Can anterior knee laxity be used as a risk predictor for anterior cruciate ligament injury?
Hsiu-Chen Lin, PhD1,*; Chia-Ming Chang, MS2; Weng-Hang Lai, MS3; Horng-Chaung Hsu, MD4
1
Department of Physical Therapy, China Medical University, Taichung, Taiwan; 2 Institute of Biomedical Engineering, National Yang-Ming University, Taipei, Taiwan; 3 Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, Taiwan; 4 Department of Orthopedics, China Medical University Hospital, Taichung, Taiwan
Background: Joint laxity was considered one of risk factors for anterior cruciate ligament (ACL) injury. This study aimed to investigate the possibility of using anterior knee laxity as a risk predictor in noninjured sides of unilateral ACL deficient (ACLD) patients. Methods: Forty unilateral ACLD patients and forty healthy volunteers were recruited. Their anterior knee laxities were tested using KT-2000 knee ligament arthrometer, and the load-displacement curves were separated into three regions by stiffness changes using a self-written MATLAB program. The displacement of each region was recorded and denoted as D1, D2, and D3. The slope in each region, the stiffness (denoted as k1, k2, and k3), was also calculated. Independent t-test was used to compare the displacement and stiffness of each region between the control group and the noninjured knees of the ACLD group. The ROC curve of each variable was created for the analysis of potential risk predictors. Results: The results showed significant differences in displacements and stiffness between the control and the noninjured knees of the ACLD group. The ROC analysis showed k3 had the largest Area Under Curve (AUC) with the value of 0.748 (p<0.001), followed by D2 with AUC=0.662 (p=0.05) (Fig 1). Conclusion: This study demonstrated different characteristics in anterior knee laxity of the noninjured sides of ACLD patients compared to healthy controls. Stiffness in the high loading region in anterior knee laxity test (k3) could be considered a potential risk predictor for ACL injury.
