Sensitivity of total knee replacement wear to variability in motion and load input: A parametric finite element analysis study. Issue 7 (8th June 2020)

• Record Type: Journal Article
• Authors: Mell, Steven P.; Wimmer, Markus A.; Lundberg, Hannah J.
• Is Part Of: Journal of orthopaedic research. Volume 38:Issue 7(2020)
• Page Start: 1538
• Page End: 1549
• Publication Date: 2020-06-08
• Subjects: computational analysis -- implant wear -- kinematics and gait -- total knee replacements; Orthopedics -- Periodicals; Musculoskeletal system -- Periodicals; 616.7
• DOI: 10.1002/jor.24755
• Languages: English
• ISSNs: 0736-0266

Abstract

Abstract: Polyethylene wear remains a contributor to long term failure in total knee replacements (TKRs). Advances in materials have improved polyethylene wear rates, therefore further wear reductions require a better understanding of patient‐specific factors that lead to wear. Variability of gait within patients is considerable and could lead to significant variability in wear rates that cannot be predicted by standard testing methods. An in‐silico study was performed to investigate the influence of gait variability on TKR polyethylene wear. Nine characteristic peaks within the load and motion profiles used for TKR wear testing were varied 75% to 125% from baseline (ISO‐14243‐3:2014) to generate 310 unique waveforms. Wear was calculated for 1‐million cycles using a finite element TKR wear model. From the results, a surrogate model was developed using multiple linear regression, and used to predict how wear changes due to dispersion of motion and force peaks within a) ±5%, the maximum allowable input tolerance of ISO, and b) ±25%, more reflective of patient gait inter‐variability. The range of wear within the ±5% tolerance was 0.65 mm 3 /million cycles and was 3.24 mm 3 /million cycles within the ±25% variability more in line with the dispersion observed within patients. Although no one kinematic or kinetic peak dominated variability in TKR volumetric wear, variability within flexion/extension peaks were the largest contributor to wear rate variability. Interaction between the peaks of different waveforms was also important. This study, and future studies incorporating patient‐specific data, could help to explain the connection between patient‐specific gait factors and wear rates.