Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies.
Quantitative analysis of phenotypic elements augments traditional electroclinical classification of common familial epilepsies.
复制标题
表型元素的定量分析增强了常见家族性癫痫的传统电临床分类。
DOI：
10.1111/epi.16354
复制
发表时间：
2019
期刊：
影响因子：
5.6
通讯作者：
Epi4KConsortium
中科院分区：
文献类型：
--
作者：
Epi4KConsortium
癫痫的类型和亚型的分类对于临床护理和研究潜在的疾病机制都很重要。一个定量的，数据驱动的方法可能会增加传统的电临床分类，并阐明了新的光对现有的分类frameworks.MethodsWe使用潜在的类分析，一种统计方法，根据表型元素将受试者分配到称为潜在类的组，从Epi4K多重家族研究中对具有常见家族性癫痫的个体进行分类。表型要素包括癫痫发作类型、癫痫发作症状和病史的其他要素。我们比较类分配到传统的电临床分类和评估的潜伏classs.ResultsA共1120例癫痫患者被分配到五个潜伏类的家族聚集。第1类和第2类包含全身性癫痫受试者，主要反映了失神性癫痫和年轻发作（第1类）与肌阵挛性癫痫和老年发作（第2类）之间的区别。第3类和第4类包含局灶性癫痫受试者，与第1类和第2类相反，这些受试者与临床定义的局灶性癫痫亚型不太接近。第5类包括几乎所有伴热性惊厥或癫痫类型未知的受试者，以及少数全身性癫痫和少数局灶性癫痫受试者。潜在类别的家族一致性类似于或大于临床定义的癫痫类型的一致性。重要性癫痫的定量分类具有通过以下方式增强传统电临床分类的潜力：（1）将某些综合征组合成单个类别，（2）将某些综合征分裂成不同类别，（3）帮助对临床上不能分类的受试者进行分类，以及（4）定义临床定义分类的边界。这种方法可以指导未来的研究，包括分子遗传学研究，通过识别可能共享潜在疾病机制的同质个体集。
ObjectiveClassification of epilepsy into types and subtypes is important for both clinical care and research into underlying disease mechanisms. A quantitative, data‐driven approach may augment traditional electroclinical classification and shed new light on existing classification frameworks.MethodsWe used latent class analysis, a statistical method that assigns subjects into groups called latent classes based on phenotypic elements, to classify individuals with common familial epilepsies from the Epi4K Multiplex Families study. Phenotypic elements included seizure types, seizure symptoms, and other elements of the medical history. We compared class assignments to traditional electroclinical classifications and assessed familial aggregation of latent classes.ResultsA total of 1120 subjects with epilepsy were assigned to five latent classes. Classes 1 and 2 contained subjects with generalized epilepsy, largely reflecting the distinction between absence epilepsies and younger onset (class 1) versus myoclonic epilepsies and older onset (class 2). Classes 3 and 4 contained subjects with focal epilepsies, and in contrast to classes 1 and 2, these did not adhere as closely to clinically defined focal epilepsy subtypes. Class 5 contained nearly all subjects with febrile seizures plus or unknown epilepsy type, as well as a few subjects with generalized epilepsy and a few with focal epilepsy. Family concordance of latent classes was similar to or greater than concordance of clinically defined epilepsy types.SignificanceQuantitative classification of epilepsy has the potential to augment traditional electroclinical classification by (1) combining some syndromes into a single class, (2) splitting some syndromes into different classes, (3) helping to classify subjects who could not be classified clinically, and (4) defining the boundaries of clinically defined classifications. This approach can guide future research, including molecular genetic studies, by identifying homogeneous sets of individuals that may share underlying disease mechanisms.