Full-fieldin vitromeasurements andin silicopredictions of strain shielding in the implanted femur after total hip arthroplasty

Abstract

Alterations in bone strain as a result of implantation may contribute towards periprosthetic bone density changes after Total Hip Arthroplasty (THA). Computational models provide full-field strain predictions in implant-bone constructs; however, these predictions should be verified using experimental models wherever possible. In the present work, finite element (FE) predictions of surface strains in intact and implanted composite femurs were verified using digital image correlation (DIC). Relationships were sought between post implantation strain states across seven defined Gruen zones (GZ) and clinically observed longer term bone density changes. Computational predictions of strain distributions in intact and implanted femurs were compared to DIC measurements in two regions of interest. Regression analyses indicated strong linear correlation between measurements and predictions (R = 0.927 intact, 0.926 implanted) with low standard error (SE = 38µε intact, 26µε implanted). Pre-to postoperative changes in measured and predicted surface strains were found to relate qualitatively to clinicallyobserved volumetric bone density changes across seven Gruen zones: marked proximal bone density loss corresponded with a 50-64% drop in surface strain, and slight distal density changes corresponded with 4-14% strain increase. These results support the use of DIC as a pre-clinical tool for predicting post implantation strain shielding, indicative of long-term bone adaptations.

The influence of loading configurations on numerical evaluation of failure mechanisms in an uncemented femoral prosthesis

“…In general, the principal strains were found to be in agreement with published data. 8,10,11,[55][56][57] After implantation, higher strains at the distal part of the femur were observed (Figure 3). However, there was hardly any risk of cortical bone failure due to these elevated strains for all loading configurations.…”

“…8,20 The inference on meh size was based on a number of previous studies. 8,[21][22][23][24] Both the intact and implanted models were meshed with ICEM CFD 19.2 (ANSYS, Inc., Canonsburg, Pennsylvania) using 10-noded tetrahedral elements having edge length varying between 0.3 and 0.8 mm. A finer mesh size was used at the implant-bone interface for better computation of stress-strain distributions ( Figure 1).…”

“…4 In most of the FE studies, either a few number of patient activities or simplified musculoskeletal loading conditions were considered. [5][6][7][8] Up to eight static load cases have been used to represent the musculoskeletal hip-joint loading configuration of a gait cycle. [3][4][5][6][7][8] Although these simplifications would reduce the computational cost of the analysis significantly, some studies identified that neglecting certain muscles have considerable influence on internal stress-strain distribution in bone.…”

“…[5][6][7][8] Up to eight static load cases have been used to represent the musculoskeletal hip-joint loading configuration of a gait cycle. [3][4][5][6][7][8] Although these simplifications would reduce the computational cost of the analysis significantly, some studies identified that neglecting certain muscles have considerable influence on internal stress-strain distribution in bone. 4,[9][10][11] Hip joint loading and muscle forces are known to have a considerable influence on the primary stability of the hip implant, peri-prosthetic bone ingrowth and adaptive bone remodelling.…”

Efficacy and efficiency of multivariate linear regression for rapid prediction of femoral strain fields during activity

“…Quantifying femoral strain distribution is important for studying bone adaptation [1][2][3], diagnosing individuals most at risk of femoral fracture [4][5][6], and optimizing the biomechanical behaviour of implantable devices [7,8]. Over the last few decades, finiteelement analysis has been used extensively to quantify the entire femoral strain field [9][10][11], and there is growing interest in using this method to characterise strain distributions in multiple individuals [12,13] and across multiple trials and tasks [14].…”