Initiation of Antiseizure Medications by US Board-Certified Neurologists After a First Unprovoked Seizure Based on EEG Findings.

Background and Objectives: To investigate neurologists' practice variability in antiseizure medication (ASM) initiation after a first unprovoked seizure based on reported EEG interpretations. Methods: We developed a 15-question multiple-choice survey incorporating a standardized clinical case scenario of a patient with a first unprovoked seizure for whom different EEG reports were provided. The survey was distributed among board-certified neurologists practicing in the United States. Associations between categorical variables were evaluated using the Fisher Exact test. Multivariate analysis was performed using logistic regression. Results: A total of 106 neurologists responded to the survey. Most responders (75%-95%) would start ASM for definite epileptiform features on EEG, with similar rates between subgroups differing in years of practice, presence of subspecialty EEG training, and self-reported confidence in EEG interpretation. There was greater variability in practice for nonspecific EEG abnormalities, with sharply contoured activity, sharp transients, and focal delta slowing associated with the highest variability and uncertainty. Neurologists with >5 years of practice experience (21% vs 44%, OR 0.35 [95% CI 0.13-0.89], p = 0.021), subspecialty EEG training (15% vs 50%, OR = 0.17 [95% CI 0.06-0.48], p < 0.001), and greater confidence in EEG interpretation (21% vs 52%, OR 0.24 [95% CI 0.09-0.62], p = 0.001) were less likely to start ASM for ≥2 nonspecific EEG abnormalities and reported greater uncertainty. In multivariate analysis, seniority (p = 0.039) and subspecialty EEG training (p = 0.032) were associated with decreased ASM initiation for nonspecific EEG features. Discussion: There was substantial variability in ASM initiation practices between board-certified neurologists after a first unprovoked seizure with nonspecific EEG abnormalities. These findings clarify specific areas where EEG reporting may be optimized and reinforces the importance of implementing evidence-based practice guidelines.

Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study.

BACKGROUND: The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event. METHODS: This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database. The study population comprised patients with an initial diagnosis of either epilepsy or convulsions before the age of 21 years, based on International Classification of Diseases, Clinical Modification (ICD-CM) codes. We compared machine learning-based predictive modelling using structured data (logistic regression and XGBoost) with emerging techniques in natural language processing by use of large language models. FINDINGS: The primary cohort comprised 14 021 patients at Boston Children's Hospital matching inclusion criteria with an initial seizure-like event and the comparison cohort comprised 15 062 patients within the IBM MarketScan research database. Seizure recurrence based on a composite expert-derived definition occurred in 57% of patients at Boston Children's Hospital and 63% of patients within IBM MarketScan. Large language models with additional domain-specific and location-specific pre-training on patients excluded from the study (F1-score 0·826 [95% CI 0·817-0·835], AUC 0·897 [95% CI 0·875-0·913]) performed best. All large language models, including the base model without additional pre-training (F1-score 0·739 [95% CI 0·738-0·741], AUROC 0·846 [95% CI 0·826-0·861]) outperformed models trained with structured data. With structured data only, XGBoost outperformed logistic regression and XGBoost models trained with the Boston Children's Hospital EMR (logistic regression: F1-score 0·650 [95% CI 0·643-0·657], AUC 0·694 [95% CI 0·685-0·705], XGBoost: F1-score 0·679 [0·676-0·683], AUC 0·725 [0·717-0·734]) performed similarly to models trained on the IBM MarketScan database (logistic regression: F1-score 0·596 [0·590-0·601], AUC 0·670 [0·664-0·675], XGBoost: F1-score 0·678 [0·668-0·687], AUC 0·710 [0·703-0·714]). INTERPRETATION: Physician's clinical notes about an initial seizure-like event include substantial signals for prediction of seizure recurrence, and additional domain-specific and location-specific pre-training can significantly improve the performance of clinical large language models, even for specialised cohorts. FUNDING: UCB, National Institute of Neurological Disorders and Stroke (US National Institutes of Health).

Automated Seizure Detection in Patients with Cardiac Arrest: A Retrospective Review of Ceribell™ Rapid-EEG Recordings.

BACKGROUND: In patients with cardiac arrest who remain comatose after return of spontaneous circulation, seizures and other abnormalities on electroencephalogram (EEG) are common. Thus, guidelines recommend urgent initiation of EEG for the evaluation of seizures in this population. Point-of-care EEG systems, such as Ceribell™ Rapid Response EEG (Rapid-EEG), allow for prompt initiation of EEG monitoring, albeit through a reduced-channel montage. Rapid-EEG incorporates an automated seizure detection software (Clarity™) to measure seizure burden in real time and alert clinicians at the bedside when a high seizure burden, consistent with possible status epilepticus, is identified. External validation of Clarity is still needed. Our goal was to evaluate the real-world performance of Clarity for the detection of seizures and status epilepticus in a sample of patients with cardiac arrest. METHODS: This study was a retrospective review of Rapid-EEG recordings from all the patients who were admitted to the medical intensive care unit at Kent Hospital (Warwick, RI) between 6/1/2021 and 3/18/2022 for management after cardiac arrest and who underwent Rapid-EEG monitoring as part of their routine clinical care (n = 21). Board-certified epileptologists identified events that met criteria for seizures or status epilepticus, as per the 2021 American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology, and evaluated any seizure burden detections generated by Clarity. RESULTS: In this study, 4 of 21 patients with cardiac arrest (19.0%) who underwent Rapid-EEG monitoring had multiple electrographic seizures, and 2 of those patients (9.5%) had electrographic status epilepticus within the first 24 h of the study. None of these ictal abnormalities were detected by the Clarity seizure detection system. Clarity showed 0% seizure burden throughout the entirety of all four Rapid-EEG recordings, including the EEG pages that showed definite seizures or status epilepticus. CONCLUSIONS: The presence of frequent electrographic seizures and/or status epilepticus can go undetected by Clarity. Timely and careful review of all raw Rapid-EEG recordings by a qualified human EEG reader is necessary to guide clinical care, regardless of Clarity seizure burden measurements.

Severity of Epilepsy and Response to Antiseizure Medications in Individuals With Multiple Sclerosis: Analysis of a Real-World Dataset.

Background and Objectives: Epilepsy is an important comorbidity that affects outcomes for people with multiple sclerosis (MS). However, it is unclear whether seizure severity among individuals with coexistence of MS and epilepsy (MS + E) is higher than in those with other focal epilepsies. Our goal was to compare the overall severity of epilepsy in individuals with MS + E vs those with focal epilepsy without MS (E - MS), as defined by seizure-related health care utilization, frequency and duration of status epilepticus, and frequency of antiseizure medication (ASM) regimen changes. Methods: In this hypothesis-generating study, we analyzed a US commercial nationwide deidentified claims data set with >86 million individuals between January 1, 2008, and August 31, 2019. Using validated algorithms, we identified adults with E - MS and those with MS + E. We compared the number and length of seizure-related hospital admissions, the number of claims and unique days with claims for status epilepticus, and the rates of ASM regimen changes between the MS + E and E - MS groups. Results: During the study period, 66,708 individuals with E - MS and 537 with MS + E had ≥2 years of coverage after their initial diagnosis of epilepsy. There was no difference between the MS + E and E - MS groups in the percentage of individuals admitted for seizures and/or status epilepticus. However, MS + E with seizure-related admissions had more admissions and longer hospital stays than those with E - MS. MS + E who experienced status epilepticus had more unique days with status epilepticus claims compared with E - MS. MS + E were more likely to have ASM regimen changes in 2 years after the initial diagnosis of epilepsy and had more ASM changes during 2 years compared with E - MS. Among individuals with MS + E, there were no differences in our measures of seizure severity for those treated with sodium channel blockers/modulators vs other ASM classes. Discussion: This study supports the notion that individuals with MS + E can have more severe epilepsy than those with E - MS. Seizure severity among individuals with MS + E treated with sodium channel blockers/modulators vs other ASM classes shows no significant differences. Classification of Evidence: This study provides Class III evidence that individuals with MS + E can have more severe epilepsy than those with E - MS.

Aggressive MOGAD with bilateral corticospinal tract lesions following infection with Jamestown Canyon virus.

We present a case of a rare viral encephalitis due to Jamestown Canyon virus precipitating a severe phenotype of myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) with novel neuroimaging findings.

Symptomatic leptomeningeal carcinomatosis: a rare presentation of chronic lymphocytic leukaemia relapse.

Central nervous system (CNS) involvement in patients with chronic lymphocytic leukaemia (CLL) is very rare and, when present, it is frequently asymptomatic. Rather, CNS involvement is more common in other haematological malignancies such as mantle cell lymphoma or diffuse large B cell lymphoma. The paucity of literature on CNS involvement in CLL underscores the importance of increasing awareness about its presentation, diagnosis and optimal management. We describe a case of symptomatic leptomeningeal leukaemic involvement as an atypical presentation of CLL relapse. A favourable clinical response was observed following systemic monotherapy with venetoclax.

Clinical Reasoning: A Young Man With Daily Episodes of Altered Awareness.

Stretch syncope is a distinct entity characterized by transient alteration in awareness (TAA) induced by neck hyperextension during stretching. Few cases of stretch syncope have been reported in the literature. Nevertheless, this is a highly relevant diagnosis as it can be easily mistaken for epilepsy for a number of reasons. These include stereotypical motor activity associated with the events, development of ictal tachycardia, and the presence of rhythmic/semirhythmic slowing on EEG in the context of transient cerebral hypoperfusion.We present the case of a young man who was referred to our comprehensive epilepsy center for frequent episodes of TAA. After careful evaluation, the episodes were initially considered to be epileptic. Given that he had negligible clinical response to antiseizure medications, he underwent an experimental protocol at a cardiovascular research laboratory that ultimately confirmed the diagnosis of stretch syncope. The present article describes an approach to the evaluation of TAA and illustrates a typical case of stretch syncope. The importance of considering stretch syncope in the differential diagnosis of TAA is exemplified. Finally, our analyses help elucidate the pathophysiology of this rare entity.