The Value of MRI in Diagnosing and Classifying Acute Traumatic Multiple Ligament Knee Injuries [post]

Xusheng Li, Qian Hou, Xuehua Zhan, Long Chang, Xiaobing Ma, Haifeng Yuan
2021 unpublished
Background Magnetic resonance imaging (MRI) is a widely used examination for knee injuries, however, the accuracy of MRI in classifying multiple ligament knee injuries (MLKIs) has not been reported. The purpose of this study was to investigate the value of MRI in diagnosing and classifying acute traumatic MLKIs.Methods The clinical data of 97 patients who were diagnosed with acute traumatic MLKIs and managed by multi-ligament reconstruction were retrospectively reviewed. Intraoperative findings
more » ... were considered as the standard pattern of injured structures. The value of MRI in detecting injuries of ligaments and meniscus was evaluated by calculating the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and kappa coefficients analysis. The value of MRI in classifying MLKIs was evaluated by calculating the agreement between MRI and intraoperative findings.Results For detecting the specific injured structures in MLKIs, MRI had high sensitivity and moderate specificity in detecting cruciate ligament injuries, moderate sensitivity and specificity in detecting collateral ligament injuries, fair sensitivity and low specificity in the diagnosis of injuries to the meniscus. For classifying the MIKIs, MRI had a moderate agreement with intraoperative findings in classifying KD-Ⅴ (kappa value=0.57), poor agreement in the KD-Ⅰ (kappa value=0.39) and KD-ⅢM (kappa value=0.31), meaningless in the KD-Ⅱ and KD-ⅢL (kappa value <0). The overall agreement in classifying MLKIs was poor (kappa value =0.23). Conclusions MRI can be used for the early detection of MLKIs, however, the value of MRI in classifying MLKIs is limited, management of MLKIs should be based on intraoperative findings.
doi:10.21203/rs.3.rs-555848/v1 fatcat:qq53xvv7f5clnce2z3jjnkim2i