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The majority of people with epilepsy achieves long-term seizure-freedom and may consider withdrawal of their anti-seizure medications (ASMs). Withdrawal of ASMs can yield substantial benefits but may be associated with potential risks. This review critically examines the existing literature on ASM withdrawal, emphasizing evidence-based recommendations, where available. Our focus encompasses deprescribing strategies for individuals who have attained seizure freedom through medical treatment, those who have undergone successful epilepsy surgery, and individuals initiated on ASMs following acute symptomatic seizures. We explore state-of-the-art prognostic models in these scenarios that could guide the decision-making process. The review underscores the importance of a collaborative shared-decision approach between patients, caregivers, and physicians. We describe the subjective and objective factors influencing these decisions and illustrate how trade-offs may be effectively managed in practice. [ABSTRACT FROM AUTHOR]

More than half of adults with epilepsy undergoing resective epilepsy surgery achieve long-term seizure freedom and might consider withdrawing antiseizure medications (ASMs). We aimed to identify predictors of seizure recurrence after starting postoperative ASM withdrawal and develop and validate predictive models. We performed an international multicentre observational cohort study in nine tertiary epilepsy referral centres. We included 850 adults who started ASM withdrawal following resective epilepsy surgery and were free of seizures other than focal non-motor aware seizures before starting ASM withdrawal. We developed a model predicting recurrent seizures, other than focal non-motor aware seizures, using Cox proportional hazards regression in a derivation cohort (n = 231). Independent predictors of seizure recurrence, other than focal non-motor aware seizures, following the start of ASM withdrawal were focal non motor-aware seizures after surgery and before withdrawal (adjusted hazards ratio [aHR] 5.5, 95% confidence interval [CI] 2.7-11.1), history of focal to bilateral tonic-clonic seizures before surgery (aHR 1.6, 95% CI 0.9-2.8), time from surgery to the start of ASM withdrawal (aHR 0.9, 95% CI 0.8-0.9), and number of ASMs at time of surgery (aHR 1.2, 95% CI 0.9-1.6). Model discrimination showed a concordance statistic of 0.67 (95% CI 0.63-0.71) in the external validation cohorts (n = 500). A secondary model predicting recurrence of any seizures (including focal non-motor aware seizures) was developed and validated in a subgroup that did not have focal non-motor aware seizures before withdrawal (n = 639), showing a concordance statistic of 0.68 (95% CI 0.64-0.72). Calibration plots indicated high agreement of predicted and observed outcomes for both models. We show that simple algorithms, available as graphical nomograms and online tools (predictepilepsy.github.io), can provide probabilities of seizure outcomes after starting postoperative ASMs withdrawal. These multicentre-validated models may assist clinicians when discussing ASM withdrawal after surgery with their patients. [ABSTRACT FROM AUTHOR]

Objective: Choosing candidates for antiseizure medication (ASM) withdrawal in well‐controlled epilepsy is challenging. We evaluated (a) the correlation between neurologists' seizure risk estimation ("clinician predictions") vs calculated predictions, (b) how viewing calculated predictions influenced recommendations, and (c) barriers to using risk calculation. Methods: We asked US and European neurologists to predict 2‐year seizure risk after ASM withdrawal for hypothetical vignettes. We compared ASM withdrawal recommendations before vs after viewing calculated predictions, using generalized linear models. Results: Three‐hundred and forty‐six neurologists responded. There was moderate correlation between clinician and calculated predictions (Spearman coefficient 0.42). Clinician predictions varied widely, for example, predictions ranged 5%‐100% for a 2‐year seizure‐free adult without epileptiform abnormalities. Mean clinician predictions exceeded calculated predictions for vignettes with epileptiform abnormalities (eg, childhood absence epilepsy: clinician 65%, 95% confidence interval [CI] 57%‐74%; calculated 46%) and surgical vignettes (eg, focal cortical dysplasia 6‐month seizure‐free mean clinician 56%, 95% CI 52%‐60%; calculated 28%). Clinicians overestimated the influence of epileptiform EEG findings on withdrawal risk (26%, 95% CI 24%‐28%) compared with calculators (14%, 95% 13%‐14%). Viewing calculated predictions slightly reduced willingness to withdraw (−0.8/10 change, 95% CI −1.0 to −0.7), particularly for vignettes without epileptiform abnormalities. The greatest barrier to calculator use was doubting its accuracy (44%). Significance: Clinicians overestimated the influence of abnormal EEGs particularly for low‐risk patients and overestimated risk and the influence of seizure‐free duration for surgical patients, compared with calculators. These data may question widespread ordering of EEGs or time‐based seizure‐free thresholds for surgical patients. Viewing calculated predictions reduced willingness to withdraw particularly without epileptiform abnormalities. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to identify risk factors for acute symptomatic seizures and post‐stroke epilepsy after acute ischemic stroke and evaluate the effects of reperfusion treatment. Methods: We assessed the risk factors for post‐stroke seizures using logistic or Cox regression in a multicenter study, including adults from 8 European referral centers with neuroimaging‐confirmed ischemic stroke. We compared the risk of post‐stroke seizures between participants with or without reperfusion treatment following propensity score matching to reduce confounding due to treatment selection. Results: In the overall cohort of 4,229 participants (mean age 71 years, 57% men), a higher risk of acute symptomatic seizures was observed in those with more severe strokes, infarcts located in the posterior cerebral artery territory, and strokes caused by large‐artery atherosclerosis. Strokes caused by small‐vessel occlusion carried a small risk of acute symptomatic seizures. 6% developed post‐stroke epilepsy. Risk factors for post‐stroke epilepsy were acute symptomatic seizures, more severe strokes, infarcts involving the cerebral cortex, and strokes caused by large‐artery atherosclerosis. Electroencephalography findings within 7 days of stroke onset were not independently associated with the risk of post‐stroke epilepsy. There was no association between reperfusion treatments in general or only intravenous thrombolysis or mechanical thrombectomy with the time to post‐stroke epilepsy or the risk of acute symptomatic seizures. Interpretation: Post‐stroke seizures are related to stroke severity, etiology, and location, whereas an early electroencephalogram was not predictive of epilepsy. We did not find an association of reperfusion treatment with risks of acute symptomatic seizures or post‐stroke epilepsy. ANN NEUROL 2021;90:808–820 [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is an autoimmune, inflammatory neurodegenerative disease of the central nervous system characterized by demyelination and axonal damage. Diagnosis and prognosis are mainly assessed through clinical examination and neuroimaging. However, more sensitive biomarkers are needed to measure disease activity and guide treatment decisions in MS. Prompt and individualized management can reduce inflammatory activity and delay disease progression. Neurofilament Light chain (NfL), a neuron-specific cytoskeletal protein that is released into the extracellular fluid following axonal injury, has been identified as a biomarker of disease activity in MS. Measurement of NfL levels can capture the extent of neuroaxonal damage, especially in early stages of the disease. A growing body of evidence has shown that NfL in cerebrospinal fluid (CSF) and serum can be used as reliable indicators of prognosis and treatment response. More recently, NfL has been shown to facilitate individualized treatment decisions for individuals with MS. In this review, we discuss the characteristics that make NfL a highly informative biomarker and depict the available technologies used for its measurement. We further discuss the growing role of serum and CSF NfL in MS research and clinical settings. Finally, we address some of the current topics of debate regarding the use of NfL in clinical practice and examine the possible directions that this biomarker may take in the future. [ABSTRACT FROM AUTHOR]

Stroke is the leading cause of seizures and epilepsy in older adults. Patients who have larger and more severe strokes involving the cortex, are younger, and have acute symptomatic seizures and intracerebral haemorrhage are at highest risk of developing post-stroke epilepsy. Prognostic models, including the SeLECT and CAVE scores, help gauge the risk of epileptogenesis. Early electroencephalogram and blood-based biomarkers can provide information additional to the clinical risk factors of post-stroke epilepsy. The management of acute versus remote symptomatic seizures after stroke is markedly different. The choice of an ideal antiseizure medication should not only rely on efficacy but also consider adverse effects, altered pharmacodynamics in older adults, and the influence on the underlying vascular co-morbidity. Drug–drug interactions, particularly those between antiseizure medications and anticoagulants or antiplatelets, also influence treatment decisions. In this review, we describe the epidemiology, risk factors, biomarkers, and management of seizures after an ischaemic or haemorrhagic stroke. We discuss the special considerations required for the treatment of post-stroke epilepsy due to the age, co-morbidities, co-medication, and vulnerability of stroke survivors. [ABSTRACT FROM AUTHOR]