Nathan D Schilaty1,2,3,4, Nathaniel A Bates1,2,3, Sydney Kruisselbrink1, Aaron J Krych1,2, Timothy E Hewett5. 1. Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota, USA. 2. Sports Medicine Center, Mayo Clinic, Rochester, Minnesota, USA. 3. Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA. 4. Department of Physical Medicine & Rehabilitation, Mayo Clinic, Rochester, Minnesota, USA. 5. Sparta Science, Menlo Park, California, USA.
Abstract
BACKGROUND: The most commonly damaged structures of the knee are the anterior cruciate ligament (ACL), medial collateral ligament (MCL), and menisci. Given that these injuries present as either isolated or concomitant, it follows that these events are driven by specific mechanics versus coincidence. This study was designed to investigate the multiplanar mechanisms and determine the important biomechanical and demographic factors that contribute to classification of the injury outcome. HYPOTHESIS: Linear discriminant analysis (LDA) would accurately classify each injury type generated by the mechanical impact simulator based on biomechanical input variables (ie, ligament strain and knee kinetics). STUDY DESIGN: Controlled laboratory study. METHODS: In vivo kinetics and kinematics of 42 healthy, athletic participants were measured to determine stratification of injury risk (ie, low, medium, and high) in 3 degrees of knee forces/moments (knee abduction moment, anterior tibial shear, and internal tibial rotation). These stratified kinetic values were input into a cadaveric impact simulator to assess ligamentous strain and knee kinetics during a simulated landing task. Uniaxial and multiaxial load cells and implanted strain sensors were used to collect mechanical data for analysis. LDA was used to determine the ability to classify injury outcome by demographic and biomechanical input variables. RESULTS: From LDA, a 5-factor model (Entropy R2 = 0.26) demonstrated an area under the receiver operating characteristic curve (AUC) for all 5 injury outcomes (ACL, MCL, ACL+MCL, ACL+MCL+meniscus, ACL+meniscus) of 0.74 or higher, with "good" prediction for 4 of 5 injury classifications. A 10-factor model (Entropy R2 = 0.66) improved the AUC to 0.86 or higher, with "excellent" prediction for 5 injury classifications. The 15-factor model (Entropy R2 = 0.85), produced 94.1% accuracy with the AUC 0.98 or higher for all 5 injury classifications. CONCLUSION: Use of LDA accurately predicted the outcome of knee injury from kinetic data from cadaveric simulations with the use of a mechanical impact simulator at 25° of knee flexion. Thus, with clinically relevant kinetics, it is possible to determine clinical risk of injury and also the likely presentation of singular or concomitant knee injury. CLINICAL RELEVANCE: LDA demonstrates that injury outcomes are largely characterized by specific mechanics that can distinguish ACL, MCL, and medial meniscal injury. Furthermore, as the mechanics of injury are better understood, improved interventional prehabilitation can be designed to reduce these injuries.
BACKGROUND: The most commonly damaged structures of the knee are the anterior cruciate ligament (ACL), medial collateral ligament (MCL), and menisci. Given that these injuries present as either isolated or concomitant, it follows that these events are driven by specific mechanics versus coincidence. This study was designed to investigate the multiplanar mechanisms and determine the important biomechanical and demographic factors that contribute to classification of the injury outcome. HYPOTHESIS: Linear discriminant analysis (LDA) would accurately classify each injury type generated by the mechanical impact simulator based on biomechanical input variables (ie, ligament strain and knee kinetics). STUDY DESIGN: Controlled laboratory study. METHODS: In vivo kinetics and kinematics of 42 healthy, athletic participants were measured to determine stratification of injury risk (ie, low, medium, and high) in 3 degrees of knee forces/moments (knee abduction moment, anterior tibial shear, and internal tibial rotation). These stratified kinetic values were input into a cadaveric impact simulator to assess ligamentous strain and knee kinetics during a simulated landing task. Uniaxial and multiaxial load cells and implanted strain sensors were used to collect mechanical data for analysis. LDA was used to determine the ability to classify injury outcome by demographic and biomechanical input variables. RESULTS: From LDA, a 5-factor model (Entropy R2 = 0.26) demonstrated an area under the receiver operating characteristic curve (AUC) for all 5 injury outcomes (ACL, MCL, ACL+MCL, ACL+MCL+meniscus, ACL+meniscus) of 0.74 or higher, with "good" prediction for 4 of 5 injury classifications. A 10-factor model (Entropy R2 = 0.66) improved the AUC to 0.86 or higher, with "excellent" prediction for 5 injury classifications. The 15-factor model (Entropy R2 = 0.85), produced 94.1% accuracy with the AUC 0.98 or higher for all 5 injury classifications. CONCLUSION: Use of LDA accurately predicted the outcome of knee injury from kinetic data from cadaveric simulations with the use of a mechanical impact simulator at 25° of knee flexion. Thus, with clinically relevant kinetics, it is possible to determine clinical risk of injury and also the likely presentation of singular or concomitant knee injury. CLINICAL RELEVANCE: LDA demonstrates that injury outcomes are largely characterized by specific mechanics that can distinguish ACL, MCL, and medial meniscal injury. Furthermore, as the mechanics of injury are better understood, improved interventional prehabilitation can be designed to reduce these injuries.
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