Willy Taillard Laboratory of Kinesiology, Geneva University Hospitals and Geneva University, Switzerland.
Res Dev Disabil. 2013 Sep;34(9):2684-93. doi: 10.1016/j.ridd.2013.05.002. Epub 2013 Jun 12.
Great importance has been placed on the development of gait classification in cerebral palsy (CP) to assist clinicians. Nevertheless, gait classification is challenging within this group because the data is characterized by a high-dimensionality and a high-variability. Thus, the aim of this study was to analyze without a priori, a database of clinical gait analysis (CGA) of CP patients, using multiple correspondence analysis (MCA). A retrospective search, including biomechanical and clinical parameters was done between 2006 and 2012. One hundred and twenty two CP patients were included in this study (51 females and 71 males, mean age ± SD: 14.2 ± 7.5 years). Sixteen biomechanical spatio-temporal and kinematic parameters were included in the analysis. This data was transformed by a fuzzy window coding based on the distribution of each parameter in three modalities: low, average and high. Afterward, a MCA was used to associate parameters and to define classes. From this, seven most explicative gait parameters used to characterize gait of CP patients were identified: maximal hip extension, hip range, knee range, maximal knee flexion at initial contact, time of peak knee flexion, and maximal ankle dorsiflexion in stance phase and in swing phase. Moreover, four main profiles of CP patients have been defined from the multivariate approach: an apparent equinus gait group (the most similar of the control group with diplegic and hemiplegic patients with a GMFCS 1), a true equinus gait group (the youngest group with diplegic and some hemiplegic patients with a GMFCS 1), a crouch gait group (the oldest group with a majority of diplegic and rare hemiplegic patients with a GMFCS 2) and a jump knee gait group (the greatest level of global spasticity of the lower limbs with a majority of diplegic and rare hemiplegic patients with a GMFCS 2). Thus, this study showed the feasibility of the MCA in order to characterize and classify a large database of CP patients.
人们高度重视脑瘫（CP）患者步态分类的研究，以便为临床医生提供帮助。然而，CP 患者的步态分类具有一定的挑战性，因为数据具有高维度和高度可变性。因此，本研究旨在使用多元对应分析（MCA）对 CP 患者的临床步态分析（CGA）数据库进行无先验分析。2006 年至 2012 年进行了回顾性检索，包括生物力学和临床参数。本研究共纳入 122 例 CP 患者（女性 51 例，男性 71 例，平均年龄±标准差：14.2±7.5 岁）。分析中包括 16 个生物力学时空和运动学参数。这些数据通过基于每个参数在三个模态（低、中、高）中的分布的模糊窗口编码进行转换。之后，使用 MCA 关联参数并定义类别。由此，确定了七个最能解释 CP 患者步态的参数：最大髋关节伸展、髋关节活动度、膝关节活动度、初始接触时最大膝关节屈曲、膝关节屈曲峰值时间和站立相和摆动相的最大踝关节背屈。此外，通过多变量方法定义了 CP 患者的四个主要类型：明显马蹄内翻足组（与痉挛型双瘫和偏瘫患者最相似，GMFCS 1 级）、真性马蹄内翻足组（最年轻的组，GMFCS 1 级，痉挛型双瘫和一些偏瘫患者）、膝过伸组（最年长的组，GMFCS 2 级，多数为痉挛型双瘫，少数为偏瘫患者）和膝跳组（下肢整体痉挛程度最高，GMFCS 2 级，多数为痉挛型双瘫，少数为偏瘫患者）。因此，本研究表明 MCA 用于描述和分类 CP 患者大型数据库的可行性。
