Search

Background: In addition to other stroke-related deficits, the risk of seizures may impact driving ability after stroke. Methods: We analysed data from a multicentre international cohort, including 4452 adults with acute ischaemic stroke and no prior seizures. We calculated the Chance of Occurrence of Seizure in the next Year (COSY) according to the SeLECT$_{2.0}$prognostic model. We considered COSY<20% safe for private and <2% for professional driving, aligning with commonly used cut-offs. Results: Seizure risks in the next year were mainly influenced by the baseline risk-stratified according to the SeLECT$_{2.0}$score and, to a lesser extent, by the poststroke seizure-free interval (SFI). Those without acute symptomatic seizures (SeLECT$_{2.0}$0–6 points) had low COSY (0.7%–11%) immediately after stroke, not requiring an SFI. In stroke survivors with acute symptomatic seizures (SeLECT$_{2.0}$3–13 points), COSY after a 3-month SFI ranged from 2% to 92%, showing substantial interindividual variability. Stroke survivors with acute symptomatic status epilepticus (SeLECT$_{2.0}$7–13 points) had the highest risk (14%–92%). Conclusions: Personalised prognostic models, such as SeLECT$_{2.0}$, may offer better guidance for poststroke driving decisions than generic SFIs. Our findings provide practical tools, including a smartphone-based or web-based application, to assess seizure risks and determine appropriate SFIs for safe driving.

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.

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.MethodsWe 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 calcu

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. Th