On-demand EEG education through competition - A novel, app-based approach to learning to identify interictal epileptiform discharges.

Objective: Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG. Methods: We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings. Results: Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice. Conclusions: Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback. Significance: This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation.

BACKGROUND AND OBJECTIVES: Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. METHODS: We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. RESULTS: SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. DISCUSSION: SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs.

BACKGROUND AND OBJECTIVES: The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. METHODS: This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. RESULTS: Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. DISCUSSION: Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.

Measuring expertise in identifying interictal epileptiform discharges.

OBJECTIVE: Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills. METHODS: A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader's skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision. RESULTS: Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were -0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identified that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). SIGNIFICANCE: Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.

Electroencephalographic changes in purified pharmaceutical cannabidiol therapy.

OBJECTIVE: Evaluate electroencephalographic changes in patients receiving purified pharmaceutical cannabidiol (CBD). METHODS: A total of 104 EEG studies from 52 patients with pediatric-onset refractory epilepsy, who were enrolled in the FDA-approved expanded access investigational new drug program, were retrospectively analyzed for electroencephalographic changes in the background, interictal epileptiform discharges (IEDs), ictal findings, and sleep architecture after CBD treatment. RESULTS: Patients were between 18 months and 52 years of age. After CBD treatment, 88.4% (46/52) of patients had EEG changes. Eighty-nine percent of these patients had changes in their background, 74% in IEDs, 46% in ictal findings, and 17% in sleep architecture. Seven out of 52 patients had modified hypsarrhythmia on their pre-treatment EEG. The pattern resolved in 2/7 patients (29%), diminished in prevalence in 4/7 (57%) subjects, and remained unchanged in 1/7 (14%) cases. Electrographic improvement was seen in 70% (32/46) of the patients, and worsening in 7% (3/46) of the cases. At the post-CBD EEG, 83% had a reduction in the frequency of the most predominant seizure type, and 25% reported subjective cognitive improvement. Of these patients, 88% (p = 0.09) and 92% (p = 0.45) had corresponding EEG changes, respectively. CONCLUSION: Our results revealed electrographic changes in association with the CBD treatment. Despite these changes, a substantial association between specific electrographic findings and clinical outcomes was not established.

Hemisphere-Dependent Ictal Tachycardia Versus Ictal Bradycardia in a Critically Ill Patient.

SUMMARY: Tachycardia is a common ictal phenomenon; however, ictal bradycardia is less commonly reported and rarely presents as ictal asystole/syncope. In critically ill patients, seizures are much less likely to manifest with overt clinical signs, i.e., are more likely to be subtle or nonconvulsive. In this setting, changes in heart rate may be the only clue that seizures are occurring. The authors report an exemplary case of a 78-year-old right-handed man who presented with spontaneous left frontal intraparenchymal hemorrhages. During standard clinical monitoring in the Neuro-Intensive Care Unit, the patient had discrete paroxysms of relative sinus tachycardia, independent episodes of sinus bradycardia, and 3 to 4 seconds of sinus pause. The cardiac investigation was unrevealing, but continuous EEG revealed the answer. The episodes of mild tachycardia were associated with seizures from the left temporal region, whereas those with bradycardia were associated with independent seizures from the right temporal region. The case stands as a stark reminder to remain vigilant of seizures in high-risk patients, especially as a cause for paroxysmal autonomic changes.

Continuous EEG Findings in Autoimmune Encephalitis.

PURPOSE: Autoimmune encephalitis (AE) is a cause of new-onset seizures, including new-onset refractory status epilepticus, yet there have been few studies assessing the EEG signature of AE. METHODS: Multicenter retrospective review of patients diagnosed with AE who underwent continuous EEG monitoring. RESULTS: We identified 64 patients (male, 39%; white, 49%; median age, 44 years); of whom, 43 (67%) were antibody-proven AE patients. Of the patients with confirmed antibody AE, the following were identified: N-methyl-D-aspartate receptor (n = 17, 27%), voltage-gated potassium channel (n = 16, 25%), glutamic acid decarboxylase (n = 6, 9%), and other (n = 4, 6%). The remaining patients were classified as probable antibody-negative AE (n = 11, 17%), definite limbic encephalitis (antibody-negative) (n = 2, 3%), and Hashimoto encephalopathy (n = 8, 13%). Fifty-three percent exhibited electrographic seizures. New-onset refractory status epilepticus was identified in 19% of patients. Sixty-three percent had periodic or rhythmic patterns; of which, 38% had plus modifiers. Generalized rhythmic delta activity was identified in 33% of patients. Generalized rhythmic delta activity and generalized rhythmic delta activity plus fast activity were more common in anti-N-methyl-D-aspartate AE (P = 0.0001 and 0.0003, respectively). No other periodic or rhythmic patterns exhibited AE subtype association. Forty-two percent had good outcome on discharge. Periodic or rhythmic patterns, seizures, and new-onset refractory status epilepticus conferred an increased risk of poor outcome (OR, 6.4; P = 0.0012; OR, 3; P = 0.0372; OR, 12.3; P = 0.02, respectively). CONCLUSION: Our study confirms a signature EEG pattern in anti-N-methyl-D-aspartate AE, termed extreme delta brush, identified as generalized rhythmic delta activity plus fast activity in our study. We found no other pattern association with other AE subtypes. We also found a high incidence of seizures among patients with AE. Finally, periodic or rhythmic patterns, seizures, and new-onset refractory status epilepticus conferred an increased risk of poor outcome regardless of AE subtype.

Deep active learning for Interictal Ictal Injury Continuum EEG patterns.

OBJECTIVES: Seizures and seizure-like electroencephalography (EEG) patterns, collectively referred to as "ictal interictal injury continuum" (IIIC) patterns, are commonly encountered in critically ill patients. Automated detection is important for patient care and to enable research. However, training accurate detectors requires a large labeled dataset. Active Learning (AL) may help select informative examples to label, but the optimal AL approach remains unclear. METHODS: We assembled >200,000 h of EEG from 1,454 hospitalized patients. From these, we collected 9,808 labeled and 120,000 unlabeled 10-second EEG segments. Labels included 6 IIIC patterns. In each AL iteration, a Dense-Net Convolutional Neural Network (CNN) learned vector representations for EEG segments using available labels, which were used to create a 2D embedding map. Nearest-neighbor label spreading within the embedding map was used to create additional pseudo-labeled data. A second Dense-Net was trained using real- and pseudo-labels. We evaluated several strategies for selecting candidate points for experts to label next. Finally, we compared two methods for class balancing within queries: standard balanced-based querying (SBBQ), and high confidence spread-based balanced querying (HCSBBQ). RESULTS: Our results show: 1) Label spreading increased convergence speed for AL. 2) All query criteria produced similar results to random sampling. 3) HCSBBQ query balancing performed best. Using label spreading and HCSBBQ query balancing, we were able to train models approaching expert-level performance across all pattern categories after obtaining ∼7000 expert labels. CONCLUSION: Our results provide guidance regarding the use of AL to efficiently label large EEG datasets in critically ill patients.

Rapid annotation of seizures and interictal-ictal-injury continuum EEG patterns.

BACKGROUND: Manual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous EEG (cEEG) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEEG. NEW METHOD: We learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEEG recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. EEG experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters. RESULTS: Mean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 ±â€¯4.44 min to label the 30.19 ±â€¯3.84 h of cEEG data, more than 45 times faster than unaided manual review, demonstrating efficiency. COMPARISON WITH EXISTING METHODS: Previous studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods. CONCLUSIONS: Our results suggest that long EEG recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method.

Cannabidiol in treatment of refractory epileptic spasms: An open-label study.

OBJECTIVE: This study aimed to evaluate clinical efficacy and safety of purified pharmaceutical cannabidiol (CBD) as an adjunctive therapy in refractory childhood-onset epileptic spasms (ES). METHODS: Nine patients with ES were enrolled in an Institutional Review Board (IRB)- and Food and Drug Administration (FDA)-approved expanded access investigational new drug trial. Patients received plant-derived highly purified CBD in oral solution in addition to their baseline medications at an initial dosage of 5 mg/kg/day, which was increased by 5 mg/kg/day every week to an initial target dosage of 25 mg/kg/day. Seizure frequency, adverse event, and parents' subjective reports of cognitive and behavioral changes were recorded after 2 weeks and 1, 2, 3, 6, 9, and 12 months of CBD treatment. Responder rates (percent of patients with >50% reduction in ES frequency from baseline) were calculated. Electrographic changes were studied in relation to CBD initiation and clinical response. RESULTS: Overall, the responder rates in 9 patients were 67%, 78%, 67%, 56%, 78%, 78%, and 78% after 2 weeks and 1, 2, 3, 6, 9, and 12 months of CBD treatment, respectively. Three out of nine patients (33%) were ES free after two months of treatment. Parents reported subjective improvements in cognitive and behavioral domains. Side effects, primarily drowsiness, were seen in 89% of patients (n = 8). Eight of the nine (89%) patients had electroencephalographic (EEG) studies prior to and after initiation of CBD. Three out of five patients (60%) had resolution in their hypsarrhythmia pattern. SIGNIFICANCE: Purified pharmaceutical CBD may be an effective and safe adjunctive therapy in refractory ES and may also be associated with improvements in electrographic findings.

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.

Language dysfunction-associated EEG findings in patients with CAR-T related neurotoxicity.

Chimeric antigen receptor-modified T cells (CAR-T) have emerged as a promising immunotherapeutic approach in relapsed/refractory haematolgical malignancies. Broader application is limited by unique toxicities, notably, neurotoxicity (NTX). Language dysfunction is among the most frequent symptoms of NTX, the underlying mechanisms of which remain to be elucidated. Electroencephalogram (EEG) is an important tool to monitor for NTX and may provide insights into language dysfunction. AIM: We aimed to characterise language dysfunction and define electroencephalographic signatures after CAR-T cell therapy. METHODS: We reviewed the clinical presentation and EEG findings of 20 adult patients presenting with language dysfunction after CAR-T cell infusion. The cohort included a subset of patients treated with investigational CD19-directed CAR-T cells for non-Hodgkin's lymphoma (n=17), acute lymphoblastic leukaemia (n=1), follicular lymphoma (n=1) and chronic lymphocytic leukaemia (n=1). RESULTS: Language dysfunction presented within 14 days of CAR-T cell infusion in 16 (84%) patients. Ten (50%) patients had mild word-finding difficulties and 10 (50%) had marked dysphasia with profound word-finding difficulties; the latter were all associated with generalised rhythmic delta activity or generalised periodic discharges on EEG. CONCLUSIONS: Language dysfunction after CAR-T cell therapy is associated with generalised EEG abnormalities.

Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation.

Importance: Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability. Objective: To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs. Design, Setting, and Participants: A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built. Main Outcomes and Measures: SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation. Results: SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). Conclusions and Relevance: In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.

Interrater Reliability of Experts in Identifying Interictal Epileptiform Discharges in Electroencephalograms.

Importance: The validity of using electroencephalograms (EEGs) to diagnose epilepsy requires reliable detection of interictal epileptiform discharges (IEDs). Prior interrater reliability (IRR) studies are limited by small samples and selection bias. Objective: To assess the reliability of experts in detecting IEDs in routine EEGs. Design, Setting, and Participants: This prospective analysis conducted in 2 phases included as participants physicians with at least 1 year of subspecialty training in clinical neurophysiology. In phase 1, 9 experts independently identified candidate IEDs in 991 EEGs (1 expert per EEG) reported in the medical record to contain at least 1 IED, yielding 87 636 candidate IEDs. In phase 2, the candidate IEDs were clustered into groups with distinct morphological features, yielding 12 602 clusters, and a representative candidate IED was selected from each cluster. We added 660 waveforms (11 random samples each from 60 randomly selected EEGs reported as being free of IEDs) as negative controls. Eight experts independently scored all 13 262 candidates as IEDs or non-IEDs. The 1051 EEGs in the study were recorded at the Massachusetts General Hospital between 2012 and 2016. Main Outcomes and Measures: Primary outcome measures were percentage of agreement (PA) and beyond-chance agreement (Gwet κ) for individual IEDs (IED-wise IRR) and for whether an EEG contained any IEDs (EEG-wise IRR). Secondary outcomes were the correlations between numbers of IEDs marked by experts across cases, calibration of expert scoring to group consensus, and receiver operating characteristic analysis of how well multivariate logistic regression models may account for differences in the IED scoring behavior between experts. Results: Among the 1051 EEGs assessed in the study, 540 (51.4%) were those of females and 511 (48.6%) were those of males. In phase 1, 9 experts each marked potential IEDs in a median of 65 (interquartile range [IQR], 28-332) EEGs. The total number of IED candidates marked was 87 636. Expert IRR for the 13 262 individually annotated IED candidates was fair, with the mean PA being 72.4% (95% CI, 67.0%-77.8%) and mean κ being 48.7% (95% CI, 37.3%-60.1%). The EEG-wise IRR was substantial, with the mean PA being 80.9% (95% CI, 76.2%-85.7%) and mean κ being 69.4% (95% CI, 60.3%-78.5%). A statistical model based on waveform morphological features, when provided with individualized thresholds, explained the median binary scores of all experts with a high degree of accuracy of 80% (range, 73%-88%). Conclusions and Relevance: This study's findings suggest that experts can identify whether EEGs contain IEDs with substantial reliability. Lower reliability regarding individual IEDs may be largely explained by various experts applying different thresholds to a common underlying statistical model.

Responsive neurostimulation targeting anterior thalamic nucleus in generalized epilepsy.

Responsive neurostimulation (RNS) has emerged as an adjunctive treatment modality for patients with intractable focal epilepsy who are not surgical candidates or have more than one ictal onset focus. We report a 34-year-old patient with intractable, childhood-onset, genetic generalized epilepsy (GGE) with tonic, atonic, myoclonic and absence seizures treated with RNS. Strip electrodes over the right posterior frontal cortex and depth electrodes placed in the right anterior nucleus were used for event detection and responsive stimulation. Two-year follow-up revealed 90-95% clinical seizure reduction. This case suggests that refractory GGE may be effectively treated with RNS targeting thalamocortical networks.

Clinical presentation, management, and biomarkers of neurotoxicity after adoptive immunotherapy with CAR T cells.

Chimeric antigen receptor (CAR) T cells have emerged as a promising class of cell-based immunotherapy in refractory malignancies. Neurotoxicity represents a common and potentially life-threatening adverse effect of CAR T cells, and clinical experience is limited. Here, we describe the clinical presentation and management of 25 adult patients who presented with neurotoxic syndromes after CAR T-cell therapy at the Massachusetts General Hospital. This cohort includes 24 patients treated with CD19-directed CAR T cells for non-Hodgkin lymphoma (n = 23) and acute lymphoblastic leukemia (n = 1), and 1 patient treated with α-fetoprotein-directed CAR T cells for hepatocellular carcinoma (n = 1). Twelve of the 25 patients (48%) developed grade 1-2 neurotoxicity and 13 patients (52%) presented with grade 3-4 neurotoxicity. We found that lower platelet counts at time of CAR T-cell infusion were associated with more severe neurotoxicity (P = .030). Cytokine release syndrome occurred in 24 of 25 patients (96%). Serum levels of ferritin peaked with onset of neurologic symptoms, and higher ferritin levels were associated with higher neurotoxicity grade. Grade 3-4 neurotoxicity correlated negatively with overall survival (OS) (P = .013). Median OS of the entire cohort was 54.7 weeks. Eight patients (32%) with grade 3-4 neurotoxicity were deceased at database closure, whereas none died with neurotoxicity grade 1-2. High pretreatment lactate dehydrogenase was frequently encountered in lymphoma patients with grade 3-4 neurotoxicity and correlated negatively with progression-free survival (P = .048). We did not find evidence that steroid use ≥7 days altered the patient's outcome when compared with <7 days of steroids. Management of CAR T cell-mediated neurotoxicity warrants evaluation in prospective clinical trials.

Bilateral independent periodic discharges are associated with electrographic seizures and poor outcome: A case-control study.

OBJECTIVE: To determine the clinical correlates bilateral independent periodic discharges (BIPDs) and their association with electrographic seizures and outcome. METHODS: Retrospective case-control study of patients with BIPDs compared to patients without periodic discharges ("No PDs") and patients with lateralized periodic discharges ("LPDs"), matched for age, etiology and level of alertness. RESULTS: We included 85 cases and 85 controls in each group. The most frequent etiologies of BIPDs were stroke, CNS infections, and anoxic brain injury. Acute bilateral cerebral injury was more common in the BIPDs group than in the No PDs and LPDs groups (70% vs. 37% vs. 35%). Electrographic seizures were more common with BIPDs than in the absence of PDs (45% vs. 8%), but not than with LPDs (52%). Mortality was higher in the BIPDs group (36%) than in the No PDs group (18%), with fewer patients with BIPDs achieving good outcome (moderate disability or better; 18% vs. 36%), but not than in the LPDs group (24% mortality, 26% good outcome). In multivariate analyses, BIPDs remained associated with mortality (OR: 3.0 [1.4-6.4]) and poor outcome (OR: 2.9 [1.4-6.2]). CONCLUSION: BIPDs are caused by bilateral acute brain injury and are associated with a high risk of electrographic seizures and of poor outcome. SIGNIFICANCE: BIPDs are uncommon but their identification in critically ill patients has potential important implications, both in terms of clinical management and prognostication.

Neuroimaging Correlates of Periodic Discharges.

Despite being first described over 50 years ago, periodic discharges continue to generate controversy as to whether they are always, sometimes, or never "ictal." Investigators and clinicians have proposed adjunctive markers to help clarify this distinction-in particular measures of perfusion and metabolism. Here, we review the growing number of neuroimaging studies using Fluorodeoxyglucose-PET, MRI diffusion, Magnetic resonance perfusion, Single Photon Emission Computed Tomography, and Magnetoencepgalography to gain further insight into the physiology and clinical significance of periodic discharges. To date, however, no definitive consensus exists regarding the features of periodic discharges that warrant treatment intensification. However, an emerging consilience among neuroimaging modalities suggests that periodic discharges can induce a hyperexcitatory state with associated hypermetabolism and hyperperfusion, which may result in local metabolic failure.

Extreme delta brush evolving into status epilepticus in a patient with anti-NMDA encephalitis.

Extreme delta brush (EDB) is an EEG pattern unique to anti-NMDA encephalitis. It is correlated with seizures and status epilepticus in patients who have a prolonged course of illness. The etiology of the underlying association between EDB and seizures is not understood. We present a patient with anti-NMDA encephalitis who developed status epilepticus evolving from the high frequency activity of the extreme delta brush. This case demonstrates that EDB is not only a marker for a greater propensity for seizures but also directly implicated in seizure generation.