Seizure exacerbation by antiepileptic drugs

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches

PURPOSE

Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods.

METHODS

We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa.

RESULTS

The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use.

CONCLUSION

A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging.

Drug-resistant generalized epilepsies: Revisiting the frontiers of idiopathic generalized epilepsies

The 2017 International League Against Epilepsy (ILAE) classification suggested that the term "genetic generalized epilepsies" (GGEs) should be used for the broad group of epilepsies with so-called "generalized" seizure types and "generalized" spike-wave activity on EEG, based on a presumed genetic etiology. Within this framework, idiopathic generalized epilepsies (IGEs) are described as a subset of GGEs and include only four epileptic syndromes: childhood absence epilepsy, juvenile absence epilepsy, juvenile myoclonic epilepsy, and epilepsy with generalized tonic-clonic seizures alone. The recent 2022 ILAE definition of IGEs is based on the current state of knowledge and reflects a community consensus and is designed to evolve as knowledge advances. The term "frontiers of IGEs" refers to the actual limits of our understanding of these four syndromes. Indeed, among patients presenting with a syndrome compatible with the 2022 definition of IGEs, we still observe a significant proportion of patients presenting with specific clinical features, refractory seizures, or drug-resistant epilepsies. This leads to the discussion of the boundaries of IGEs and GGEs, or what is accepted within a clinical spectrum of a definite IGE. Here, we discuss several entities that have been described in the literature for many years and that may either constitute rare features of IGEs or a distinct differential diagnosis. Their recognition by clinicians may allow a more individualized approach and improve the management of patients presenting with such entities.

Efficacy and tolerability of antiseizure drugs

Drug-resistant epilepsy occurs in 25-30% of patients. Furthermore, treatment with a first-generation antiseizure drug (ASD) fails in 30-40% of individuals because of their intolerable adverse effects. Over the past three decades, 20 newer- (second- and third-)generation ASDs with unique mechanisms of action and pharmacokinetic profiles have been introduced into clinical practice. This advent has expanded the therapeutic armamentarium of epilepsy and broadens the choices of ASDs to match the individual patient's characteristics. In recent years, research has been focused on defining the ASD of choice for different seizure types. In 2017, the International League Against Epilepsy published a new classification for seizure types and epilepsy syndrome. This classification has been of paramount importance to accurately classify the patient's seizure type(s) and prescribe the ASD that is appropriate. A year later, the American Academy of Neurology published a new guideline for ASD selection in adult and pediatric patients with new-onset and treatment-resistant epilepsy. The guideline primarily relied on studies that compare the first-generation and second-generation ASDs, with limited data for the efficacy of third-generation drugs. While researchers have been called for investigating those drugs in future research, epilepsy specialists may wish to share their personal experiences to support the treatment guidelines. Given the rapid advances in the development of ASDs in recent years and the continuous updates in definitions, classifications, and treatment guidelines for seizure types and epilepsy syndromes, this review aims to present a complete overview of the current state of the literature about the efficacy and tolerability of ASDs and provide guidance to clinicians about selecting appropriate ASDs for initial treatment of epilepsy according to different seizure types and epilepsy syndromes based on the current literature and recent US and UK practical guidelines.

Neuropharmacology of Antiseizure Drugs

BACKGROUND

Antiseizure drugs (ASDs) are the primary therapy for epilepsy, with more than 20 drugs introduced into clinical practice to date. These drugs are typically grouped by their mechanisms of action and therapeutic spectrum. This article aims to educate non-neurologists and medical students about the new frontiers in the pharmacology of ASDs and presents the current state of the literature on the efficacy and tolerability of these agents.

METHODS

Randomized controlled trials, observational studies, and evidence-based meta-analyses of ASD efficacy and tolerability as initial monotherapy for epileptic seizures and syndromes were identified in PubMed, EMBASE, the Cochrane Library, and Elsevier Clinical Pharmacology.

RESULTS

The choice of ASD varies primarily according to the seizure type. Practical guidelines for ASD selection in patients with new-onset and drug-resistant epilepsy were recently published. The guidelines have shown that the newer-generation drugs, which have unique mechanistic and pharmacokinetic properties, are better tolerated but have similar efficacy compared with the older drugs. Several ASDs are effective as first-line monotherapy in focal seizures, including lamotrigine, carbamazepine, phenytoin, levetiracetam, and zonisamide. Valproate remains the first-line drug for many patients with generalized and unclassified epilepsies. However, valproate should be avoided, if possible, in women of childbearing potential because of teratogenicity. Toxicity profile precludes several drugs from use as first-line treatment, for example, vigabatrin, felbamate, and rufinamide.

CONCLUSIONS

Antiseizure drugs have different pharmacologic profiles that should be considered when selecting and prescribing these agents for epilepsy. These include pharmacokinetic properties, propensity for drug-drug interactions, and adverse effects.

Smartphone EEG and remote online interpretation for children with epilepsy in the Republic of Guinea: Quality, characteristics, and practice implications

PURPOSE

Children with epilepsy in low-income countries often go undiagnosed and untreated. We examine a portable, low-cost smartphone-based EEG technology in a heterogeneous pediatric epilepsy cohort in the West African Republic of Guinea.

METHODS

Children with epilepsy were recruited at the Ignace Deen Hospital in Conakry, 2017. Participants underwent sequential EEG recordings with an app-based EEG, the Smartphone Brain Scanner-2 (SBS2) and a standard Xltek EEG. Raw EEG data were transmitted via Bluetooth^™ connection to an Android^™ tablet and uploaded for remote EEG specialist review and reporting via a new, secure web-based reading platform, crowdEEG. The results were compared to same-visit Xltek 10-20 EEG recordings for identification of epileptiform and non-epileptiform abnormalities.

RESULTS

97 children meeting the International League Against Epilepsy's definition of epilepsy (49 male; mean age 10.3 years, 29 untreated with an antiepileptic drug; 0 with a prior EEG) were enrolled. Epileptiform discharges were detected on 21 (25.3%) SBS2 and 31 (37.3%) standard EEG recordings. The SBS2 had a sensitivity of 51.6% (95%CI 32.4%, 70.8%) and a specificity of 90.4% (95%CI 81.4%, 94.4%) for all types of epileptiform discharges, with positive and negative predictive values of 76.2% and 75.8% respectively. For generalized discharges, the SBS2 had a sensitivity of 43.5% with a specificity of 96.2%.

CONCLUSIONS

The SBS2 has a moderate sensitivity and high specificity for the detection of epileptiform abnormalities in children with epilepsy in this low-income setting. Use of the SBS2+crowdEEG platform permits specialist input for patients with previously poor access to clinical neurophysiology expertise.

Angelman Syndrome: Identification and Management

Angelman syndrome (AS) is a neurobehavioral and genetically determined condition, which affects approximately 1 in 15,000 individuals. It is caused by various genetic mutations and deletions of the maternally-inherited UBE3A gene, on the 15q11-13 chromosomal region. The UBE3A gene, which encodes E3 ubiquitin ligase, shows tissue-specific imprinting, being expressed entirely from the maternal allele.The diagnosis of AS is confirmed either by methylation test or by mutation analysis. A more severe clinical picture is linked with the deletion phenotype.Patients with AS have a behavioral and motor pattern defined as "happy puppet" because it is characterized by puppet-like ataxic jerky movements; a happy, sociable disposition; and paroxysms of laughter. There is currently no cure for AS, and management is mainly symptomatic. Novel therapeutic options are directed toward the possibility of activating the silenced paternal copy of the UBE3A gene.