Schubert Kai Michael, Schmick Anton, Stattmann Miranda, Galovic Marian
Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.
Clin Neurophysiol Pract. 2025 Mar 4;10:116-128. doi: 10.1016/j.cnp.2025.02.008. eCollection 2025.
Epilepsy is a frequent consequence of acute brain injuries, such as stroke, brain tumors, and traumatic brain injury (TBI). Accurate prediction of epilepsy is essential for early intervention and improved patient outcomes. This review evaluates the best-established prognostic models, including the SeLECT and CAVE scores, which estimate the risk of developing seizures and epilepsy following these injuries. The review highlights their clinical applicability, predictive accuracy, and limitations for different etiologies. In addition to providing practical tables for risk estimation, we also offer user-friendly online calculators for these models at www.predictepilepsy.com to facilitate clinical implementation. These tools help identify high-risk patients and support decision-making for follow-up and treatment. Furthermore, we discuss the potential of integrating electrophysiological data, including EEG biomarkers, to further enhance prediction accuracy and patient care. These insights highlight the need for further refinement and validation of predictive models, enabling more personalized treatment strategies and better patient care.
癫痫是急性脑损伤（如中风、脑肿瘤和创伤性脑损伤（TBI））的常见后果。准确预测癫痫对于早期干预和改善患者预后至关重要。本综述评估了已确立的最佳预后模型，包括SeLECT和CAVE评分，这些模型可估计这些损伤后发生癫痫发作和癫痫的风险。该综述强调了它们在不同病因中的临床适用性、预测准确性和局限性。除了提供风险估计的实用表格外，我们还在www.predictepilepsy.com为这些模型提供用户友好的在线计算器，以促进临床应用。这些工具有助于识别高危患者，并支持随访和治疗的决策。此外，我们讨论了整合包括脑电图生物标志物在内的电生理数据以进一步提高预测准确性和患者护理水平的潜力。这些见解凸显了对预测模型进行进一步完善和验证的必要性，从而实现更个性化的治疗策略和更好的患者护理。
