BACKGROUND:Electroencephalogram (EEG) interpretation is essential for neurological diagnosis, but expert interpretation is limited globally, and existing AI methods address narrow tasks. This study aimed to develop and externally validate a broadly applicable foundation model capable of expert-level performance across diverse EEG tasks and clinical settings. METHODS:In this multicentre study we developed a multidomain omnibus for reading and generalising over thorough EEG interpretation (MORGOTH), a foundation model that supports broad clinical interpretation across all major settings. We developed MORGOTH using EEGs from 18 677 patients across Massachusetts General Hospital, Brigham and Women's Hospital, Beth Israel Deaconess Medical Center, and Boston Children's Hospital, collected between Jan 1, 2003, and Feb 1, 2025, and validated it internally on 13 334 patients and externally on 1573 patients from 48 institutions, spanning diverse clinical settings and ages (0 years to >90 years). Test datasets annotated by six to 30 experts enabled inter-rater reliability (IRR) analysis by comparing model-expert and expert-expert agreement. MORGOTH was compared against both human experts and state-of-the-art models using area under the curve (AUC) and the percentage of experts' operating points under the curve (EUC) for receiver operating characteristic (ROC) and precision-recall curves, as well as IRR and statistical calibration. FINDINGS:MORGOTH achieved expert-level performance with AUC-ROC scores of 0·86 to 0·98 across 17 EEG findings. MORGOTH outperformed at least 90% of experts on three of seven multi-expert-annotated datasets and exceeded at least 20% of experts on each of the 17 tasks. Event-level performance was especially strong for seizure and ictal-interictal-injury continuum detection (EUC=96·6%) and spike detection (EUC=100%). IRR analysis showed that MORGOTH matched or exceeded expert consensus. External validation confirmed consistent performance with modest declines from internal to external test sets (event-level AUC -1·21%, EUC -3·33%; EEG-level AUC -2·12%, and EUC -9·52%). MORGOTH performance also remained robust across age, sex, and moderate channel loss, with lower age sensitivity (20·90% vs 30·90%) and fewer sex-related differences (33·33% vs 44·00%) than SPaRCNet. INTERPRETATION:MORGOTH advances automated EEG interpretation with expert-level performance across clinical settings, offering improved diagnostic accuracy in low-resource environments and greater efficiency in high-volume centres. FUNDING:US National Institutes of Health.

A 27-year-old man was evaluated because of ongoing convulsive episodes that occurred despite the use of anticonvulsant medications. MRI of the head was normal. A diagnostic test was performed.

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.

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.

Routine outpatient epilepsy care has shifted from in-person to telemedicine visits in response to safety concerns posed by the coronavirus disease 2019 (COVID-19) pandemic. But whether telemedicine can support and maintain standardized documentation of high-quality epilepsy care remains unknown. In response, the authors conducted a quality improvement study at a level 4 epilepsy center between January 20, 2019, and May 31, 2020. Weekly average completion proportion of standardized documentation used by a team of neurologists for adult patients for the diagnosis of epilepsy, seizure classification, and frequency were analyzed. By December 15, 2019, a 94% average weekly completion proportion of standardized epilepsy care documentation was achieved that was maintained through May 31, 2020. Moreover, during the period of predominately telemedicine encounters in response to the pandemic, the completion proportion was 90%. This study indicates that high completion of standardized documentation of seizure-related information can be sustained during telemedicine appointments for routine outpatient epilepsy care at a level 4 epilepsy center.

Objective To examine the relationship between scalp EEG biomarkers of hyperexcitability in Alzheimer disease (AD) and to determine how these electric biomarkers relate to the clinical expression of seizures in AD. Methods In this cross-sectional study, we performed 24-hour ambulatory scalp EEGs on 43 cognitively normal elderly healthy controls (HC), 41 participants with early-stage AD with no history or risk factors for epilepsy (AD-NoEp), and 15 participants with early-stage AD with late-onset epilepsy related to AD (AD-Ep). Two epileptologists blinded to diagnosis visually reviewed all EEGs and annotated all potential epileptiform abnormalities. A panel of 9 epileptologists blinded to diagnosis was then surveyed to generate a consensus interpretation of epileptiform abnormalities in each EEG. Results Epileptiform abnormalities were seen in 53% of AD-Ep, 22% of AD-NoEp, and 4.7% of HC. Specific features of epileptiform discharges, including high frequency, robust morphology, right temporal location, and occurrence during wakefulness and REM, were associated with clinical seizures in AD. Multiple EEG biomarkers concordantly demonstrated a pattern of left temporal lobe hyperexcitability in early stages of AD, whereas clinical seizures in AD were often associated with bitemporal hyperexcitability. Frequent small sharp spikes were specifically associated with epileptiform EEGs and thus identified as a potential biomarker of hyperexcitability in AD. Conclusion Epileptiform abnormalities are common in AD but not all equivalent. Specific features of epileptiform discharges are associated with clinical seizures in AD. Given the difficulty recognizing clinical seizures in AD, these EEG features could provide guidance on which patients with AD are at high risk for clinical seizures.

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.

Objective: To assess the applicability of a smartphone-based electroencephalography (EEG), the Smartphone Brain Scanner-2 in a low-income country, including quality of results and usefulness of repeat testing. Background: People with epilepsy in Sub-Saharan Africa are often undiagnosed. We examine a low-cost, portable smartphone-based EEG technology in a heterogeneous epilepsy cohort in the West African Republic of Guinea. Design/Methods: The SBS2 system consists of an Android tablet wirelessly connected to a 14-electrode EasyCap headset. SBS2 was performed in people with suspected epilepsy in Guinea (2018–19), with a repeat EEG carried out at a variable time interval. Recordings were interpreted by U.S., Canadian, and U.K. experts in Clinical Neurophysiology. Results: We included 149 participants (41% female, median age 17.9 years). 66.6% ≤ 21 years; mean number of seizures per month 5.7 +/−15.5. The mean duration of EEG1 was 53 minutes +/−12.3 and EEG2 was 29.6 minutes +/−12.8. The mean quality score of EEG1 and EEG2 independently was 6.4 (range 1(low)-10(high), median 7.0). 29.5% of participants had epileptiform discharges (EDs) at EEG1 and 16.7% at EEG2. 41.6% had abnormal slowing and/or EDs at EEG1 and 28.8% at EEG2. 26.1% were recommended for neuro-imaging after EEG1 and 14.7% after EEG2. Of those without EDs at EEG1 (n=53, 55.8%), 7 (13.2%) had EDs at EEG2. Of those with detectable EDs on EEG1 (n = 23, 24.2%), 12 (52.1%) did not have EDs at EEG2. Patients for whom at least one EEG was not scored for EDs were excluded from the comparative ED analysis (n = 54, 36%). Conclusions: The SBS2 has a reproducible level of quality on repeat testing and is useful for the detection of EDs. One EEG of approximately 50 minutes was adequate to support diagnosis. The need for neuroimaging access in this patient population is evident. Disclosure: Dr. Ayub has nothing to disclose. Dr. Leung has nothing to disclose. Dr. Fantaneanu has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with UCB, Eisai, and Sunovion. Dr. Patel has nothing to disclose. Dr. Vyas has nothing to disclose. Dr. Milligan has nothing to disclose. Dr. Villamar has nothing to disclose. Dr. Hoch has nothing to disclose. Dr. Purves has nothing to disclose. Dr. Esmaeili has nothing to disclose. Dr. Tellez-Zenteno has nothing to disclose. Dr. Gonzalez-Giraldo has nothing to disclose. Dr. Tolokh has nothing to disclose. Dr. Heidarian has nothing to disclose. Dr. Worden has nothing to disclose. Dr. Jadeja has nothing to disclose. Dr. Fridinger has nothing to disclose. Dr. Lee has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Receive clinical trial funds from Novartis Canada, Roche Canada Serve on ad boards for Celgene, Novartis Canada, Roche Canada, Teva Neuroscience, Serono Canada, Genzyme Canada, Biogen Canada. Dr. Lee has received research support from Receive clinical trial funds from Novartis Canada, Roche Canada Serve on ad boards for Celgene, Novartis Canada, Roche Canada, Teva Neuroscience, Serono Canada, Genzyme Canada, Biogen Canada. Dr. Cisse has nothing to disclose. Dr. Mateen has received research support from IQVIA.

OBJECTIVE:To evaluate the accuracy of ICD-10-CM claims-based definitions for epilepsy and classifying seizure types in the outpatient setting. METHODS:We reviewed electronic health records (EHR) for a cohort of adults aged 18+ years seen by six neurologists who had an outpatient visit at a level 4 epilepsy center between 01/2019-09/2019. The neurologists used a standardized documentation template to capture the diagnosis of epilepsy (yes/no/unsure), seizure type (focal/generalized/unknown), and seizure frequency in the EHR. Using linked ICD-10-CM codes assigned by the provider, we assessed the accuracy of claims-based definitions for epilepsy, focal seizure type, and generalized seizure type against the reference-standard EHR documentation by estimating sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV). RESULTS:There were 673 eligible outpatient encounters. After review of EHRs for standardized documentation, an analytic sample consisted of 520 encounters representing 402 unique patients. In the EHR documentation, 93.5 % (n = 486/520) of encounters were with patients with a diagnosis of epilepsy. Of those, 66.0 % (n = 321/486) had ≥1 focal seizure, 41.6 % (n = 202/486) had ≥1 generalized seizure, and 7% (n = 34/486) had ≥1 unknown seizure. An ICD-10-CM definition for epilepsy (i.e., ICD-10 G40.X) achieved Sn = 84.4 % (95 % CI 80.8-87.5%), Sp = 79.4 % (95 % CI 62.1-91.3%), PPV = 98.3 % (95 % CI 96.6-99.3%), and NPV = 26.2 % (95 % CI 18.0-35.8%). The classification of focal vs generalized/unknown seizures achieved Sn = 69.8 % (95 % CI 64.4-74.8%), Sp = 79.4 % (95 % CI 72.4-85.3%), PPV = 86.8 % (95 % CI 82.1-90.7%), and NPV = 57.5 % (95 % CI 50.8-64.0%). CONCLUSIONS:Claims-based definitions using groups of ICD-10-CM codes assigned by neurologists in routine outpatient clinic visits at a level 4 epilepsy center performed well in discriminating between patients with and without a diagnosis of epilepsy and between seizure types.

Objective To incorporate standardized documentation into an epilepsy clinic and to use these standardized data to compare patients' perception of epilepsy diagnosis to provider documentation. Methods Using quality improvement methodology, we implemented interventions to increase documentation of epilepsy diagnosis, seizure frequency, and type from 49.8% to 70% of adult nonemployee patients seen by 6 providers over 5 months of routine clinical care. The main intervention consisted of an interactive SmartPhrase that mirrored a documentation template developed by the Epilepsy Learning Healthcare System. We assessed the weekly proportion of complete SmartPhrases among eligible patient encounters with a statistical process control chart. We used a subset of patients with established epilepsy care linked to existing patient-reported survey data to examine the proportion of patient-to-provider agreement on epilepsy diagnosis (yes vs no/unsure). We also examined sociodemographic and clinical characteristics of patients who disagreed vs agreed with provider's documentation of epilepsy diagnosis. Results The median SmartPhrase weekly completion rate was 78%. Established patients disagreed with providers with respect to epilepsy diagnosis in 18.5% of encounters (κ = 0.13), indicating that they did not have or were unsure if they had epilepsy despite having a provider-documented epilepsy diagnosis. Patients who disagreed with providers were similar to those who agreed with respect to age, sex, ethnicity, marital status, seizure frequency, type, and other quality-of-life measures. Conclusion This project supports the feasibility of implementing standardized documentation of data relevant to epilepsy care in a tertiary epilepsy clinic and highlights an opportunity for improvement in patient-provider communication.

Purpose: : Children with epilepsy in low-income countries often go undiagnosed and untreated. We examine a portable, low-cost smartphone-based EEG technology in a heterogeneous pediatric epilepsy cohort in the West African Republic of Guinea. Methods: : Children with epilepsy were recruited at the Ignace Deen Hospital in Conakry, 2017. Participants underwent sequential EEG recordings with an app-based EEG, the Smartphone Brain Scanner-2 (SBS2) and a standard Xltek EEG. Raw EEG data were transmitted via Bluetooth (TM) connection to an Android (TM) tablet and uploaded for remote EEG specialist review and reporting via a new, secure web-based reading platform, crowdEEG. The results were compared to same-visit Xltek 10-20 EEG recordings for identification of epileptiform and non-epileptiform abnormalities. Results: : 97 children meeting the International League Against Epilepsy's definition of epilepsy (49 male; mean age 10.3 years, 29 untreated with an antiepileptic drug; 0 with a prior EEG) were enrolled. Epileptiform discharges were detected on 21 (25.3%) SBS2 and 31 (37.3%) standard EEG recordings. The SBS2 had a sensitivity of 51.6% (95%CI 32.4%, 70.8%) and a specificity of 90.4% (95%CI 81.4%, 94.4%) for all types of epileptiform discharges, with positive and negative predictive values of 76.2% and 75.8% respectively. For generalized discharges, the SBS2 had a sensitivity of 43.5% with a specificity of 96.2%. Conclusions: : The SBS2 has a moderate sensitivity and high specificity for the detection of epileptiform abnormalities in children with epilepsy in this low-income setting. Use of the SBS2 + crowdEEG platform permits specialist input for patients with previously poor access to clinical neurophysiology expertise.

ObjectiveTo see if systematic collection of patient-reported epilepsy quality measures can identify opportunities to improve care, and to examine the associations between these measures and physical and mental health.MethodsWe developed a patient-reported questionnaire for medication adherence, seizure frequency, medication side effects, and driving that included the Patient-Reported Outcome Measurement Information System–10 (PROMIS-10) (physical and mental health). We offered it to all adult patients seen twice in an epilepsy clinic (January 2017–January 2018). The questionnaire was available on the web as well as a tablet provided at appointment check-in. We used the first completed questionnaire to explore the relationship between patient-reported care quality and measures of physical and mental health.ResultsA total of 610 unique patients (15% of the total encounters) completed a survey. Respondents were comparable to nonrespondents. Respondents reported gaps in care or opportunities for quality improvement in 48.4% (n = 295) of the encounters. Of patients who reported at least 1 seizure per month over the previous 3 months, 55.2% (n = 100) reported problems with adherence, 30.0% (n = 131) reported having problems believed to be adverse reactions to anticonvulsants, and 15.2% (n = 41) reported driving. In addition, respondents who reported either seizures over the recent 3 months, nonadherence to treatment due to cost, or anticonvulsant-associated adverse effects had consistently worse physical and mental health (all p < 0.05).ConclusionsSystematic collection of epilepsy quality measures endorsed by the American Academy of Neurology can identify opportunities for quality improvement. Measures of epilepsy care quality predict outcomes that matter to patients.

OBJECTIVE Foramen ovale electrodes (FOEs) are a minimally invasive method to localize mesial temporal seizures in cases in which noninvasive methods are inconclusive. The objective of this study was to identify factors predicting the ability of FOEs to yield a diagnosis in order to determine optimal candidates for this procedure. METHODS All cases of diagnostic investigations performed with FOEs at the authors’ institution between 2005 and 2017 were reviewed. FOE investigation was defined as diagnostic if it led to a treatment decision. Demographic and clinical variables for diagnostic and nondiagnostic investigations were compared using a Wilcoxon rank-sum test for continuous variables and Fisher’s exact test for categorical variables. RESULTS Ninety-three patients underwent investigations performed with FOEs during the study period and were included in the study. FOE investigation was diagnostic in 75.3% of cases. Of patients who underwent anterior temporal lobectomy following diagnostic FOE evaluation, 75.9% were Engel class I at last follow-up (average 40.1 months). When the diagnostic and nondiagnostic FOE groups were compared, patients who had diagnostic investigations were more likely to be male (57.1% male vs 26.1% in the nondiagnostic group, p = 0.015). They were also more likely to have temporal lesions on preoperative MRI (p = 0.018). CONCLUSIONS FOEs are a useful, minimally invasive diagnostic modality resulting in a treatment decision in 75% of cases. Male patients and patients with temporal lesions on MRI may be most likely to benefit from FOE investigation.

To compare the ability of a smartphone-based app, the Smartphone Brain Scanner-2(SBS2), and pre-placed EEG cap (Easycap), costing ~300USD, to detect abnormalities with that of standard EEG.

Objective To compare the expected quality-adjusted life-years (QALYs) in adult patients undergoing immediate vs deferred antiepileptic drug (AED) treatment after a first unprovoked seizure. Methods We constructed a simulated clinical trial (Markov decision model) to compare immediate vs deferred AED treatment after a first unprovoked seizure in adults. Three base cases were considered, representing patients with varying degrees of seizure recurrence risk and effect of seizures on quality of life (QOL). Cohort simulation was performed to determine which treatment strategy would maximize the patient's expected QALYs. Sensitivity analyses were guided by clinical data to define decision thresholds across plausible measurement ranges, including seizure recurrence rate, effect of seizure recurrence on QOL, and efficacy of AEDs. Results For patients with a moderate risk of recurrent seizures (52.0% over 10 years after first seizure), immediate AED treatment maximized QALYs compared to deferred treatment. Sensitivity analyses showed that for the preferred choice to change to deferred AED treatment, key clinical measures needed to reach implausible values were 10-year seizure recurrence rate ≤38.0%, QOL reduction with recurrent seizures ≤0.06, and efficacy of AEDs on lowering seizure recurrence rate ≤16.3%. Conclusion Our model determined that immediate AED treatment is preferable to deferred treatment in adult first-seizure patients over a wide and clinically relevant range of variables. Furthermore, our analysis suggests that the 10-year seizure recurrence rate that justifies AED treatment (38.0%) is substantially lower than the 60% threshold used in the current definition of epilepsy.

OBJECTIVE:Excitotoxic injury involving N-methyl-d-aspartate (NMDA) receptor hyperactivity contributes to epilepsy-related memory dysfunction (ERMD). Current treatment strategies for ERMD have limited efficacy and fail to target the underlying pathophysiology. The present pilot study evaluated the efficacy of memantine, an NMDA receptor antagonist, for the treatment of ERMD in adults with focal-onset seizures. METHODS:Subjects underwent cognitive testing at baseline, after a 13-week randomized, parallel-group, double-blinded phase (of memantine titrated to 10 mg bid or placebo), and following a 13-week open-label extension phase (of memantine titrated to 10 mg bid). The selective reminding test (SRT) continuous long-term retrieval (CLTR) score and 7/24 Spatial Recall Test learning score served as the primary outcome measures. Secondary measures included tests of attention span, fluency, visual construction, and response inhibition, as well as assessments of quality of life, depression, sleepiness, and side effects. RESULTS:Seventeen subjects contributed data to the blinded phase (n = 8 memantine, n = 9 placebo). No significant differences were seen between groups on the primary or secondary outcome measures. Pooled data at the end of the open-label phase from 10 subjects (initially randomized to memantine n = 3 or placebo n = 7) demonstrated statistically significant improvement from baseline in CLTR score, memory-related quality of life, spatial span, and response inhibition. No significant changes were evident in depression, sleepiness, side effects, or seizure frequency throughout the trial. SIGNIFICANCE:Results demonstrated no significant effect of memantine on cognition when assessed at the end of the blinded period. Pooled data at the end of the open-label phase showed significant improvement over baseline performance in measures of verbal memory, frontal-executive function, and memory-related quality of life. These improvements, however, may be due to practice effects and should be interpreted cautiously. Findings suggest a favorable safety profile of memantine in the setting of epilepsy.

ObjectiveDelayed cerebral ischemia (DCI) is a common, disabling complication of subarachnoid hemorrhage (SAH). Preventing DCI is a key focus of neurocritical care, but interventions carry risk and cannot be applied indiscriminately. Although retrospective studies have identified continuous electroencephalographic (cEEG) measures associated with DCI, no study has characterized the accuracy of cEEG with sufficient rigor to justify using it to triage patients to interventions or clinical trials. We therefore prospectively assessed the accuracy of cEEG for predicting DCI, following the Standards for Reporting Diagnostic Accuracy Studies.MethodsWe prospectively performed cEEG in nontraumatic, high‐grade SAH patients at a single institution. The index test consisted of clinical neurophysiologists prospectively reporting prespecified EEG alarms: (1) decreasing relative alpha variability, (2) decreasing alpha‐delta ratio, (3) worsening focal slowing, or (4) late appearing epileptiform abnormalities. The diagnostic reference standard was DCI determined by blinded, adjudicated review. Primary outcome measures were sensitivity and specificity of cEEG for subsequent DCI, determined by multistate survival analysis, adjusted for baseline risk.ResultsOne hundred three of 227 consecutive patients were eligible and underwent cEEG monitoring (7.7‐day mean duration). EEG alarms occurred in 96.2% of patients with and 19.6% without subsequent DCI (1.9‐day median latency, interquartile range = 0.9–4.1). Among alarm subtypes, late onset epileptiform abnormalities had the highest predictive value. Prespecified EEG findings predicted DCI among patients with low (91% sensitivity, 83% specificity) and high (95% sensitivity, 77% specificity) baseline risk.InterpretationcEEG accurately predicts DCI following SAH and may help target therapies to patients at highest risk of secondary brain injury. Ann Neurol 2018;83:958–969

Background: Pressure ulcers resulting from continuous EEG (cEEG) monitoring in hospitalized patients have gained attention as a preventable medical complication. We measured their incidence and risk factors.Methods: We performed an observational investigation of cEEG-electrode-related pressure ulcers (EERPU) among acutely ill patients over a 22-month period. Variables analyzed included age, sex, monitoring duration, hospital location, application methods, vasopressor usage, nutritional status, skin allergies, fever, and presence/severity of EERPU. We examined risk for pressure ulcers vs monitoring duration using Kaplan-Meyer survival analysis, and performed multivariate risk assessment using Cox proportional hazard model.Results: Among 1,519 patients, EERPU occurred in 118 (7.8%). Most (n = 109, 92.3%) consisted of hyperemia only without skin breakdown. A major predictor was monitoring duration, with 3-, 5-, and 10-day risks of 16%, 32%, and 60%, respectively. Risk factors included older age (mean age 60.65 vs 50.3, p < 0.01), care in an intensive care unit (9.37% vs 5.32%, p < 0.01), lack of a head wrap (8.31% vs 27.3%, p = 0.02), use of vasopressors (16.7% vs 9.64%, p < 0.01), enteral feeding (11.7% vs 5.45%, p 5 0.04), and fever (18.4% vs 9.3%, p < 0.01). Elderly patients (71-80 years) were at higher risk (hazard ratio 6.84 [1.95-24], p, 0.01), even after accounting for monitoring time and other pertinent variables in multivariate analysis.Conclusions: EERPU are uncommon and generally mild. Elderly patients and those with more severe illness have higher risk of developing EERPU, and the risk increases as a function of monitoring duration.