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ObjectiveThe aim was to compare the outcomes of subdural electrode (SDE) implantations versus stereotactic electroencephalography (SEEG), the 2 predominant methods of intracranial electroencephalography (iEEG) performed in difficult-to-localize drug-resistant focal epilepsy.MethodsThe Surgical Therapies Commission of the International League Against Epilepsy created an international registry of iEEG patients implanted between 2005 and 2019 with ≥1 year of follow-up. We used propensity score matching to control exposure selection bias and generate comparable cohorts. Study endpoints were: (1) likelihood of resection after iEEG; (2) seizure freedom at last follow-up; and (3) complications (composite of postoperative infection, symptomatic intracranial hemorrhage, or permanent neurological deficit).ResultsTen study sites from 7 countries and 3 continents contributed 2,012 patients, including 1,468 (73%) eligible for analysis (526 SDE and 942 SEEG), of whom 988 (67%) underwent subsequent resection. Propensity score matching improved covariate balance between exposure groups for all analyses. Propensity-matched patients who underwent SDE had higher odds of subsequent resective surgery (odds ratio [OR] = 1.4, 95% confidence interval [CI] 1.05, 1.84) and higher odds of complications (OR = 2.24, 95% CI 1.34, 3.74; unadjusted: 9.6% after SDE vs 3.3% after SEEG). Odds of seizure freedom in propensity-matched resected patients were 1.66 times higher (95% CI 1.21, 2.26) for SEEG compared with SDE (unadjusted: 55% seizure free after SEEG-guided resections vs 41% after SDE).InterpretationIn comparison to SEEG, SDE evaluations are more likely to lead to brain surgery in patients with drug-resistant epilepsy but have more surgical complications and lower probability of seizure freedom. This comparative-effectiveness study provides the highest feasible evidence level to guide decisions on iEEG. ANN NEUROL 2021;90:927-939.

Objective Methods Results Significance Health state utilities are required to obtain quality adjusted life years, a common metric that informs clinical decision‐making at individual, group, and health policy levels. Health state utilities are different from health‐related quality of life, and their distribution across patients with epilepsy, as well as the factors that impact them, have not been studied in depth. We aimed to describe the distribution of health state utilities in people with epilepsy and the impact of different combinations of clinical and demographic factors on health state evaluation.We performed a retrospective analysis of patients' data prospectively collected in the Calgary Comprehensive Epilepsy Program registry. Patient‐reported health state utilities were measured using the 5‐level EuroQol 5‐Dimension scale (EQ‐5D‐5L) completed at their initial assessment. EQ‐5D‐5L index scores were derived via the time trade‐off approach based on Canadian norms, and their distribution across different health states and patient characteristics was obtained. The Tobit regression model was used to evaluate the determinants of EQ‐5D‐5L index scores.Of 1446 patients included in this analysis, 724 (50.5%) were female. The median (interquartile range) Canada‐normed EQ‐5D‐5L index score was .87 (.71–.91). Patients with significantly lower health utilities were more likely to be female (p = .008), to be older (p = .034), to be unmarried (p = .013), to have failed to achieve 1‐year seizure freedom (p < .001), to have no postsecondary education (p = .028), to be depressed (p < .001), to have antiseizure medication side effects (p = .001), to be unemployed (p < .001), and to be unable to drive (p < .001). A look‐up table of health utilities based on combinations of clinical–demographic characteristics was produced.Health utility estimates for combinations of different health states in people with epilepsy attending specialty clinics are now available. These can help guide clinical decision‐making in routine clinical practice, economic evaluations of treatment interventions, and health care policies. [ABSTRACT FROM AUTHOR]

The International League Against Epilepsy (ILAE) Classification of the Epilepsies has been updated to reflect our gain in understanding of the epilepsies and their underlying mechanisms following the major scientific advances that have taken place since the last ratified classification in 1989. As a critical tool for the practicing clinician, epilepsy classification must be relevant and dynamic to changes in thinking, yet robust and translatable to all areas of the globe. Its primary purpose is for diagnosis of patients, but it is also critical for epilepsy research, development of antiepileptic therapies, and communication around the world. The new classification originates from a draft document submitted for public comments in 2013, which was revised to incorporate extensive feedback from the international epilepsy community over several rounds of consultation. It presents three levels, starting with seizure type, where it assumes that the patient is having epileptic seizures as defined by the new 2017 ILAE Seizure Classification. After diagnosis of the seizure type, the next step is diagnosis of epilepsy type, including focal epilepsy, generalized epilepsy, combined generalized, and focal epilepsy, and also an unknown epilepsy group. The third level is that of epilepsy syndrome, where a specific syndromic diagnosis can be made. The new classification incorporates etiology along each stage, emphasizing the need to consider etiology at each step of diagnosis, as it often carries significant treatment implications. Etiology is broken into six subgroups, selected because of their potential therapeutic consequences. New terminology is introduced such as developmental and epileptic encephalopathy. The term benign is replaced by the terms self-limited and pharmacoresponsive, to be used where appropriate. It is hoped that this new framework will assist in improving epilepsy care and research in the 21st century.

The ILAE Task Force on Classification presents a road map for the development of an updated, relevant classification of the epilepsies. Our objective is to explain the process to date and the plan moving forward as well as to invite further discussion about the newly proposed terms and concepts. Here, we present our response to feedback about the 2010 Organization of the Epilepsies and clarify the reintroduction of the word "classification" to map out a framework for epilepsy diagnosis. We introduce some new concepts and suggest four diagnostic levels: seizure type, epilepsy category, epilepsy syndrome, and epilepsy with (specific) etiology to denote specific levels of diagnosis. We expand the etiological categories to six, focusing on those with treatment implications. Finally, we discuss the changes in terminology originally suggested and modifications in response to comments from the epilepsy community. We welcome feedback and discussion from the global epilepsy community, particularly for the new suggested terms, so that we can cement a classification that both reflects current thinking and scientific understanding and provides a dynamic, evolving framework.

BackgroundGuidelines recommend that surgery be considered in patients with drug-resistant epilepsy, yet delays to epilepsy surgery still exist. A Web-based, evidence-informed clinical decision tool (www.toolsforepilepsy.com) was developed to help physicians determine which patients are appropriate for an epilepsy surgery evaluation. We evaluated the usability and feasibility of the tool with the intended end users in order to improve implementation into practice.MethodsUsability testing was conducted with relevant end users. After the tool was modified based on usability results, another group of end users trialed the tool in their clinical practice. This latter group of end users then participated in focus groups and semi-structured interviews to address barriers and facilitators to tool implementation. Finally, a stakeholder meeting was held with domain experts and end users to discuss further changes to the tool and implementation strategies.ResultsSix overall themes were identified through usability testing, and an additional 11 themes were identified through the focus groups and interviews. The tool was modified based on these findings, which were then presented at the stakeholder meeting of experts and end users for further refinement. The findings were also used to guide discussions of potential implementation strategies at the meeting.ConclusionThis study provides guidance on how to improve the usability of clinical decision tools by engaging end users, experts, and other key stakeholders. The modifications to the tool should facilitate its implementation in clinical practice and ultimately enhance the quality of care persons with epilepsy receive.

Epilepsy was defined conceptually in 2005 as a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures. This definition is usually practically applied as having two unprovoked seizures >24 h apart. The International League Against Epilepsy (ILAE) accepted recommendations of a task force altering the practical definition for special circumstances that do not meet the two unprovoked seizures criteria. The task force proposed that epilepsy be considered to be a disease of the brain defined by any of the following conditions: (1) At least two unprovoked (or reflex) seizures occurring >24 h apart; (2) one unprovoked (or reflex) seizure and a probability of further seizures similar to the general recurrence risk (at least 60%) after two unprovoked seizures, occurring over the next 10 years; (3) diagnosis of an epilepsy syndrome. Epilepsy is considered to be resolved for individuals who either had an age-dependent epilepsy syndrome but are now past the applicable age or who have remained seizure-free for the last 10 years and off antiseizure medicines for at least the last 5 years. "Resolved" is not necessarily identical to the conventional view of "remission or "cure." Different practical definitions may be formed and used for various specific purposes. This revised definition of epilepsy brings the term in concordance with common use. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.

A biomarker is defined as an objectively measured characteristic of a normal or pathologic biologic process. Identification and proper validation of biomarkers of epileptogenesis (the development of epilepsy) and ictogenesis (the propensity to generate spontaneous seizures) might predict the development of an epilepsy condition; identify the presence and severity of tissue capable of generating spontaneous seizures; measure progression after the condition is established; and determine pharmacoresistance. Such biomarkers could be used to create animal models for more cost-effective screening of potential antiepileptogenic and antiseizure drugs and devices, and to reduce the cost of clinical trials by enriching the trial population, and acting as surrogate markers to shorten trial duration. The objectives of the biomarker subgroup for the London Workshop were to define approaches for identifying possible biomarkers for these purposes. Research to identify reliable biomarkers may also reveal underlying mechanisms that could serve as therapeutic targets for the development of new antiepileptogenic and antiseizure compounds.

A biomarker is defined as an objectively measured characteristic of a normal or pathologic biologic process. Identification and proper validation of biomarkers of epileptogenesis (the development of epilepsy) and ictogenesis (the propensity to generate spontaneous seizures) might predict the development of an epilepsy condition; identify the presence and severity of tissue capable of generating spontaneous seizures; measure progression after the condition is established; and determine pharmacoresistance. Such biomarkers could be used to create animal models for more cost-effective screening of potential antiepileptogenic and antiseizure drugs and devices, and to reduce the cost of clinical trials by enriching the trial population, and acting as surrogate markers to shorten trial duration. The objectives of the biomarker subgroup for the London Workshop were to define approaches for identifying possible biomarkers for these purposes. Research to identify reliable biomarkers may also reveal underlying mechanisms that could serve as therapeutic targets for the development of new antiepileptogenic and antiseizure compounds.

Objective: This study was undertaken to develop a multimodal machine learning (ML) approach for predicting incident depression in adults with epilepsy. Methods: We randomly selected 200 patients from the Calgary Comprehensive Epilepsy Program registry and linked their registry‐based clinical data to their first‐available clinical electroencephalogram (EEG) and magnetic resonance imaging (MRI) study. We excluded patients with a clinical or Neurological Disorders Depression Inventory for Epilepsy (NDDI‐E)‐based diagnosis of major depression at baseline. The NDDI‐E was used to detect incident depression over a median of 2.4 years of follow‐up (interquartile range [IQR] = 1.5–3.3 years). A ReliefF algorithm was applied to clinical as well as quantitative EEG and MRI parameters for feature selection. Six ML algorithms were trained and tested using stratified threefold cross‐validation. Multiple metrics were used to assess model performances. Results: Of 200 patients, 150 had EEG and MRI data of sufficient quality for ML, of whom 59 were excluded due to prevalent depression. Therefore, 91 patients (41 women) were included, with a median age of 29 (IQR = 22–44) years. A total of 42 features were selected by ReliefF, none of which was a quantitative MRI or EEG variable. All models had a sensitivity > 80%, and five of six had an F1 score ≥.72. A multilayer perceptron model had the highest F1 score (median =.74, IQR =.71–.78) and sensitivity (84.3%). Median area under the receiver operating characteristic curve and normalized Matthews correlation coefficient were.70 (IQR =.64–.78) and.57 (IQR =.50–.65), respectively. Significance: Multimodal ML using baseline features can predict incident depression in this population. Our pilot models demonstrated high accuracy for depression prediction. However, overall performance and calibration can be improved. This model has promise for identifying those at risk for incident depression during follow‐up, although efforts to refine it in larger populations along with external validation are required. [ABSTRACT FROM AUTHOR]