Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Julius Wolff Institute, Berlin, Germany.
Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany.
J Biomech. 2022 Apr;135:111037. doi: 10.1016/j.jbiomech.2022.111037. Epub 2022 Mar 14.
Younger patients increasingly receive total hip arthroplasty (THA) as therapy for end-stage osteoarthritis. To maintain the long-term success of THA in such patients, avoiding extremely high hip loads, i.e., in vivo hip contact force (HCF), is considered essential. However, in vivo HCFs are difficult to determine and their direct measurement is limited to instrumented joint implants. It remains unclear whether external measurements of ground reaction forces (GRFs), a non-invasive, markerless and clinic-friendly measure can estimate in vivo HCFs. Using data from eight patients with instrumented hip implants, this study determined whether GRF time series data, alone or combined with other scalar variables such as hip joint moments (HJMs) and lean muscle volume (LMV), could predict the resultant HCF (rHCF) impulse using a functional linear modeling approach. Overall, single GRF time series data did not predict in vivo rHCF impulses. However, when GRF time series data were combined with LMV of the gluteus medius or sagittal HJM using a functional linear modeling approach, the in vivo rHCF impulse could be predicted from external measures only. Accordingly, this approach can predict in vivo rHCF impulses, and thus provide patients with useful insight regarding their gait behavior to avoid hip joint overloading.
越来越多的年轻患者接受全髋关节置换术（THA）作为终末期骨关节炎的治疗方法。为了保持此类患者 THA 的长期成功，避免极高的髋关节负荷（即体内髋关节接触力（HCF））被认为是至关重要的。然而，体内 HCF 难以确定，其直接测量仅限于仪器化关节植入物。目前尚不清楚外部测量地面反作用力（GRF）——一种非侵入性、无标记且对诊所友好的测量方法——是否可以估计体内 HCF。本研究使用来自 8 名植入仪器髋关节的患者的数据，使用功能线性建模方法确定单独或与其他标量变量（如髋关节力矩（HJM）和瘦肌肉体积（LMV））组合的 GRF 时间序列数据是否可以预测结果 HCF（rHCF）冲量。总体而言，单一 GRF 时间序列数据并不能预测体内 rHCF 冲量。然而，当使用功能线性建模方法将 GRF 时间序列数据与臀中肌的 LMV 或矢状面 HJM 结合时，仅通过外部测量即可预测体内 rHCF 冲量。因此，该方法可以预测体内 rHCF 冲量，从而为患者提供有关其步态行为的有用见解，以避免髋关节过度负荷。
