Definition and Classification of Periodic and Rhythmic Patterns

Periodic electroencephalographic discharges and epileptic spasms involve cortico-striatal-thalamic loops on Arterial Spin Labeling Magnetic Resonance Imaging

Periodic discharges are a rare peculiar electroencephalogram pattern, occasionally associated with motor or other clinical manifestations, usually observed in critically ill patients. Their underlying pathophysiology remains poorly understood. Epileptic spasms in clusters and periodic discharges with motor manifestations share similar electroencephalogram pattern and some aetiologies of unfavourable prognosis such as subacute sclerosing panencephalitis or herpes encephalitis. Arterial spin labelling magnetic resonance imaging identifies localizing ictal and inter-ictal changes in neurovascular coupling, therefore assumed able to reveal concerned cerebral structures. Here, we retrospectively analysed ictal and inter-ictal arterial spin labelling magnetic resonance imaging in patients aged 6 months to 15 years (median 3 years 4 months) with periodic discharges including epileptic spasms, and compared these findings with those of patients with drug-resistant focal epilepsy who never presented periodic discharges nor epileptic spasms as well as to those of age-matched healthy controls. Ictal electroencephalogram was recorded either simultaneously with arterial spin labelling magnetic resonance imaging or during the close time lapse of patients' periodic discharges, whereas inter-ictal examinations were performed during the patients' active epilepsy but without seizures during the arterial spin labelling magnetic resonance imaging. Ictal arterial spin labelling magnetic resonance imaging was acquired in five patients with periodic discharges [subacute sclerosing panencephalitis (1), stroke-like events (3), West syndrome with cortical malformation (1), two of them also had inter-ictal arterial spin labelling magnetic resonance imaging]. Inter-ictal group included patients with drug-resistant epileptic spasms of various aetiologies (14) and structural drug-resistant focal epilepsy (8). Cortex, striatum and thalamus were segmented and divided in six functional subregions: prefrontal, motor (rostral, caudal), parietal, occipital and temporal. Rest cerebral blood flow values, absolute and relative to whole brain, were compared with those of age-matched controls for each subregion. Main findings were diffuse striatal as well as cortical motor cerebral blood flow increase during ictal examinations in generalized periodic discharges with motor manifestations (subacute sclerosing panencephalitis) and focal cerebral blood flow increase in corresponding cortical-striatal-thalamic subdivisions in lateralized periodic discharges with or without motor manifestations (stroke-like events and asymmetrical epileptic spasms) with straight topographical correlation with the electroencephalogram focus. For inter-ictal examinations, patients with epileptic spasms disclosed cerebral blood flow changes in corresponding cortical-striatal-thalamic subdivisions (absolute-cerebral blood flow decrease and relative-cerebral blood flow increase), more frequently when compared with the group of drug-resistant focal epilepsies, and not related to Vigabatrin treatment. Our results suggest that corresponding cortical-striatal-thalamic circuits are involved in periodic discharges with and without motor manifestations, including epileptic spasms, opening new insights in their pathophysiology and new therapeutical perspectives. Based on these findings, we propose a model for the generation of periodic discharges and of epileptic spasms combining existing pathophysiological models of cortical-striatal-thalamic network dynamics.

Periodic and rhythmic patterns in critically ill children: Incidence, interrater agreement, and seizures

OBJECTIVES

We aimed to determine the incidence of periodic and rhythmic patterns (PRP), assess the interrater agreement between electroencephalographers scoring PRP using standardized terminology, and analyze associations between PRP and electrographic seizures (ES) in critically ill children.

METHODS

This was a prospective observational study of consecutive critically ill children undergoing continuous electroencephalographic monitoring (CEEG). PRP were identified by one electroencephalographer, and then two pediatric electroencephalographers independently scored the first 1-h epoch that contained PRP using standardized terminology. We determined the incidence of PRPs, evaluated interrater agreement between electroencephalographers scoring PRP, and evaluated associations between PRP and ES.

RESULTS

One thousand three hundred ninety-nine patients underwent CEEG. ES occurred in 345 (25%) subjects. PRP, ES + PRP, and ictal-interictal continuum (IIC) patterns occurred in 142 (10%), 81 (6%), and 93 (7%) subjects, respectively. The most common PRP were generalized periodic discharges (GPD; 43, 30%), lateralized periodic discharges (LPD; 34, 24%), generalized rhythmic delta activity (GRDA; 34, 24%), bilateral independent periodic discharges (BIPD; 14, 10%), and lateralized rhythmic delta activity (LRDA; 11, 8%). ES risk varied by PRP type (p < .01). ES occurrence was associated with GPD (odds ratio [OR] = 6.35, p < .01), LPD (OR = 10.45, p < .01), BIPD (OR = 6.77, p < .01), and LRDA (OR = 6.58, p < .01). Some modifying features increased the risk of ES for each of those PRP. GRDA was not significantly associated with ES (OR = 1.34, p = .44). Each of the IIC patterns was associated with ES (OR = 6.83-8.81, p < .01). ES and PRP occurred within 6 h (before or after) in 45 (56%) subjects.

SIGNIFICANCE

PRP occurred in 10% of critically ill children who underwent CEEG. The most common patterns were GPD, LPD, GRDA, BIPD, and LRDA. The GPD, LPD, BIPD, LRDA, and IIC patterns were associated with ES. GRDA was not associated with ES.

Can Spectral Power Be Used as a Candidate Seizure Marker of the Periodic Discharges Pattern?

Introduction: It remains controversial whether the periodic discharges (PDs) pattern is an ictal or interictal phenomenon. The aims of the study are to apply time-frequency and power spectrum analysis to study the PDs pattern and prediction of seizures.

Methods: We retrospectively searched continuous electroencephalography (cEEG) recordings to identify patients exhibiting the PDs pattern. Artifact-free cEEG segments demonstrating the PDs pattern with stable baselines were chosen for time-frequency and power spectrum analysis.

Results: In total, 72 patients (1.3%) exhibited the PDs pattern, with a mean age 36.0 ± 20.7 years (range, 8–76 years). The median spectral power of PDs with a length of 60 s was 70.94 μV² and that of PDs with a length of 20 s was 195.80 μV². During follow-up, patients with spectral power of PDs of length 60 and 20 s lower than 28.65 and 36.09 μV², respectively, exhibited no seizure. For predicting seizures, when the spectral power for PDs of 60 and 20 s equaled to 17.26 and 21.40 μV², respectively, the diagnostic sensitivity was 100% and specificity was 86%. The locations of maximal spectral power of PDs, crude seizure onset zone (SOZ) judged from scalp EEG, and the most prominent regions of hyper- or hypo-metabolism on FDG-PET were congruent.

Conclusions: Spectral power might be a candidate seizure marker of the PDs pattern. High spectral power predicted a high risk of seizures, and low spectral power was associated with a low risk of seizures.

Hemodynamic Changes Associated with Lateralized Periodic Discharges: A Near-Infrared Spectroscopy and Continuous EEG Study

BACKGROUND

Lateral periodic discharges (LPDs) have been recognized as a common electroencephalographic (EEG) pattern in critically ill patients. However, management decisions in these patients are still a challenge for clinicians. This study investigates hemodynamic changes associated with LPDs and evaluates if this pattern is likely to represent an ictal, interictal, or ictal-interictal continuum phenomenon via non-invasive near infra-red spectroscopy (NIRS) with concurrent with continuous EEG.

METHODS

Seventeen patients admitted to the intensive care unit with LPDs on continuous electroencephalogram (EEG) were included. Participants engaged in NIRS recording-with scalp probes on right and left frontal regions simultaneously. Associations between LPDs laterality, primary frequency, NIRS a of cerebral oxygen saturation (SO₂), total hemoglobin concentration (tHb), oxygenated hemoglobin concentration (O₂Hb), de-oxygenated hemoglobin concentration (HHb), and variables in participant medical history were studied.

RESULTS

Hemispheres with LPDs showed higher overall SO₂ when compared to non-LPDs hemispheres (57% vs 52%, p = 0.03). Additionally, mildly increased tHb, O₂Hb, and mildly decreased HHb concentrations were detected in the hemisphere showing LPDs, but changes were not statistically significant. A higher primary frequency of LPDs was associated with lower cerebral SO₂ (Pearson correlation r =  - 0.55, p = 0.022) and O₂Hb (Pearson correlation r =  - 0.52, p = 0.033). In patients with seizure during their EEG recording (64.7%), lower tHb (28.2 μmol/L vs 37.8 μmol/L, p = 0.049) and O₂Hb (15.5 μmol/L vs 24.2 μmol/L, p = 0.033) were recorded in the LPDs hemisphere.

CONCLUSIONS

This study demonstrates an increased cerebral SO₂ in the hemisphere with LPDs, and decreased SO₂ and O₂Hb when the frequency of LPDs increases. The findings indicate that LPDs increase oxygen demand on the ipsilateral hemisphere. We infer that a threshold of LPDs frequency might exit, when the cerebral oxygen demand begins to supersede the ability of delivery, and saturation decreases.

Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method

INTRODUCTION

Current electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients' clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures. We aimed to test the performance of a recently FDA-cleared machine learning method (Claritγ, Ceribell Inc.) that measures the burden of seizure activity in real time and generates bedside alerts for possible status epilepticus (SE).

METHODS

We retrospectively identified adult patients (n = 353) who underwent evaluation of possible seizures with Rapid Response EEG system (Rapid-EEG, Ceribell Inc.). Automated detection of seizure activity and seizure burden throughout a recording (calculated as the percentage of ten-second epochs with seizure activity in any 5-min EEG segment) was performed with Claritγ, and various thresholds of seizure burden were tested (≥ 10% indicating ≥ 30 s of seizure activity in the last 5 min, ≥ 50% indicating ≥ 2.5 min of seizure activity, and ≥ 90% indicating ≥ 4.5 min of seizure activity and triggering a SE alert). The sensitivity and specificity of Claritγ's real-time seizure burden measurements and SE alerts were compared to the majority consensus of at least two expert neurologists.

RESULTS

Majority consensus of neurologists labeled the 353 EEGs as normal or slow activity (n = 249), highly epileptiform patterns (HEP, n = 87), or seizures [n = 17, nine longer than 5 min (e.g., SE), and eight shorter than 5 min]. The algorithm generated a SE alert (≥ 90% seizure burden) with 100% sensitivity and 93% specificity. The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82% for ≥ 50% seizure burden and 88% and 60% for ≥ 10% seizure burden. Of the 179 EEG recordings in which the algorithm detected no seizures, seizures were identified by the expert reviewers in only two cases, indicating a negative predictive value of 99%.

DISCUSSION

Claritγ detected SE events with high sensitivity and specificity, and it demonstrated a high negative predictive value for distinguishing nonepileptiform activity from seizure and highly epileptiform activity.

CONCLUSIONS

Ruling out seizures accurately in a large proportion of cases can help prevent unnecessary or aggressive over-treatment in critical care settings, where empiric treatment with antiseizure medications is currently prevalent. Claritγ's high sensitivity for SE and high negative predictive value for cases without epileptiform activity make it a useful tool for triaging treatment and the need for urgent neurological consultation.

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.

Neurocritical Care Outcomes, Research, and Technology: A Review

Importance

Neurocritical care has grown into an organized specialty that may have consequences for patient care, outcomes, research, and neurointensive care (neuroICU) technology.

Observations

Neurocritical care improves care and outcomes of the patients who are neurocritically ill, and neuroICUs positively affect the financial state of health care systems. The development of neurocritical care as a recognized subspecialty has fostered multidisciplinary research, neuromonitoring, and neurocritical care information technology, with advances and innovations in practice and progress.

Conclusions and Relevance

Neurocritical care has become an important part of health systems and an established subspecialty of neurology. Understanding its structure, scope of practice, consequences for care, and research are important.