ArticleJournal of functional biomaterials2022
A Comparison of Wear Patterns on Retrieved and Simulator-Tested Total Knee Replacements.
Article in Journal of functional biomaterials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Gait-to-Contact (G2C): A Novel Deep Learning Framework to Predict Total Knee Replacement Wear from Gait Patterns.Annals of biomedical engineering · 2026Article
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Authors and funding
3 authors.
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Abstract
Aseptic implant loosening is the most common reason for revision surgery after total knee replacement. This is associated with adverse biological reactions to wear debris from the articulating implant components. To predict the amount of wear debris generated in situ, standard wear testing of total knee replacement (TKR) is carried out before its clinical use. However, wear data reported on retrievals of total knee replacement (TKR) revealed significant discrepancies compared with standard wear simulator studies. Therefore, the aim of the present study was to compare the wear patterns on identical posterior-cruciate-retaining TKR designs by analyzing retrieved and experimentally tested implants. The identification and classification of wear patterns were performed using 21 retrieved ultra-high-molecular-weight-polyethylene (UHMW-PE) inserts and four sets of inserts of identical design and material tested in a knee wear simulator. These four sets had undergone different worst-case conditions and a standard test in a wear simulator according to ISO 14243-1. Macroscopic and microscopic examinations of the polyethylene inserts were performed, including the determination of seven modes of wear that correspond to specific wear patterns, the calculation of wear areas, and the classification of the damage over the whole articulating area. Retrieved and standard wear simulator-tested UHMW-PE inserts showed significant differences in wear area and patterns. The total wear areas and the damage score were significantly larger on the retrievals (52.3% versus 23.9%, 32.7 versus 22.7). Furthermore, the range of wear patterns found on the retrievals was not reproducible in the simulator-tested inserts. However, good correspondence was found with the simulator-tested polyethylene inserts under worst-case conditions (third body wear), i.e., deep wear areas could be replicated according to the in vivo situation compared with other wear test scenarios. Based on the findings presented here, standard simulator testing can be used to directly compare different TKR designs but is limited in the prediction of their in situ wear. Preclinical wear testing may be adjusted by worst-case conditions to improve the prediction of in situ performance of total knee implants in the future.
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