Abstract：
Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.
摘要：
目的：抗癫痫药物（ASM）是癫痫的主要治疗方法。在临床实践中,评估ASM疗效的方法（预测癫痫发作自由或癫痫发作减少），在药物治疗生命周期的任何阶段，是有限的。本范围审查确定和评估预后脑电图（EEG）生物标志物和使用EEG特征的预后模型，与ASM启动后的癫痫发作结果相关，剂量调整或退出。我们还旨在总结识别和描述这些生物标志物和模型的人群和背景。以了解它们如何用于临床实践。
方法：在2021年1月至2022年10月之间，四个数据库，系统检索了ASM研究的参考文献和引文,这些研究使用脑电图特征和癫痫发作结局来调查发作间脑电图或预后模型的变化.使用改良的预后研究质量标准评估研究偏倚。结果被合成为定性综述。
结果：确定了875项研究，93人包括在内。鉴定的生物标志物被分类为定性的(通过波形态视觉鉴定)或定量的。定性生物标志物包括识别心律失常，中心颞峰,发作间癫痫样放电（IED），将EEG分类为正常/异常/癫痫样，和光阵发性反应.定量生物标志物是应用于IED的统计学，高频活动，频带功率，电流源密度估计，EEG通道之间的成对统计相互依存，和复杂性的度量。使用EEG特征的预后模型是Cox比例风险模型和机器学习模型。有希望的是，一些定量脑电图生物标志物可用于评估ASM疗效，但需要进一步的研究。没有足够的证据得出结论，任何特定的生物标志物都可以用于特定的人群或背景。预测ASM疗效。
结论：我们确定了一组潜在的预后脑电图生物标志物，可以与预后模型相结合来评估ASM疗效。然而,需要解决许多混杂因素，以便转化为临床实践。
方法：在2021年1月至2022年10月之间，四个数据库，
结果：确定了875项研究，
结论：我们确定了一组潜在的预后脑电图生物标志物，
