (mitoribosomes). The subsequent protein-protein interaction analysis and the weighted gene coexpression network analysis confirmed that the mitoribosome is the most enriched protein cluster. Furthermore, the mitoribosome was also identified in our analysis of a replication set of ten PD and nine healthy control SN tissues. This study provides potential disease pathways involved in PD and paves the way to study further the pathogenic mechanism of PD.

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.

Adult Critical Care Electroencephalography Monitoring for Seizures: A Narrative Review

Electroencephalography (EEG) is an important and relatively inexpensive tool that allows intensivists to monitor cerebral activity of critically ill patients in real time. Seizure detection in patients with and without acute brain injury is the primary reason to obtain an EEG in the Intensive Care Unit (ICU). In response to the increased demand of EEG, advances in quantitative EEG (qEEG) created an approach to review large amounts of data instantly. Finally, rapid response EEG is now available to reduce the time to detect electrographic seizures in limited-resource settings. This review article provides a concise overview of the technical aspects of EEG monitoring for seizures, clinical indications for EEG, the various available modalities of EEG, common and challenging EEG patterns, and barriers to EEG monitoring in the ICU.

Cortical Spreading Depolarizations and Clinically Measured Scalp EEG Activity After Aneurysmal Subarachnoid Hemorrhage and Traumatic Brain Injury

BACKGROUND

Spreading depolarizations (SDs) are associated with worse outcome following subarachnoid hemorrhage (SAH) and traumatic brain injury (TBI), but gold standard detection requires electrocorticography with a subdural strip electrode. Electroencephalography (EEG) ictal-interictal continuum abnormalities are associated with poor outcomes after TBI and with both delayed cerebral ischemia (DCI) and poor outcomes after SAH. We examined rates of SD detection in patients with SAH and TBI with intraparenchymal and subdural strip electrodes and assessed which continuous EEG (cEEG) measures were associated with intracranially quantified SDs.

METHODS

In this single-center cohort, we included patients with SAH and TBI undergoing ≥ 24 h of interpretable intracranial monitoring via eight-contact intraparenchymal or six-contact subdural strip platinum electrodes or both. SDs were rated according to established consensus criteria and compared with cEEG findings rated according to the American Clinical Neurophysiology Society critical care EEG monitoring consensus criteria: lateralized rhythmic delta activity, generalized rhythmic delta activity, lateralized periodic discharges, generalized periodic discharges, any ictal-interictal continuum, or a composite scalp EEG tool for seizure risk estimation: the 2HELPS2B score. Among patients with SAH, cEEG was assessed for validated DCI biomarkers: new or worsening epileptiform abnormalities and new background deterioration.

RESULTS

Over 6 years, SDs were recorded in 5 (18%) of 28 patients recorded with intraparenchymal electrodes and 4 (40%) of 10 patients recorded with subdural strip electrodes. There was no significant association between occurrence of SDs and day 1 cEEG findings (American Clinical Neurophysiology Society main terms lateralized periodic discharges, generalized periodic discharges, lateralized rhythmic delta activity, or seizures, individually or in combination). After SAH, established cEEG DCI predictors were not associated with SDs.

CONCLUSIONS

Intraparenchymal recordings yielded low rates of SD, and documented SDs were not associated with ictal-interictal continuum abnormalities or other cEEG DCI predictors. Identifying scalp EEG correlates of SD may require training computational EEG analytics and use of gold standard subdural strip electrocorticography recordings.

Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method.

Serum neuron-specific enolase: a new tool for seizure risk monitoring after status epilepticus

BACKGROUND AND PURPOSE

There is a need for accurate biomarkers to monitor electroencephalography (EEG) activity and assess seizure risk in patients with acute brain injury. Seizure recurrence may lead to cellular alterations and subsequent neurological sequelae. Whether neuron-specific enolase (NSE) and S100-beta (S100B), brain injury biomarkers, can reflect EEG activity and help to evaluate the seizure risk was investigated.

METHODS

Eleven patients, admitted to an intensive care unit for refractory status epilepticus, who underwent a minimum of 3 days of continuous EEG concomitantly with daily serum NSE and S100B assays were included. At 103 days the relationships between serum NSE and S100B levels and two EEG scores able to monitor the seizure risk were investigated. Biochemical biomarker thresholds able to predict seizure recurrence were sought.

RESULTS

Only NSE levels positively correlated with EEG scores. Similar temporal dynamics were observed for the time courses of EEG scores and NSE levels. NSE levels above 17 ng/ml were associated with seizure in 71% of patients. An increase of more than 15% of NSE levels was associated with seizure recurrence in 80% of patients.

CONCLUSIONS

Our study highlights the potential of NSE as a biomarker of EEG activity and to assess the risk of seizure recurrence.

Seizure Prophylaxis After Spontaneous Intracerebral Hemorrhage

Importance

Limited evidence is available concerning optimal seizure prophylaxis after spontaneous intracerebral hemorrhage (sICH).

Objective

To evaluate which of 4 seizure prophylaxis strategies provides the greatest net benefit for patients with sICH.

Design, Setting, and Participants

This decision analysis used models to simulate the following 4 common scenarios: (1) a 60-year-old man with low risk of early (≤7 days after stroke) (10%) and late (3.6% or 9.8%) seizures and average risk of short- (9%) and long-term (30%) adverse drug reaction (ADR); (2) an 80-year-old woman with low risk of early (10%) and late (3.6% or 9.8%) seizures and high short- (24%) and long-term (80%) ADR risks; (3) a 55-year-old man with high risk of early (19%) and late (34.8% or 46.2%) seizures and low short- (9%) and long-term (30%) ADR risks; and (4) a 45-year-old woman with high risk of early (19%) and late (34.8% or 46.2%) seizures and high short- (18%) and long-term (60%) ADR risks.

Interventions

The following 4 antiseizure drug strategies were included: (1) conservative, consisting of short-term (7-day) secondary early-seizure prophylaxis with long-term therapy after late seizure; (2) moderate, consisting of long-term secondary early-seizure prophylaxis or late-seizure therapy; (3) aggressive, consisting of long-term primary prophylaxis; and (4) risk guided, consisting of short-term secondary early-seizure prophylaxis among low-risk patients (2HELPS2B score, 0), short-term primary prophylaxis among patients at higher risk (2HELPS2B score, ≥1), and long-term secondary therapy for late seizure.

Main Outcomes and Measures

Quality-adjusted life-years (QALYs).

Results

For scenario 1, the risk-guided strategy (8.13 QALYs) was preferred over the conservative (8.08 QALYs), moderate (8.07 QALYs), and aggressive (7.88 QALYs) strategies. For scenario 2, the conservative strategy (2.18 QALYs) was preferred over the risk-guided (2.17 QALYs), moderate (2.09 QALYs), and aggressive (1.15 QALYs) strategies. For scenario 3, the aggressive strategy (9.21 QALYs) was preferred over the risk-guided (8.98 QALYs), moderate (8.93 QALYs), and conservative (8.77 QALYs) strategies. For scenario 4, the risk-guided strategy (11.53 QALYs) was preferred over the conservative (11.23 QALYs), moderate (10.93 QALYs), and aggressive (8.08 QALYs) strategies. Sensitivity analyses suggested that short-term strategies (conservative and risk guided) are preferred under most scenarios, and the risk-guided strategy performs comparably to or better than alternative strategies in most settings.

Conclusions and Relevance

This decision analytical model suggests that short-term (7-day) prophylaxis dominates longer-term therapy after sICH. Use of the 2HELPS2B score to guide clinical decisions for initiation of short-term primary vs secondary early-seizure prophylaxis should be considered for all patients after sICH.

Evaluation and Management of Seizures and Status Epilepticus

Seizures are frequently triggered by an inciting event and result from uninhibited excitation and/or decreased inhibition of a pool of neurons. If physiologic seizure abortive mechanisms fail, the ensuing unrestrained synchronization of neurons-status epilepticus-can be life-threatening and is associated with the potential for marked morbidity in survivors and high medical care costs. Prognosis is intimately related to etiology and its response to therapeutic measures. Timely implementation of pharmacologic therapy while concurrently performing a stepwise workup for etiology are paramount. Neurodiagnostic testing should guide titration of pharmacologic therapies, and help determine if there is a role for immune modulation.

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.

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.

Continuous EEG monitoring in the follow-up of convulsive status epilepticus patients: A proposal and preliminary validation of an EEG-based seizure build-up score (EaSiBUSSEs)

Continuous electroencephalography (EEG) is a major tool for monitoring patients admitted to the intensive care unit after refractory convulsive status epilepticus, following control of convulsive movements. We review the values of different EEG patterns observed in critically ill patients for prognosis and seizure risk, together with proposed criteria for non-convulsive status epilepticus diagnosis (Salzburg Criteria), the EEG scores for prognosis (Epidemiology-based Mortality score in Status Epilepticus, EMSE) and for seizure risk (2HELPS2B). These criteria and scores, based partially on continuous EEG, are not tailored to repetitively monitor the progressive build-up leading to seizure or status epilepticus recurrence. Therefore, we propose a new EEG-based seizure build-up score in status epilepticus (EaSiBUSSEs), based on the morphology and the prevalence of the EEG patterns observed in the follow-up of convulsive status epilepticus patients. It displays subscores from the least (no interictal activity) to the most associated with seizures (focal or generalized status epilepticus). We then evaluated the performance of the EaSiBUSSEs in a cohort of eleven patients who were admitted to intensive care unit for convulsive status epilepticus and who underwent continuous EEG recording. The receiver operating curve revealed good accuracy in identifying patients who would have seizures in the next 24 h, with excellent intra- and inter-rater reliability. We believe that this score is simple to perform, and suitable for repeated monitoring of EEG following refractory convulsive status epilepticus, with quantitative description of major EEG changes leading to seizures.

DIagnostic Subdural EEG electrodes And Subdural hEmatoma (DISEASE): a study protocol for a prospective nonrandomized controlled trial

Background

Epileptic seizures are common clinical features in patients with acute subdural hematoma (aSDH); however, diagnostic feasibility and therapeutic monitoring remain limited. Surface electroencephalography (EEG) is the major diagnostic tool for the detection of seizures but it might be not sensitive enough to detect all subclinical or nonconvulsive seizures or status epilepticus. Therefore, we have planned a clinical trial to evaluate a novel treatment modality by perioperatively implanting subdural EEG electrodes to diagnose seizures; we will then treat the seizures under therapeutic monitoring and analyze the clinical benefit.

Methods

In a prospective nonrandomized trial, we aim to include 110 patients with aSDH. Only patients undergoing surgical removal of aSDH will be included; one arm will be treated according to the guidelines of the Brain Trauma Foundation, while the other arm will additionally receive a subdural grid electrode. The study’s primary outcome is the comparison of incidence of seizures and time-to-seizure between the interventional and control arms. Invasive therapeutic monitoring will guide treatment with antiseizure drugs (ASDs). The secondary outcome will be the functional outcome for both groups as assessed via the Glasgow Outcome Scale and modified Rankin Scale both at discharge and during 6 months of follow-up. The tertiary outcome will be the evaluation of chronic epilepsy within 2–4 years of follow-up.

Discussion

The implantation of a subdural EEG grid electrode in patients with aSDH is expected to be effective in diagnosing seizures in a timely manner, facilitating treatment with ASDs and monitoring of treatment success. Moreover, the occurrence of epileptiform discharges prior to the manifestation of seizure patterns could be evaluated in order to identify high-risk patients who might benefit from prophylactic treatment with ASDs.

Trial registration

ClinicalTrials.gov identifier no. NCT04211233.