PDF(Pubmed)
Abstract：
BACKGROUND: Characterizing the condition of patients suffering from knee osteoarthritis is complex due to multiple associations between clinical, functional, and structural parameters. While significant variability exists within this population, especially in candidates for total knee arthroplasty, there is increasing interest in knee kinematics among orthopedic surgeons aiming for more personalized approaches to achieve better outcomes and satisfaction. The primary objective of this study was to identify distinct kinematic phenotypes in total knee arthroplasty candidates and to compare different methods for the identification of these phenotypes.
METHODS: Three-dimensional kinematic data obtained from a Knee Kinesiography exam during treadmill walking in the clinic were used. Various aspects of the clustering process were evaluated and compared to achieve optimal clustering, including data preparation, transformation, and representation methods.
RESULTS: A K-Means clustering algorithm, performed using Euclidean distance, combined with principal component analysis applied on data transformed by standardization, was the optimal approach. Two unique kinematic phenotypes were identified among 80 total knee arthroplasty candidates. The two distinct phenotypes divided patients who significantly differed both in terms of knee kinematic representation and clinical outcomes, including a notable variation in 63.3% of frontal plane features and 81.8% of transverse plane features across 77.33% of the gait cycle, as well as differences in the Pain Catastrophizing Scale, highlighting the impact of these kinematic variations on patient pain and function.
CONCLUSIONS: Results from this study provide valuable insights for clinicians to develop personalized treatment approaches based on patients\' phenotype affiliation, ultimately helping to improve total knee arthroplasty outcomes.
METHODS: Three-dimensional kinematic data obtained from a Knee Kinesiography exam during treadmill walking in the clinic were used. Various aspects of the clustering process were evaluated and compared to achieve optimal clustering, including data preparation, transformation, and representation methods.
RESULTS: A K-Means clustering algorithm, performed using Euclidean distance, combined with principal component analysis applied on data transformed by standardization, was the optimal approach. Two unique kinematic phenotypes were identified among 80 total knee arthroplasty candidates. The two distinct phenotypes divided patients who significantly differed both in terms of knee kinematic representation and clinical outcomes, including a notable variation in 63.3% of frontal plane features and 81.8% of transverse plane features across 77.33% of the gait cycle, as well as differences in the Pain Catastrophizing Scale, highlighting the impact of these kinematic variations on patient pain and function.
CONCLUSIONS: Results from this study provide valuable insights for clinicians to develop personalized treatment approaches based on patients\' phenotype affiliation, ultimately helping to improve total knee arthroplasty outcomes.
摘要：
背景：由于临床，功能，和结构参数。虽然这个群体存在显著的变异性，特别是在全膝关节置换术的候选人中，矫形外科医师对膝关节运动学的兴趣日益增加，其目的是寻求更个性化的方法来获得更好的结果和满意度。这项研究的主要目的是鉴定全膝关节置换术候选人中不同的运动学表型，并比较鉴定这些表型的不同方法。
方法：使用从临床跑步机步行期间的膝关节运动成像检查获得的三维运动学数据。对聚类过程的各个方面进行了评估和比较，以实现最佳聚类，包括数据准备，改造,和表示方法。
结果：K-Means聚类算法，使用欧几里德距离执行，结合主成分分析应用于标准化转化的数据，是最佳方法。在80名全膝关节置换术候选人中鉴定出两种独特的运动学表型。两种不同的表型将在膝关节运动学表现和临床结果方面均存在显着差异的患者分开。在77.33%的步态周期中，包括63.3%的额叶平面特征和81.8%的横向平面特征的显著变化，以及疼痛突变量表的差异，强调这些运动学变化对患者疼痛和功能的影响。
结论：这项研究的结果为临床医生提供了有价值的见解，以开发基于患者表型的个性化治疗方法，最终有助于改善全膝关节置换术的结果。
方法：使用从临床跑步机步行期间的膝关节运动成像检查获得的三维运动学数据。对聚类过程的各个方面进行了评估和比较，以实现最佳聚类，包括数据准备，改造,和表示方法。
结果：K-Means聚类算法，使用欧几里德距离执行，结合主成分分析应用于标准化转化的数据，是最佳方法。在80名全膝关节置换术候选人中鉴定出两种独特的运动学表型。两种不同的表型将在膝关节运动学表现和临床结果方面均存在显着差异的患者分开。在77.33%的步态周期中，包括63.3%的额叶平面特征和81.8%的横向平面特征的显著变化，以及疼痛突变量表的差异，强调这些运动学变化对患者疼痛和功能的影响。
结论：这项研究的结果为临床医生提供了有价值的见解，以开发基于患者表型的个性化治疗方法，最终有助于改善全膝关节置换术的结果。
