RESUMEN
Cenobamate has demonstrated efficacy in patients with treatment-resistant epilepsy, including patients who continued to have seizures after epilepsy surgery. This article provides recommendations for cenobamate use in patients referred for epilepsy surgery evaluation. A panel of six senior epileptologists from the United States and Europe with experience in presurgical evaluation of patients with epilepsy and in the use of antiseizure medications (ASMs) was convened to provide consensus recommendations for the use of cenobamate in patients referred for epilepsy surgery evaluation. Many patients referred for surgical evaluation may benefit from ASM optimization; both ASM and surgical treatment should be individualized. Based on previous clinical studies and the authors' clinical experience with cenobamate, a substantial proportion of patients with treatment-resistant epilepsy can become seizure-free with cenobamate. We recommend a cenobamate trial and ASM optimization in parallel with presurgical evaluations. Cenobamate can be started before phase two monitoring, especially in patients who are found to be suboptimal surgery candidates. As neurostimulation therapies are generally palliative, we recommend trying cenobamate before vagus nerve stimulation (VNS), deep brain stimulation, or responsive neurostimulation (RNS). In surgically remediable cases (mesial temporal sclerosis, benign discrete lesion in non-eloquent cortex, cavernous angioma, etc.), cenobamate use should not delay imminent surgery; however, a patient may decide to defer or even cancel surgery should they achieve sustained seizure freedom with cenobamate. This decision should be made on an individual, case-by-case basis based on seizure etiology, patient preferences, potential surgical risks (mortality and morbidity), and likely surgical outcome. The addition of cenobamate after unsuccessful surgery or palliative neuromodulation may also be associated with better outcomes.
RESUMEN
Objective: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. Methods: This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. Results: The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. Conclusions: Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.