Institute for Biomechanics, ETH Zurich, Zurich, Switzerland.
Institute for Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK; NIHR Leeds Biomedical Research Centre, Leeds, UK.
Osteoarthritis Cartilage. 2019 Jun;27(6):895-905. doi: 10.1016/j.joca.2019.01.016. Epub 2019 Feb 15.
To examine hip contact force (HCF), calculated through multibody modelling, in a large total hip replacement (THR) cohort stratified by patient characteristics such as body mass index (BMI), age and function.
132 THR patients undertook one motion capture session of gait analysis at a self-selected walking speed. HCFs were then calculated using the AnyBody Modelling System. Patients were stratified into three BMI groups, five age groups, and finally three functional groups determined by their self-selected gait speed. By means of statistical parametric mapping (SPM), statistical analyses of the 1-dimensional time series were performed to separately evaluate the influence of age, BMI and functionality on HCF.
The mean predicted HCFs were comparable to HCFs measured with instrumented prostheses reported in the literature. The SPM analysis revealed a statistically significant positive linear correlation between BMI and HCF, indicating that obese patients are more likely to experience higher HCF during most of the stance phase, while a statistically significant negative correlation with age was found only during the late swing-phase. Patients with higher functional ability exhibited significantly increased peak HCF, while patients with lower functional ability demonstrated lower HCFs overall and a pathological flattening of the typical double hump force profile.
HCFs experienced at the bearing surface are highly dependent on patient characteristics. BMI and functional ability were determined to have the biggest influence on contact forces. Current preclinical testing standards do not reflect this.
通过多体建模来研究髋关节接触力（HCF），并对患者的身体质量指数（BMI）、年龄和功能等特征进行分层，分析其对 HCF 的影响。
132 例 THR 患者以自身选择的步行速度进行步态分析的单次运动捕捉测试。然后使用 AnyBody 建模系统计算 HCF。患者被分为三个 BMI 组、五个年龄组，最后根据自身选择的步行速度分为三个功能组。通过统计参数映射（SPM）对 1 维时间序列进行统计分析，分别评估年龄、BMI 和功能对 HCF 的影响。
预测的平均 HCF 与文献中报道的带有仪器假体的 HCF 测量值相当。SPM 分析显示 BMI 与 HCF 之间存在显著的正线性相关，这表明肥胖患者在大部分站立阶段更有可能经历更高的 HCF，而与年龄的显著负相关仅在晚期摆动阶段才发现。功能能力较高的患者表现出明显更高的峰值 HCF，而功能能力较低的患者整体 HCF 较低，典型的双峰力曲线呈现病理性变平。
承载表面上的 HCF 高度依赖于患者的特征。BMI 和功能能力被确定为对接触力的最大影响因素。当前的临床前测试标准并未反映这一点。
