Dickinson Alexander S
J Biomech Eng. 2014 Apr;136(4). doi: 10.1115/1.4026256.
Periprosthetic bone remodeling is frequently observed after total hip replacement. Reduced bone density increases the implant and bone fracture risk, and a gross loss of bone density challenges fixation in subsequent revision surgery. Computational approaches allow bone remodeling to be predicted in agreement with the general clinical observations of proximal resorption and distal hypertrophy. However, these models do not reproduce other clinically observed bone density trends, including faster stabilizing mid-stem density losses, and loss-recovery trends around the distal stem. These may resemble trends in postoperative joint loading and activity, during recovery and rehabilitation, but the established remodeling prediction approach is often used with identical pre- and postoperative load and activity assumptions. Therefore, this study aimed to evaluate the influence of pre- to postoperative changes in activity and loading upon the predicted progression of remodeling. A strain-adaptive finite element model of a femur implanted with a cemented Charnley stem was generated, to predict 60 months of periprosthetic remodeling. A control set of model input data assumed identical pre- and postoperative loading and activity, and was compared to the results obtained from another set of inputs with three varying activity and load profiles. These represented activity changes during rehabilitation for weak, intermediate and strong recoveries, and pre- to postoperative joint force changes due to hip center translation and the use of walking aids. Predicted temporal bone density change trends were analyzed, and absolute bone density changes and the time to homeostasis were inspected, alongside virtual X-rays. The predicted periprosthetic bone density changes obtained using modified loading inputs demonstrated closer agreement with clinical measurements than the control. The modified inputs also predicted the clinically observed temporal density change trends, but still under-estimated density loss during the first three postoperative months. This suggests that other mechanobiological factors have an influence, including the repair of surgical micro-fractures, thermal damage and vascular interruption. This study demonstrates the importance of accounting for pre- to postoperative changes in joint loading and patient activity when predicting periprosthetic bone remodeling. The study's main weakness is the use of an individual patient model; computational expense is a limitation of all previously reported iterative remodeling analysis studies. However, this model showed sufficient computational efficiency for application in probabilistic analysis, and is an easily implemented modification of a well-established technique.
全髋关节置换术后常可见假体周围骨重塑。骨密度降低会增加植入物和骨折风险，而骨密度的严重丧失会给后续翻修手术中的固定带来挑战。计算方法能够预测骨重塑，且与近端吸收和远端肥大的一般临床观察结果相符。然而，这些模型无法再现其他临床观察到的骨密度趋势，包括中干密度损失更快趋于稳定以及远端干周围的损失 - 恢复趋势。这些趋势可能类似于恢复和康复期间术后关节负荷及活动的趋势，但既定的重塑预测方法通常在术前和术后负荷及活动假设相同的情况下使用。因此，本研究旨在评估术前至术后活动和负荷变化对预测的重塑进展的影响。生成了一个植入骨水泥型Charnley柄的股骨的应变自适应有限元模型，以预测60个月的假体周围重塑。一组对照模型输入数据假设术前和术后负荷及活动相同，并与另一组具有三种不同活动和负荷情况的输入结果进行比较。这三种情况分别代表了弱、中、强恢复康复期间的活动变化，以及由于髋关节中心平移和使用助行器导致的术前至术后关节力变化。分析了预测的颞骨密度变化趋势，并检查了绝对骨密度变化和达到稳态的时间，同时还观察了虚拟X线影像。使用修改后的负荷输入获得的预测假体周围骨密度变化与临床测量结果的一致性比对照组更高。修改后的输入还预测出了临床观察到的颞骨密度变化趋势，但在前三个月仍低估了密度损失。这表明其他机械生物学因素也有影响，包括手术微骨折的修复、热损伤和血管中断。本研究证明了在预测假体周围骨重塑时考虑术前至术后关节负荷和患者活动变化的重要性。该研究的主要不足在于使用了个体患者模型；计算成本是所有先前报道的迭代重塑分析研究的一个限制因素。然而，该模型在概率分析中的应用显示出了足够的计算效率，并且是对一种成熟技术的易于实施的改进。
