| description abstract | 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 midstem density losses, and lossrecovery 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 preand postoperative load and activity assumptions. Therefore, this study aimed to evaluate the influence of preto postoperative changes in activity and loading upon the predicted progression of remodeling. A strainadaptive 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 preand 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 preto 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 Xrays. 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 underestimated density loss during the first three postoperative months. This suggests that other mechanobiological factors have an influence, including the repair of surgical microfractures, thermal damage and vascular interruption. This study demonstrates the importance of accounting for preto 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 wellestablished technique. | |
