Department of Neurology, Penn State University, Hershey Medical Center, Hershey, PA, USA.
Department of Neurology, Penn State University, Hershey Medical Center, Hershey, PA, USA.
Epilepsy Behav. 2021 Aug;121(Pt A):108003. doi: 10.1016/j.yebeh.2021.108003. Epub 2021 May 21.
We performed a systematic review to evaluate available risk models to predict late seizure onset among stroke survivors.
We searched major databases (PubMed, SCOPUS, and Cochrane Library) from inception to October 2020 for articles on the development and/or validation of risk models to predict late seizures after a stroke. The impact of models to predict late-onset seizures was also assessed. We included seven articles in the final analysis. For each of these studies, we evaluated the study design and scope of predictors analyzed to derive each model. We assessed the performance of the models during internal and external validation in terms of discrimination and calibration.
Three studies focused on ischemic stroke alone, with c-statistic values ranging from 0.73 to 0.77. The SeLECT model from Switzerland was externally validated in Italian, German, and Austrian cohorts where c-statistics ranged from 0.69 to 0.81. This model along with the PSEiCARe model, were internally validated and calibration performance was provided for both models. The CAVS and CAVE models reported on the risk of late-onset seizures in patients with hemorrhagic stroke. The CAVS model derivation cohort was racially diverse. The CAVS model's c-statistic was 0.76, while the CAVE model had a c-statistic of 0.81. Calibration and internal validation were not performed for either study. The CAVS model, created from a Finnish population, was externally validated in American and French cohorts, with c-statistics of 0.73 and 0.69, respectively. Finally, the two studies focusing on both types of stroke came from the PoSERS and INPOSE models. Neither model provided c-statistics, calibration metrics, internal or external validation information. We found no evidence of the presence of impact studies to assess the effect of adopting late-onset seizure risk models after stroke on clinical outcomes.
The SeLECT model was the only model developed in line with proposed guidelines for appropriate model development. The model, which was externally validated in a very similar and homogeneous population, may need to be tested in a more racially/ethnic diverse and younger population; testing the SeLECT model, accounting for overall brain health is likely to improve the identification of high-risk patients for late post stroke seizures.
我们进行了一项系统评价，以评估现有的风险模型，以预测卒中幸存者的迟发性癫痫发作。
我们从成立到 2020 年 10 月，在主要数据库（PubMed、SCOPUS 和 Cochrane Library）中搜索了关于开发和/或验证预测卒中后迟发性癫痫发作风险模型的文章。我们还评估了模型预测迟发性发作的影响。我们最终分析了 7 篇文章。对于每一项研究，我们评估了研究设计和分析的预测因子范围，以得出每个模型。我们根据内部和外部验证中的区分度和校准度评估了模型的性能。
三项研究仅关注缺血性卒中，其 c 统计值范围为 0.73 至 0.77。来自瑞士的 SeLECT 模型在意大利、德国和奥地利的队列中进行了外部验证，c 统计值范围为 0.69 至 0.81。该模型与 PSEiCARe 模型一起进行了内部验证，并为两个模型提供了校准性能。CAVS 和 CAVE 模型报告了出血性卒中患者迟发性发作的风险。CAVS 模型的推导队列具有种族多样性。CAVS 模型的 c 统计值为 0.76，而 CAVE 模型的 c 统计值为 0.81。这两项研究均未进行校准和内部验证。CAVS 模型是从芬兰人群中建立的，在美国和法国的队列中进行了外部验证，c 统计值分别为 0.73 和 0.69。最后，两项同时关注两种类型卒中的研究来自 PoSERS 和 INPOSE 模型。这两个模型都没有提供 c 统计值、校准指标、内部或外部验证信息。我们没有发现任何证据表明有影响研究来评估在卒中后采用迟发性发作风险模型对临床结果的影响。
SeLECT 模型是唯一按照适当模型开发建议制定的模型。该模型在非常相似和同质的人群中进行了外部验证，可能需要在种族/民族更加多样化和更年轻的人群中进行测试；测试考虑整体脑健康的 SeLECT 模型可能会提高对高危患者进行迟发性卒中后癫痫发作的识别。
