Kai Michael Schubert, Vijaya Dasari, Ana Lúcia Oliveira, Chiara Tatillo, Gilles Naeije, Adam Strzelczyk, Giovanni Merlino, Mariarosaria Valente, Nicolas Gaspard, Vineet Punia, Marian Galovic, Carla Bentes
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Early electroencephalography (EEG) findings may enhance risk prediction, particularly in patients without acute symptomatic seizures, aiding in patient management and counseling.</p><p><strong>Methods: </strong>We conducted a multicenter cohort study using data from 1,105 stroke survivors (mean age 71 years, 54% male) with neuroimaging-confirmed ischemic stroke who underwent EEG within 7 days post-stroke. Electrographic biomarkers, including epileptiform activity and regional slowing, were analyzed for their association with post-stroke epilepsy using Cox proportional hazards regression and Fine-Gray subdistribution hazard models, adjusted for differences in EEG timing and patient characteristics.</p><p><strong>Results: </strong>Post-stroke epilepsy developed in 119 patients (11%), whereas 233 (21%) had acute symptomatic seizures. The 5-year epilepsy risk was 42% (95% confidence interval [CI]: 30-49%) in patients with epileptiform activity versus 13% (95% CI: 9-16%) in those without. Regional slowing doubled the 5-year epilepsy risk (23%, 95% CI: 17-30% vs 11%, 95% CI: 7-16%). Epileptiform activity (subdistribution hazard ratio: 2.3, 95% CI: 1.5-3.4, p < 0.001) and regional slowing (subdistribution hazard ratio: 1.7, 95% CI: 1.1-2.7, p = 0.02) were independently associated with post-stroke epilepsy. A novel prognostic model, SeLECT-EEG (concordance statistic: 0.75, 95% CI: 0.71-0.80), outperformed the previous standard (SeLECT<sub>2.0</sub>; 0.71, 95% CI: 0.65-0.76, p < 0.001).</p><p><strong>Interpretation: </strong>Electrographic biomarkers improve post-stroke epilepsy prediction beyond clinical risk factors. The SeLECT-EEG model enhances early risk stratification, particularly in patients without acute symptomatic seizures, informing management strategies and patient counseling. ANN NEUROL 2025.</p>","PeriodicalId":127,"journal":{"name":"Annals of Neurology","volume":" ","pages":""},"PeriodicalIF":8.1000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Role of Electroencephalography in Predicting Post-Stroke Seizures and an Updated Prognostic Model (SeLECT-EEG).\",\"authors\":\"Kai Michael Schubert, Vijaya Dasari, Ana Lúcia Oliveira, Chiara Tatillo, Gilles Naeije, Adam Strzelczyk, Giovanni Merlino, Mariarosaria Valente, Nicolas Gaspard, Vineet Punia, Marian Galovic, Carla Bentes\",\"doi\":\"10.1002/ana.27301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Seizures negatively impact stroke outcomes, highlighting the need for reliable predictors of post-stroke epilepsy. Although acute symptomatic seizures are a known risk factor, most stroke survivors who develop epilepsy do not experience them. 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Regional slowing doubled the 5-year epilepsy risk (23%, 95% CI: 17-30% vs 11%, 95% CI: 7-16%). Epileptiform activity (subdistribution hazard ratio: 2.3, 95% CI: 1.5-3.4, p < 0.001) and regional slowing (subdistribution hazard ratio: 1.7, 95% CI: 1.1-2.7, p = 0.02) were independently associated with post-stroke epilepsy. A novel prognostic model, SeLECT-EEG (concordance statistic: 0.75, 95% CI: 0.71-0.80), outperformed the previous standard (SeLECT<sub>2.0</sub>; 0.71, 95% CI: 0.65-0.76, p < 0.001).</p><p><strong>Interpretation: </strong>Electrographic biomarkers improve post-stroke epilepsy prediction beyond clinical risk factors. The SeLECT-EEG model enhances early risk stratification, particularly in patients without acute symptomatic seizures, informing management strategies and patient counseling. ANN NEUROL 2025.</p>\",\"PeriodicalId\":127,\"journal\":{\"name\":\"Annals of Neurology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2025-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Neurology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/ana.27301\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Neurology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/ana.27301","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
目的：癫痫发作对卒中预后有负面影响，强调卒中后癫痫可靠预测指标的必要性。虽然急性症状性癫痫发作是一个已知的危险因素，但大多数中风幸存者并没有癫痫发作的经历。早期脑电图（EEG）结果可以提高风险预测，特别是在没有急性症状性癫痫发作的患者中，有助于患者管理和咨询。方法：我们进行了一项多中心队列研究，使用了1105名中风幸存者（平均年龄71岁，54%男性）的数据，这些中风幸存者在中风后7天内接受了脑电图检查。使用Cox比例风险回归和Fine-Gray亚分布风险模型，对电图生物标志物（包括癫痫样活动和区域减缓）与卒中后癫痫的相关性进行分析，并对EEG时间和患者特征的差异进行调整。结果：119例（11%）发生脑卒中后癫痫，233例（21%）发生急性症状性癫痫发作。癫痫样活动患者的5年癫痫风险为42%(95%可信区间[CI]: 30-49%)，而无癫痫样活动患者的5年癫痫风险为13%（95%可信区间[CI]: 9-16%）。区域性减缓使5年癫痫风险加倍（23%,95% CI: 17-30% vs 11%, 95% CI: 7-16%）。癫痫样活动(亚分布风险比：2.3,95% CI: 1.5-3.4, p 2.0；0.71, 95% CI: 0.65-0.76， p解释：电图生物标志物在卒中后癫痫预测方面优于临床危险因素。SeLECT-EEG模型增强了早期风险分层，特别是在没有急性症状性癫痫发作的患者中，为管理策略和患者咨询提供了信息。Ann neurol 2025。
The Role of Electroencephalography in Predicting Post-Stroke Seizures and an Updated Prognostic Model (SeLECT-EEG).
Objective: Seizures negatively impact stroke outcomes, highlighting the need for reliable predictors of post-stroke epilepsy. Although acute symptomatic seizures are a known risk factor, most stroke survivors who develop epilepsy do not experience them. Early electroencephalography (EEG) findings may enhance risk prediction, particularly in patients without acute symptomatic seizures, aiding in patient management and counseling.
Methods: We conducted a multicenter cohort study using data from 1,105 stroke survivors (mean age 71 years, 54% male) with neuroimaging-confirmed ischemic stroke who underwent EEG within 7 days post-stroke. Electrographic biomarkers, including epileptiform activity and regional slowing, were analyzed for their association with post-stroke epilepsy using Cox proportional hazards regression and Fine-Gray subdistribution hazard models, adjusted for differences in EEG timing and patient characteristics.
Results: Post-stroke epilepsy developed in 119 patients (11%), whereas 233 (21%) had acute symptomatic seizures. The 5-year epilepsy risk was 42% (95% confidence interval [CI]: 30-49%) in patients with epileptiform activity versus 13% (95% CI: 9-16%) in those without. Regional slowing doubled the 5-year epilepsy risk (23%, 95% CI: 17-30% vs 11%, 95% CI: 7-16%). Epileptiform activity (subdistribution hazard ratio: 2.3, 95% CI: 1.5-3.4, p < 0.001) and regional slowing (subdistribution hazard ratio: 1.7, 95% CI: 1.1-2.7, p = 0.02) were independently associated with post-stroke epilepsy. A novel prognostic model, SeLECT-EEG (concordance statistic: 0.75, 95% CI: 0.71-0.80), outperformed the previous standard (SeLECT2.0; 0.71, 95% CI: 0.65-0.76, p < 0.001).
Interpretation: Electrographic biomarkers improve post-stroke epilepsy prediction beyond clinical risk factors. The SeLECT-EEG model enhances early risk stratification, particularly in patients without acute symptomatic seizures, informing management strategies and patient counseling. ANN NEUROL 2025.
期刊介绍：
Annals of Neurology publishes original articles with potential for high impact in understanding the pathogenesis, clinical and laboratory features, diagnosis, treatment, outcomes and science underlying diseases of the human nervous system. Articles should ideally be of broad interest to the academic neurological community rather than solely to subspecialists in a particular field. Studies involving experimental model system, including those in cell and organ cultures and animals, of direct translational relevance to the understanding of neurological disease are also encouraged.
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