Prediction of chronicity of anterior cruciate ligament tear using MRI findings

Jong Pil Yoon, Jae Ho Yoo, Chong Bum Chang, Ju Sung, Ja Young Choi, Jae Hyuck Yi, Tae Kyun Kim

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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 timelimits 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.

Original languageEnglish
Pages (from-to)19-25
Number of pages7
JournalClinics in Orthopedic Surgery
Volume5
Issue number1
DOIs
StatePublished - Mar 2013

Keywords

  • Anterior cruciate ligament
  • Chronicity
  • Magnetic resonance imaging
  • Predictive model

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