Diagnosis of Nonconvulsive Status Epilepticus (NCSE) in Adults with Altered Mental Status: Clinico - Electroencephalographic Considerations

Commentary on stimulus-induced arousal with transient electroencephalographic improvement distinguishes nonictal from ictal generalized periodic discharges

Here we critique recent arguments proposing to distinguish ictal from non-ictal generalized periodic discharges (GPDs) based on etiology and stimulation response, arguing that these are unreliable. We advocate for an empirical approach to GPDs: describe objectively, interpret through medication trials, and base further treatment on response. We call for evidence-based approaches considering meaningful clinical outcomes.

Differences in functional network between focal onset nonconvulsive status epilepticus and toxic metabolic encephalopathy: application to machine learning models for differential diagnosis

We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.

Clinical Characteristics and Treatment Outcomes of De Novo Nonconvulsive Status Epilepticus: A Retrospective Study

Background and Purpose

Nonconvulsive status epilepticus (NCSE) is challenging to diagnose. This study aimed to describe and classify the clinical features and electroencephalography (EEG) findings of patients with de novo NCSE and to correlate them with clinical outcomes.

Methods

We retrospectively reviewed the medical and EEG records of patients admitted to our institution with altered mentation and EEG abnormalities from January 1, 2013 to December 31, 2018. We evaluated premorbid modified Rankin Scale (mRS) scores, underlying disorders, precipitating factors, clinical manifestations, laboratory tests, and outcomes after a 3-month follow-up. Patients who met the Salzburg Consensus Criteria for NCSE were categorized into good-outcome and poor-outcome groups. A good outcome was defined as 1) clinical and electrographic seizures ceasing after treatment, and 2) an mRS score of ≤2 or remaining unchanged during the 3-month follow-up. A poor outcome was defined as 1) death, 2) seizures continuing despite treatment, or 3) a follow-up mRS score of ≥3 in a patient with a premorbid mRS score of ≤2, or a follow-up mRS score that increased in a patient with a premorbid mRS score of ≥3.

Results

The 48 included patients comprised 37 categorized into the good-outcome group and 11 into the poor-outcome group. The presence of acute metabolic disturbances was significantly correlated with poor outcome (p=0.036), while the other analyzed variables were not significantly correlated with outcomes.

Conclusions

Acute metabolic disturbances in NCSE are associated with poor outcomes. Adequate treatment of underlying reversible disorders alongside controlling seizures is critical for patients with NCSE.

Can endotracheal intubation be the first step in management of nonconvulsive status epilepticus?: A case report

RATIONALE

Nonconvulsive status epilepticus (NCSE) is prolonged seizure activity without motor manifestations. Clinically, there are certain examination findings, in addition to elements of history, that help differentiate it from other encephalopathies. When diagnosing NCSE, the physician faces significant difficulties due to inconsistent clinical presentation and somewhat nonspecific electroencephalogram (EEG) criteria.

PATIENT CONCERNS

To highlight the problems faced when dealing with such a patient, a case of a 29-year-old male who presented with an altered state of consciousness is put forth for the reader. Only after an extensive laboratory and radiological workup had ruled out other causes, an eventual diagnosis was established when clinical features were correlated with suggestive EEG results.

DIAGNOSES

The diagnosis that was reached was NCSE.

INTERVENTIONS

The initial therapeutic interventions generally deployed in such a scenario ultimately failed and consequently the patient had to be sedated and intubated, while being kept on antiepileptic medication.

OUTCOMES

This measure resulted in satisfactory recovery.

LESSONS

Accordingly, we recommend consideration of NCSE in any unconscious patient whose presentation cannot be explained by other disorders. Furthermore, we suggest moving directly to utilizing anesthetic agents and endotracheal intubation, together with anti-epileptic drugs, in the treatment regimen in order to optimize patient outcomes.

Levetiracetam improves disinhibitory behavior in nonconvulsive status epilepticus

BACKGROUND

Nonconvulsive status epilepticus (NCSE) is a severe medical condition and heterogeneous disorder defined by different seizure types and diverse etiologies. NCSE occurs commonly in the elderly and is potentially misdiagnosed as a psychiatric disorder. Current treatment options for NCSE are still unsatisfactory.

CASE PRESENTATION

We report a case of NCSE in a 55-year-old epileptic male patient with a history of infectious encephalitis, disinhibitory behavior, and a suspected diagnosis of frontotemporal dementia. Add-on levetiracetam (LEV) to carbamazepine treatment improved clinical manifestations and abnormal electroencephalographic discharge.

CONCLUSION

With disinhibitory behavior in the elderly, the possibility of NCSE should be considered. Moreover, LEV may be an effective and well-tolerated pharmacotherapy for elderly NCSE patients.

Clinical risk factors for non-convulsive status epilepticus during emergent electroencephalogram

PURPOSE

Emergent electroencephalograms (EmEEG) are performed to exclude non-convulsive status epilepticus (NCSE) but are resource-intensive. Prior studies have identified a seizure or seizures in the acute setting preceding the EmEEG request as a risk factor of NCSE but few other consistent clinical risk factors have been identified. We aimed to identify clinical risk factors for NCSE in EmEEGs

METHODS

We conducted a retrospective analysis of consecutive patients who underwent EmEEG to exclude NCSE over a 20-month period. One blinded investigator extracted clinical information from patient case records using a standardized form. Patients were grouped using EmEEG results into those with and without NCSE. We analyzed differences between these two groups.

RESULTS

A total of 2333 EEGs were performed over the study period, 215 (9.3%) were EmEEGs ordered to exclude NCSE. 21 patients (9.8%) of the 215 patients were found to have NCSE. Three independent clinical risk factors for NCSE were identified--seizure(s) in the acute setting, ocular movements (nystagmus and/or gaze deviation) and ongoing CNS infection. The presence of seizure(s) in the acute setting showed the highest adjusted odds ratio (OR=8.8, 95% CI 2.0-39.4, p=0.005). In addition, prevalence of NCSE increased as more clinical risk factors were present.

CONCLUSION

Seizures in the acute setting, ocular movements and ongoing CNS infection are associated with NCSE. By using these risk factors at the bedside, clinicians can prioritize patients for EmEEG, recognizing that risk of NCSE increases as more clinical risk factors are present.

Antiepileptic drugs for the primary and secondary prevention of seizures after subarachnoid haemorrhage

BACKGROUND

Subarachnoid haemorrhage may result in seizures both acutely and in the longer term. The use of antiepileptic drugs (AEDs) in the primary and secondary prevention of seizures after subarachnoid haemorrhage is uncertain, and there is currently no consensus on treatment.

OBJECTIVES

To assess the effects of AEDs for the primary and secondary prevention of seizures after subarachnoid haemorrhage.

SEARCH METHODS

We searched the Cochrane Epilepsy Group Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL) (2013, Issue 1) in The Cochrane Library, and MEDLINE (1946 to 12th March 2013). We checked the reference lists of articles retrieved from these searches.

SELECTION CRITERIA

We considered all randomised and quasi-randomised controlled trials in which patients were assigned to a treatment (one or more AEDs) or placebo.

DATA COLLECTION AND ANALYSIS

Two review authors (RM and JK) independently screened and assessed the methodological quality of the studies. If studies were included, one author extracted the data and the other checked it.

MAIN RESULTS

No relevant studies were found.

AUTHORS' CONCLUSIONS

There was no evidence to support or refute the use of antiepileptic drugs for the primary or secondary prevention of seizures related to subarachnoid haemorrhage. Well-designed randomised controlled trials are urgently needed to guide clinical practice.

Quantitative EEG analysis for automated detection of nonconvulsive seizures in intensive care units

Because of increased awareness of the high prevalence of nonconvulsive seizures in critically ill patients, use of continuous EEG (cEEG) monitoring is rapidly increasing in ICUs. However, cEEG monitoring is labor intensive, and manual review and interpretation of the EEG are impractical in most ICUs. Effective methods to assist in rapid and accurate detection of nonconvulsive seizures would greatly reduce the cost of cEEG monitoring and enhance the quality of patient care. In this study, we report a preliminary investigation of a novel ICU EEG analysis and seizure detection algorithm. Twenty-four prolonged cEEG recordings were included in this study. Seizure detection sensitivity and specificity were assessed for the new algorithm and for the two commercial seizure detection software systems. The new algorithm performed with a mean sensitivity of 90.4% and a mean false detection rate of 0.066/hour. The two commercial detection products performed with low sensitivities (12.9 and 10.1%) and false detection rates of 1.036/hour and 0.013/hour, respectively. These findings suggest that the novel algorithm has potential to be the basis of clinically useful software that can assist ICU staff in timely identification of nonconvulsive seizures. This study also suggests that currently available seizure detection software does not perform sufficiently in detection of nonconvulsive seizures in critically ill patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Seizures as a cause of altered mental status

The differential diagnosis and empiric management of altered mental status and seizures often overlap. Altered mental status may accompany seizures or simply be the manifestation of a postictal state. This article provides an overview of the numerous causes of altered mental status and seizures: metabolic, toxic, malignant, infectious, and endocrine causes. The article focuses on those agents that should prompt the emergency physician to initiate unique therapy to abate the seizure and correct the underlying cause.

Real-time differentiation of nonconvulsive status epilepticus from other encephalopathies using quantitative EEG analysis: a pilot study

PURPOSE

Distinguishing nonconvulsive status epilepticus (NCSE) from some nonepileptic encephalopathies is a challenging problem. In many situations, NCSE and nonepileptic encephalopathies are indistinguishable by clinical symptoms and can produce very similar electroencephalography (EEG) patterns. Misdiagnosis or delay to diagnosis of NCSE may increase the rate of morbidity and mortality.

METHODS

We developed a fast-differentiating algorithm using quantitative EEG analysis to distinguish NCSE patients from patients with toxic/metabolic encephalopathy (TME). EEG recordings were collected from 11 patients, including 6 with NCSE and 5 with TME. Three nonlinear dynamic measures were used in the proposed algorithm: the maximum short-term Lyapunov exponent (STLmax), phase of attractor (phase/angular frequency), and approximate entropy (ApEn). A further refined metric derived from STLmax and phase of attractor (the mean distance to EEG epoch samples from their centroid in the feature space) was also utilized as a criterion. Paired t tests were carried out to further clarify the separation between the EEG patterns of NCSE and TME.

RESULTS

Computational results showed that the performance of the proposed algorithm was sufficient to distinguish NCSE from TME. The results were consistent in all subjects in our study.

CONCLUSIONS

The study presents evidence that the maximum short-term Lyapunov exponents (STLmax) and phase of attractors (phase/angular frequency) can be useful in assisting clinical diagnosis of NCSE. Findings presented in this article provide a promising indication that the proposed algorithm may correctly distinguish NCSE from TME. Although the exact mechanism of this association remains unknown, the authors suggest that epileptic activity is highly associated with and can be modeled by dynamic systems.