Feyissa, A M

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Characteristics and Prognosis of Tumor-Related Epilepsy During Tumor Evolution in Patients With IDH Wild-Type Glioblastoma Pallud J, Roux A, Moiraghi A, Aboubakr O, Elia A, Guinard E, Oppenheim C, Tauziede-Espariat A, Parraga E, Gavaret M, Chrètien F, Huberfeld G, Zanello M. Neurology . 2024;102(1): e207902. doi: 10.1212/WNL.0000000000207902 Background and Objectives: Tumor-related epilepsy is a well-known symptom of glioblastoma. However, the particular characteristics of epileptic seizures related to glioblastoma, isocitrate dehydrogenase (IDH)-wild-type is almost unexplored longitudinally during the whole course of the disease. We assessed tumor-related epilepsy and seizure control during tumor evolution and the prognostic significance of tumor-related epilepsy. Methods: We performed an observational, retrospective single-center study at one tertiary referral neuro-oncology surgical center (2000-2020). We included adult patients treated for a newly diagnosed supratentorial glioblastoma, IDH-wild-type with available preoperative and postoperative MRI and with available epileptic seizure status at diagnosis. To determine factors associated with tumor-related epilepsy or seizure control, univariate analyses were performed using the χ 2 or Fisher exact tests for categorical variables and the unpaired t test or Mann-Whitney rank-sum test for continuous variables. Predictors associated with tumor-related epilepsy and seizure control in unadjusted analysis were entered into backward stepwise logistic regression models. Results: One thousand six patients were enrolled. The cumulative incidence of tumor-related epilepsy increased during tumor evolution (33.1% at diagnosis, 44.7% after oncologic treatment, 52.4% at progression, and 51.8% at the end-of-life phase) and is related to tumor features (cortex involvement, no necrosis, and small volume). Uncontrolled epileptic seizures increased during tumor evolution (20.1% at diagnosis, 32.0% after oncologic treatment, 46.7% at progression, and 41.1% at the end-of-life phase). Epileptic seizure control after oncologic treatment was related to seizure features (uncontrolled before oncologic treatment and focal-to-bilateral tonic-clonic seizures) and to the extent of resection. Epileptic seizure control at tumor progression was related to seizure features (presence at diagnosis and uncontrolled after oncologic treatment) and to the time to progression. Tumor-related epilepsy at diagnosis was a predictor of a longer overall survival (adjusted hazard ratio, 0.78; 95% CI 0.67-0.90; p < 0.001) independent of age, Karnofsky Performance Status score, tumor location and volume, extent of resection, standard combined chemoradiotherapy, levetiracetam use, and MGMT promoter methylation. Discussion: The progression of tumor-related epilepsy with the evolution of glioblastoma, IDH-wild-type, and the effects of surgery on seizure control argue for proper antiseizure medication and maximal safe resection. Tumor-related epilepsy is an independent predictor of a longer survival.

First Seizure From Sleep: Clinical Features and Prognosis Lawn ND, Pang EW, Lee J, Dunne JW. Epilepsia . 2023;64(10):2714-2724. doi: 10.1111/epi.17712 Objectives: Patients with a first-ever unprovoked seizure commonly have subsequent seizures and identifying predictors of recurrence has important management implications. Both prior brain insult and epileptiform abnormalities on electroencephalography (EEG) are established predictors of seizure recurrence. Some studies suggest that a first-ever seizure from sleep has a higher likelihood of recurrence. However, with relatively small numbers and inconsistent definitions, more data are required. Methods: Prospective cohort study of adults with first-ever unprovoked seizure seen by a hospital-based first seizure service between 2000 and 2015. Clinical features and outcomes of first-ever seizure from sleep and while awake were compared. Results: First-ever unprovoked seizure occurred during sleep in 298 of 1312 patients (23%), in whom the 1-year cumulative risk of recurrence was 56.9% (95% confidence interval [CI] 51.3-62.6) compared to 44.2% (95% CI 41.1-47.3, p < .0001) for patients with first-ever seizure while awake. First-ever seizure from sleep was an independent predictor of seizure recurrence, with a hazard ratio [HR] of 1.44 (95% CI 1.23-1.69), similar to epileptiform abnormalities on EEG (HR 1.48, 95% CI 1.24-1.76) and remote symptomatic etiology (HR 1.47, 95% CI 1.27-1.71). HR for recurrence in patients without either epileptiform abnormalities or remote symptomatic etiology was 1.97 (95% CI 1.60-2.44) for a sleep seizure compared to an awake seizure. For first seizure from sleep, 76% of second seizures also arose from sleep (p < .0001), with 65% of third seizures (p < .0001) also from sleep. Seizures from sleep were less likely to be associated with injury other than orolingual trauma, both with the presenting seizure (9.4% vs 30.6%, p < .0001) and first recurrence (7.5% vs 16.3%, p = .001). Significance: First-ever unprovoked seizures from sleep are more likely to recur, independent of other risk factors, with recurrences also usually from sleep, and with a lower risk of seizure-related injury. These findings may inform treatment decisions and counseling after first-ever seizure.

Antiseizure medications (ASMs) are the mainstay of symptomatic epilepsy treatment. The primary goal of pharmacotherapy with ASMs in epilepsy is to achieve complete seizure remission while minimizing therapy-related adverse events. Over the years, more ASMs have been introduced, with approximately 30 now in everyday use. With such a wide variety, much guidance is needed in choosing ASMs for initial therapy, subsequent replacement monotherapy, or adjunctive therapy. The specific ASMs are typically tailored by the patient’s related factors, including epilepsy syndrome, age, sex, comorbidities, and ASM characteristics, including the spectrum of efficacy, pharmacokinetic properties, safety, and tolerability. Weighing these key clinical variables requires experience and expertise that may be limited. Furthermore, with this approach, patients may endure multiple trials of ineffective treatments before the most appropriate ASM is found. A more reliable way to predict response to different ASMs is needed so that the most effective and tolerated ASM can be selected. Soon, alternative approaches, such as deep machine learning (ML), could aid the individualized selection of the first and subsequent ASMs. The recognition of epilepsy as a network disorder and the integration of personalized epilepsy networks in future ML platforms can also facilitate the prediction of ASM response. Augmenting the conventional approach with artificial intelligence (AI) opens the door to personalized pharmacotherapy in epilepsy. However, more work is needed before these models are ready for primetime clinical practice.

AbstractBackground and purposeCerebral amyloid angiopathy (CAA) is a common cause of intracranial hemorrhage (ICH), which is a risk factor for seizures. The incidence and risk factors of seizures associated with a heterogeneous cohort of CAA patients have not been studied.MethodsWe conducted a retrospective study of patients with CAA treated at Mayo Clinic Florida between 1 January 2015 and 1 January 2021. CAA was defined using the modified Boston criteria version 2.0. We analyzed electrophysiological and clinical features, and comorbidities including lobar ICH, nontraumatic cortical/convexity subarachnoid hemorrhage (cSAH), superficial siderosis, and inflammation (CAA with inflammation [CAA‐ri]). Cognition and mortality were secondary outcomes. Univariate and multivariate analyses were performed to determine risk of seizures relative to clinical presentation.ResultsTwo hundred eighty‐four patients with CAA were identified, with median follow‐up of 35.7 months (interquartile range = 13.5–61.3 months). Fifty‐six patients (19.7%) had seizures; in 21 (37.5%) patients, seizures were the index feature leading to CAA diagnosis. Seizures were more frequent in females (p = 0.032) and patients with lobar ICH (p = 0.002), cSAH (p = 0.030), superficial siderosis (p < 0.001), and CAA‐ri (p = 0.005), and less common in patients with microhemorrhage (p = 0.006). After controlling for age and sex, lobar ICH (odds ratio [OR] = 2.1, 95% confidence interval [CI] = 1.1–4.2), CAA‐ri (OR = 3.8, 95% CI = 1.4–10.3), and superficial siderosis (OR = 3.7, 95% CI = 1.9–7.0) were independently associated with higher odds of incident seizures.ConclusionsSeizures are common in patients with CAA and are independently associated with lobar ICH, CAA‐ri, and superficial siderosis. Our results may be applied to optimize clinical monitoring and management for patients with CAA.

ABSTRACTAntibodies against the 65‐kDa isoform of the intracellular enzyme, glutamate decarboxylase (GAD65), have been found in patients with limbic encephalitis and drug‐resistant autoimmune epilepsy. We report a 22‐year‐old female who presented with new‐onset seizures and neuropsychiatric symptoms. Video‐EEG captured unique, independent bitemporal‐onset focal seizures with impaired awareness and ictal asystole. An autoimmune epilepsy panel revealed elevated GAD65 antibodies in the serum (225 nmol/l) and CSF (2.78 nmol/l), while [18F]‐fluoro‐deoxy‐glucose positron emission tomography showed bitemporal hypometabolism (left > right). The patient was diagnosed with GAD65 antibody‐associated autoimmune epilepsy. Our observation adds to the spectrum of neurocardiac syndromes associated with autoimmune epilepsy.

Summary: Smartphones and other personal electronic devices present novel cortical processing tasks with potential for identification of novel EEG waveforms. A 17-year-old patient with epilepsy manifested as recurrent myoclonic seizures, absence seizures, and a single generalized tonic–clonic seizure was hospitalized to undergo video-EEG monitoring for seizure quantification and classification of the epilepsy syndrome. During the monitoring session, a frontocentral predominant 5 to 6 Hz theta rhythm was identified only when the patient was actively texting or playing a video game on his smartphone. Previously, patients with focal epilepsy have been found to have a frontocentral theta rhythm on EEG while texting on mobile devices. We report similar EEG findings in a patient with genetic generalized epilepsy during smartphone gaming to expand the population and triggers for this theta waveform. Given the young age and type of epilepsy, we suggest that the waveform represents the EEG manifestation of the attention–visuomotor pathway that is stimulus independent.