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Injury risk functions for the four primary knee ligaments

The purpose of this study was to develop injury risk functions (IRFs) for the anterior and posterior cruciate ligaments (ACL and PCL, respectively) and the medial and lateral collateral ligaments (MCL and LCL, respectively) in the knee joint. The IRFs were based on post-mortem human subjects (PMHSs)...

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Autores principales: Nusia, Jiota, Xu, Jia-Cheng, Knälmann, Johan, Sjöblom, Reimert, Kleiven, Svein
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582698/
https://www.ncbi.nlm.nih.gov/pubmed/37860626
http://dx.doi.org/10.3389/fbioe.2023.1228922
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author Nusia, Jiota
Xu, Jia-Cheng
Knälmann, Johan
Sjöblom, Reimert
Kleiven, Svein
author_facet Nusia, Jiota
Xu, Jia-Cheng
Knälmann, Johan
Sjöblom, Reimert
Kleiven, Svein
author_sort Nusia, Jiota
collection PubMed
description The purpose of this study was to develop injury risk functions (IRFs) for the anterior and posterior cruciate ligaments (ACL and PCL, respectively) and the medial and lateral collateral ligaments (MCL and LCL, respectively) in the knee joint. The IRFs were based on post-mortem human subjects (PMHSs). Available specimen-specific failure strains were supplemented with statistically generated failure strains (virtual values) to accommodate for unprovided detailed experimental data in the literature. The virtual values were derived from the reported mean and standard deviation in the experimental studies. All virtual and specimen-specific values were thereafter categorized into groups of static and dynamic rates, respectively, and tested for the best fitting theoretical distribution to derive a ligament-specific IRF. A total of 10 IRFs were derived (three for ACL, two for PCL, two for MCL, and three for LCL). ACL, MCL, and LCL received IRFs in both dynamic and static tensile rates, while a sufficient dataset was achieved only for dynamic rates of the PCL. The log-logistic and Weibull distributions had the best fit (p-values: >0.9, RMSE: 2.3%–4.7%) to the empirical datasets for all the ligaments. These IRFs are, to the best of the authors’ knowledge, the first attempt to generate injury prediction tools based on PMHS data for the four knee ligaments. The study has summarized all the relevant literature on PHMS experimental tensile tests on the knee ligaments and utilized the available empirical data to create the IRFs. Future improvements require upcoming experiments to provide comparable testing and strain measurements. Furthermore, emphasis on a clear definition of failure and transparent reporting of each specimen-specific result is necessary.
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spelling pubmed-105826982023-10-19 Injury risk functions for the four primary knee ligaments Nusia, Jiota Xu, Jia-Cheng Knälmann, Johan Sjöblom, Reimert Kleiven, Svein Front Bioeng Biotechnol Bioengineering and Biotechnology The purpose of this study was to develop injury risk functions (IRFs) for the anterior and posterior cruciate ligaments (ACL and PCL, respectively) and the medial and lateral collateral ligaments (MCL and LCL, respectively) in the knee joint. The IRFs were based on post-mortem human subjects (PMHSs). Available specimen-specific failure strains were supplemented with statistically generated failure strains (virtual values) to accommodate for unprovided detailed experimental data in the literature. The virtual values were derived from the reported mean and standard deviation in the experimental studies. All virtual and specimen-specific values were thereafter categorized into groups of static and dynamic rates, respectively, and tested for the best fitting theoretical distribution to derive a ligament-specific IRF. A total of 10 IRFs were derived (three for ACL, two for PCL, two for MCL, and three for LCL). ACL, MCL, and LCL received IRFs in both dynamic and static tensile rates, while a sufficient dataset was achieved only for dynamic rates of the PCL. The log-logistic and Weibull distributions had the best fit (p-values: >0.9, RMSE: 2.3%–4.7%) to the empirical datasets for all the ligaments. These IRFs are, to the best of the authors’ knowledge, the first attempt to generate injury prediction tools based on PMHS data for the four knee ligaments. The study has summarized all the relevant literature on PHMS experimental tensile tests on the knee ligaments and utilized the available empirical data to create the IRFs. Future improvements require upcoming experiments to provide comparable testing and strain measurements. Furthermore, emphasis on a clear definition of failure and transparent reporting of each specimen-specific result is necessary. Frontiers Media S.A. 2023-10-04 /pmc/articles/PMC10582698/ /pubmed/37860626 http://dx.doi.org/10.3389/fbioe.2023.1228922 Text en Copyright © 2023 Nusia, Xu, Knälmann, Sjöblom and Kleiven. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Nusia, Jiota
Xu, Jia-Cheng
Knälmann, Johan
Sjöblom, Reimert
Kleiven, Svein
Injury risk functions for the four primary knee ligaments
title Injury risk functions for the four primary knee ligaments
title_full Injury risk functions for the four primary knee ligaments
title_fullStr Injury risk functions for the four primary knee ligaments
title_full_unstemmed Injury risk functions for the four primary knee ligaments
title_short Injury risk functions for the four primary knee ligaments
title_sort injury risk functions for the four primary knee ligaments
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582698/
https://www.ncbi.nlm.nih.gov/pubmed/37860626
http://dx.doi.org/10.3389/fbioe.2023.1228922
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