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Prediction of Chronicity of Anterior Cruciate Ligament Tear Using MRI Findings
BACKGROUND: The estimation of anterior cruciate ligament (ACL) tear is required in certain cases involving legal and financial administration, such as the worker's compensation and/or insurance. The aim of this study is to propose and evaluate a quantitative evaluation instrument to estimate th...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Orthopaedic Association
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3582867/ https://www.ncbi.nlm.nih.gov/pubmed/23467216 http://dx.doi.org/10.4055/cios.2013.5.1.19 |
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author | Yoon, Jong Pil Yoo, Jae Ho Chang, Chong Bum Kim, Sung Ju Choi, Ja Young Yi, Jae Hyuck Kim, Tae Kyun |
author_facet | Yoon, Jong Pil Yoo, Jae Ho Chang, Chong Bum Kim, Sung Ju Choi, Ja Young Yi, Jae Hyuck Kim, Tae Kyun |
author_sort | Yoon, Jong Pil |
collection | PubMed |
description | BACKGROUND: The estimation of anterior cruciate ligament (ACL) tear is required in certain cases involving legal and financial administration, such as the worker's compensation and/or insurance. The aim of this study is to propose and evaluate a quantitative evaluation instrument to estimate the chronicity of the ACL tear, based on the four magnetic resonance imaging (MRI) findings. METHODS: One hundred and fifty one cases of complete ACL tear confirmed by arthroscopy were divided into 4 groups according to the time from ACL injury to MRI acquisition: acute (< 6 weeks), subacute (6 weeks to 3 months), intermediate (3 months to 1 year), and chronic (> 1 year). The four MRI findings including ACL morphology, joint effusion, posterior cruciate ligament angle, and bone bruise were analyzed for temporal changes among the 4 groups. Binary logistic regression equations were formulated using the MRI findings to estimate the chronicity of ACL tear in a quantitative manner, and the accuracy of the formulated regression equations was evaluated. RESULTS: The four MRI findings showed substantial temporal correlation with the time-limits of ACL injury to be included in the estimation model. Three predictive binary logistic equations estimated the probability of the ACL injury for the three cutoff time-limits of 6 weeks, 3 months, and 1 year with accuracies of 82.1%, 89.4%, and 89.4%, respectively. CONCLUSIONS: A series of predictive logistic equations were formulated to estimate the chronicity of ACL tear using 4 MRI findings with chronological significance. |
format | Online Article Text |
id | pubmed-3582867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | The Korean Orthopaedic Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-35828672013-03-06 Prediction of Chronicity of Anterior Cruciate Ligament Tear Using MRI Findings Yoon, Jong Pil Yoo, Jae Ho Chang, Chong Bum Kim, Sung Ju Choi, Ja Young Yi, Jae Hyuck Kim, Tae Kyun Clin Orthop Surg Original Article BACKGROUND: The estimation of anterior cruciate ligament (ACL) tear is required in certain cases involving legal and financial administration, such as the worker's compensation and/or insurance. The aim of this study is to propose and evaluate a quantitative evaluation instrument to estimate the chronicity of the ACL tear, based on the four magnetic resonance imaging (MRI) findings. METHODS: One hundred and fifty one cases of complete ACL tear confirmed by arthroscopy were divided into 4 groups according to the time from ACL injury to MRI acquisition: acute (< 6 weeks), subacute (6 weeks to 3 months), intermediate (3 months to 1 year), and chronic (> 1 year). The four MRI findings including ACL morphology, joint effusion, posterior cruciate ligament angle, and bone bruise were analyzed for temporal changes among the 4 groups. Binary logistic regression equations were formulated using the MRI findings to estimate the chronicity of ACL tear in a quantitative manner, and the accuracy of the formulated regression equations was evaluated. RESULTS: The four MRI findings showed substantial temporal correlation with the time-limits of ACL injury to be included in the estimation model. Three predictive binary logistic equations estimated the probability of the ACL injury for the three cutoff time-limits of 6 weeks, 3 months, and 1 year with accuracies of 82.1%, 89.4%, and 89.4%, respectively. CONCLUSIONS: A series of predictive logistic equations were formulated to estimate the chronicity of ACL tear using 4 MRI findings with chronological significance. The Korean Orthopaedic Association 2013-03 2013-02-20 /pmc/articles/PMC3582867/ /pubmed/23467216 http://dx.doi.org/10.4055/cios.2013.5.1.19 Text en Copyright © 2013 by The Korean Orthopaedic Association http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Yoon, Jong Pil Yoo, Jae Ho Chang, Chong Bum Kim, Sung Ju Choi, Ja Young Yi, Jae Hyuck Kim, Tae Kyun Prediction of Chronicity of Anterior Cruciate Ligament Tear Using MRI Findings |
title | Prediction of Chronicity of Anterior Cruciate Ligament Tear Using MRI Findings |
title_full | Prediction of Chronicity of Anterior Cruciate Ligament Tear Using MRI Findings |
title_fullStr | Prediction of Chronicity of Anterior Cruciate Ligament Tear Using MRI Findings |
title_full_unstemmed | Prediction of Chronicity of Anterior Cruciate Ligament Tear Using MRI Findings |
title_short | Prediction of Chronicity of Anterior Cruciate Ligament Tear Using MRI Findings |
title_sort | prediction of chronicity of anterior cruciate ligament tear using mri findings |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3582867/ https://www.ncbi.nlm.nih.gov/pubmed/23467216 http://dx.doi.org/10.4055/cios.2013.5.1.19 |
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