Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Department of Neurology, Dayanand Medical College, Ludhiana, India; NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0RJ, UK.
J Neurol Sci. 2021 Aug 15;427:117515. doi: 10.1016/j.jns.2021.117515. Epub 2021 May 29.
The classification of epilepsy is essential for people with epilepsy and their families, healthcare providers, physicians and researchers. The International League Against Epilepsy proposed updated seizure and epilepsy classifications in 2017, while another four-dimensional epilepsy classification was updated in 2019. An Integrated Epilepsy Classification system was proposed in 2020. Existing classifications, however, lack consideration of important pragmatic factors relevant to the day-to-day life of people with epilepsy and stakeholders. Despite promising developments, consideration of comorbidities in brain development, genetic causes, and environmental triggers of epilepsy remains largely user-dependent in existing classifications. Demographics of epilepsy have changed over time, while existing classification schemes exhibit caveats. A pragmatic classification scheme should incorporate these factors to provide a nuanced classification. Validation across disparate contexts will ensure widespread applicability and ease of use. A team-based approach may simplify communication between healthcare personnel, while an individual-centred perspective may empower people with epilepsy. Together, incorporating these elements into a modern but pragmatic classification scheme may ensure optimal care for people with epilepsy by emphasising cohesiveness among its myriad users. Technological advancements such as 7T MRI, next-generation sequencing, and artificial intelligence may affect future classification efforts.
癫痫的分类对于癫痫患者及其家属、医疗保健提供者、医生和研究人员来说至关重要。国际抗癫痫联盟在 2017 年提出了更新的癫痫发作和癫痫分类,而 2019 年又更新了另一种四维癫痫分类。2020 年提出了综合癫痫分类系统。然而,现有的分类法缺乏对与癫痫患者和利益相关者日常生活相关的重要实用因素的考虑。尽管有了有希望的发展,但现有的分类法在很大程度上仍然依赖于用户,而没有考虑到脑发育、遗传原因和癫痫的环境触发因素中的合并症。癫痫的人口统计学特征随着时间的推移而发生了变化,而现有的分类方案也存在一些缺陷。实用的分类方案应该纳入这些因素,提供细致的分类。在不同背景下进行验证将确保广泛的适用性和易用性。基于团队的方法可以简化医疗保健人员之间的沟通,而以个人为中心的观点可以赋予癫痫患者权力。通过将这些元素纳入一个现代但实用的分类方案中,可以通过强调其众多用户之间的凝聚力,为癫痫患者提供最佳护理。7T MRI、下一代测序和人工智能等技术进步可能会影响未来的分类工作。