Feyissa, Anteneh M.

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.

Summary: The responsive neurostimulator continuously monitors the electrocorticogram. It delivers short bursts of high-frequency electrical stimulation when personalized patterns are detected. Intracranial EEG recording including electrocorticography is susceptible to artifacts, albeit at a lesser frequency compared with scalp recording. The authors describe a novel case of a patient with focal epilepsy, bitemporal responsive neurostimulation, and seizures without self-awareness manifest as focal impaired awareness seizures adversely affecting memory. At follow-up evaluation, the patient reported being clinically seizure-free although a single long episode was detected using the Patient Data Management System over the course of 3 years. Initial review identified a left-sided rhythmic discharge with a bilateral spatial field of involvement. In response to detection, the responsive neurostimulation delivered a series of five electrical stimulations. On further review, the patient recalled undergoing cervical radiofrequency ablation, which coincided with the appearance of the “electrographic seizure.” Extrinsic electrical artifact involving monomorphic nonevolving waveforms confirmed electrical artifact identified and treated by responsive neurostimulation as an epileptic seizure. On rare occasion, implanted electrical devices may lead to misdiagnosis and mistreatment of patients because of intracranial artifact.