Massachusetts General Hospital, Department of Neurology, Boston, MA 02114, USA; Graduate Program for Neuroscience, Boston University, Boston, MA, USA; Department of Mathematics and Statistics, and Center for Systems Neuroscience, Boston University, Boston, MA, USA.
Massachusetts General Hospital, Department of Neurology, Boston, MA 02114, USA; Johns Hopkins University School of Medicine, Baltimore, MD, USA; Harvard University, Cambridge, MA, USA.
Clin Neurophysiol. 2023 Jun;150:49-55. doi: 10.1016/j.clinph.2023.02.180. Epub 2023 Mar 21.
We evaluated whether interictal epileptiform discharge (IED) rate and morphological characteristics predict seizure risk.
We evaluated 10 features from automatically detectable IEDs in a stereotyped population with self-limited epilepsy with centrotemporal spikes (SeLECTS). We tested whether the average value or the most extreme values from each feature predicted future seizure risk in cross-sectional and longitudinal models.
10,748 individual centrotemporal IEDs were analyzed from 59 subjects at 81 timepoints. In cross-sectional models, increases in average spike height, spike duration, slow wave rising slope, slow wave falling slope, and the most extreme values of slow wave rising slope each improved prediction of an increased risk of a future seizure compared to a model with age alone (p < 0.05, each). In longitudinal model, spike rising height improved prediction of future seizure risk compared to a model with age alone (p = 0.04) CONCLUSIONS: Spike height improves prediction of future seizure risk in SeLECTS. Several other morphological features may also improve prediction and should be explored in larger studies.
Discovery of a relationship between novel IED features and seizure risk may improve clinical prognostication, visual and automated IED detection strategies, and provide insights into the underlying neuronal mechanisms that contribute to IED pathology.
我们评估了发作间期棘波放电（IED）率和形态特征是否可预测癫痫发作风险。
我们评估了具有中央颞区棘波的自限性癫痫（SeLECTS）定型人群中自动检测到的 IED 的 10 种特征。我们测试了每个特征的平均值或极值是否能在横断和纵向模型中预测未来的癫痫发作风险。
在 81 个时间点对 59 名受试者的 10748 个中央颞区 IED 进行了分析。在横断模型中，与仅使用年龄的模型相比，平均棘波高度、棘波持续时间、慢波上升斜率、慢波下降斜率以及慢波上升斜率的极值增加均提高了未来癫痫发作风险增加的预测效果（p < 0.05，每项）。在纵向模型中，与仅使用年龄的模型相比，棘波上升高度提高了对未来癫痫发作风险的预测（p = 0.04）。
在 SeLECTS 中，棘波高度可提高对未来癫痫发作风险的预测。其他几种形态特征也可能提高预测效果，应在更大的研究中进行探索。
发现新型 IED 特征与癫痫发作风险之间的关系，可能会改善临床预后判断、视觉和自动 IED 检测策略，并深入了解导致 IED 病理的潜在神经元机制。
