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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

[Background] Midregional pro-atrial natriuretic peptide (MR-proANP) is a promising biomarker to differentiate the underlying etiology of acute ischemic stroke (AIS)., [Objectives] This study aimed to determine the role of MR-proANP for classification as cardioembolic (CE) stroke, identification of newly diagnosed atrial fibrillation (NDAF), and risk assessment for major adverse cardiovascular events (MACE)., [Methods] This study measured MR-proANP prospectively collected within 24 hours after symptom-onset in patients with AIS from the multicenter BIOSIGNAL (Biomarker Signature of Stroke Aetiology) cohort study. Primary outcomes were CE stroke etiology and NDAF after prolonged cardiac monitoring, as well as a composite outcome of MACE (recurrent cerebrovascular events, myocardial infarction, or cardiovascular death) within 1 year. Logistic/Poisson and subproportional hazard regression were applied to evaluate the association between MR-proANP levels and outcomes. Additionally, a model for prediction of NDAF was derived and validated as a decision tool for immediate clinical application., [Results] Between October 1, 2014, and October 31, 2017, this study recruited 1,759 patients. Log10MR-proANP levels were associated with CE stroke (OR: 7.96; 95% CI: 4.82-13.14; risk ratio: 3.12; 95% CI: 2.23-4.37), as well as NDAF (OR: 35.3; 95% CI: 17.58-71.03; risk ratio: 11.47; 95% CI: 6.74-19.53), and MACE (subdistributional HR: 2.02; 95% CI: 1.32-3.08) during follow-up. The model to predict NDAF including only age and MR-proANP levels had a good discriminatory capacity with an area under the curve of 0.81 (95% CI: 0.76-0.86), was well calibrated (calibration in the large: −0.086; calibration slope 1.053), and yielded higher net-benefit compared with validated scores to predict NDAF (AS5F score, CHA2DS2-VASc [Congestive Heart Failure, Hypertension, Age ≥65 or ≥75, Diabetes, Prior Cardioembolic Event, (female) Sex, or Vascular Disease] score)., [Conclusions] MR-proANP is a valid biomarker to determine risk of NDAF and MACE in patients with AIS and can be used as a decision tool to identify patients for prolonged cardiac monitoring. (Biomarker Signature of Stroke Aetiology Study: The BIOSIGNAL study [BIOSIGNAL]; NCT02274727)

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.