School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Chestnut Street, Bossone 718, Philadelphia, PA, 19104, USA.
Division of Orthopaedics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Spine Deform. 2023 May;11(3):723-731. doi: 10.1007/s43390-022-00634-1. Epub 2023 Jan 26.
While the C-EOS system helps organize and classify Early Onset Scoliosis (EOS) pathology, it is not data-driven and does not help achieve consensus for surgical treatment. The current study aims to create an automated method to cluster EOS patients based on pre-operative clinical indices.
A total of 1114 EOS patients were used for the study, with the following distribution by etiology: congenital (240), idiopathic (217), neuromuscular (417), syndromic (240). Pre-operative clinical indices used for clustering were age, major curve (Cobb) angle, kyphosis, number of levels involved in a major curve (Cobb angle) and kyphosis along with deformity index (defined as the ratio of major Cobb angle and kyphosis). Fuzzy C-means clustering was performed for each etiology individually, with one-way ANOVA performed to assess statistical significance (p < 0.05).
The automated clustering method resulted in three clusters per etiology as the optimal number based on the highest average membership values. Statistical analyses showed that the clusters were significantly different for all the clinical indices within and between etiologies. Link to the ACT-EOS web application: https://biomed.drexel.edu/labs/obl/toolkits/act-eos-application .
An automated method to cluster EOS patients based on pre-operative clinical indices was developed identifying three unique, data-driven subgroups for each C-EOS etiology category. Adoption of such an automated clustering framework can help improve the standardization of clinical decision-making for EOS.
C-EOS 系统有助于组织和分类早发性脊柱侧凸（EOS）的病理，但它不是数据驱动的，也不能帮助达成 EOS 手术治疗的共识。本研究旨在创建一种基于术前临床指标自动聚类 EOS 患者的方法。
共纳入 1114 例 EOS 患者，病因分布如下：先天性（240 例）、特发性（217 例）、神经肌肉型（417 例）、综合征型（240 例）。用于聚类的术前临床指标包括年龄、主弯（Cobb）角、后凸、主弯（Cobb 角）和后凸涉及的节段数以及畸形指数（定义为主弯 Cobb 角与后凸之比）。对每种病因分别进行模糊 C 均值聚类，采用单因素方差分析评估统计学意义（p < 0.05）。
基于最高平均隶属度，自动聚类方法为每种病因产生了三个聚类，这是最佳聚类数量。统计学分析表明，聚类在每个病因内和病因之间的所有临床指标上均存在显著差异。EOS 患者的自动聚类方法基于术前临床指标进行，针对每种 C-EOS 病因类别识别出三个独特的、数据驱动的亚组。采用这种自动聚类框架可以帮助提高 EOS 临床决策的标准化。
