Melanda Alessandro G, Davids Jon R, Pauleto Ana Carolina, Pelegrinelli Alexandre R M, Ferreira Alana Elisabeth Kuntze, Knaut Luiz Alberto, Lucareli Paulo Roberto G, Smaili Suhaila Mahmoud
Department of Surgery, State University of Londrina, Paraná, Brazil; Master's and Doctoral degree program in Rehabilitation Sciences - State University of Londrina, Paraná, Brazil; Gait Analysis Laboratory, Ana Carolina Moura Xavier Hospital Rehabilitation Center, Curitiba, Brazil.
Shriners Hospitals for Children, Northern California, 2425 Stockton Blvd, Sacramento, CA 95817, USA.
Gait Posture. 2022 Oct;98:355-361. doi: 10.1016/j.gaitpost.2022.09.083. Epub 2022 Sep 23.
Gait classification systems (GCS) may enable clinicians to differentiate gait patterns into clinically significant categories that assist in clinical decision-making and assessment of outcomes. Davids and Bagley in 2014 [1] described a GCS for children with cerebral palsy (GCS-CP). The purpose of our study was to use the GCS-CP for the first time on a sample of patients with CP and to evaluate the reliability and utility of the classification system.
The gait of 131 children with CP was retrospectively reviewed and classified according to Davids and Bagley's classification using two-dimensional (2D) video and three-dimensional (3D) lower limb kinematics and kinetics. Gross Motor Function Classification System (GMFCS) levels were determined, and the Gait Profile Scores (GPS) calculated to characterize the sample concerning gait classification. The comparison between the groups was performed using the Kruskal-Wallis test with respect to the non-normal distribution of the data. The intrarater and interrater reliability was determined using the Kappa index (k) statistics with 95% CI.
All GCS-CP groups were represented within the evaluated sample. Of the 131 cases evaluated, 127 (96.95%) were able to be classified with respect to sagittal plane stance phase gait deviations. All patients in the sample were able to be classified with respect to sagittal plane swing phase and transverse plane gait deviations. The interrater reliability was 0.596 and 0.485 for the first and second levels of the classification, respectively, according to the Fleiss's Kappa statistics. Intrarater reliability was 0.776 and 0.714 for the raters one and two, respectively, according to the Cohen's Kappa statistics.
The GCS-CP exhibited clinical utility, successfully classifying almost all subjects with CP in two planes, based upon kinematic and kinetic data. The classification is valid and has moderate interrater and moderate to substantial intrarater reliability.
步态分类系统(GCS)可使临床医生将步态模式区分为具有临床意义的类别,这有助于临床决策和结果评估。2014年,戴维兹和巴格利[1]描述了一种针对脑瘫患儿的步态分类系统(GCS-CP)。我们研究的目的是首次将GCS-CP应用于脑瘫患者样本,并评估该分类系统的可靠性和实用性。
回顾性分析131例脑瘫患儿的步态,并根据戴维兹和巴格利的分类方法,利用二维(2D)视频以及三维(3D)下肢运动学和动力学进行分类。确定粗大运动功能分类系统(GMFCS)水平,并计算步态轮廓评分(GPS)以描述样本的步态分类情况。由于数据呈非正态分布,故采用Kruskal-Wallis检验对组间进行比较。使用Kappa指数(k)统计量及95%置信区间确定评分者内和评分者间的可靠性。
在评估样本中涵盖了所有GCS-CP组。在评估的131例病例中,127例(96.95%)能够根据矢状面站立期步态偏差进行分类。样本中的所有患者均能够根据矢状面摆动期和横断面步态偏差进行分类。根据Fleiss's Kappa统计量,分类的第一级和第二级评分者间可靠性分别为0.596和0.485。根据Cohen's Kappa统计量,评分者一和评分者二的评分者内可靠性分别为0.776和0.714。
GCS-CP显示出临床实用性,基于运动学和动力学数据,成功地在两个平面上对几乎所有脑瘫受试者进行了分类。该分类有效,具有中等的评分者间可靠性以及中等至较高的评分者内可靠性。