Prospective Frontal Plane Angles Used to Predict ACL Strain and Identify Those at High Risk for Sports-Related ACL Injury

Abstract

Background: Knee abduction moment during landing has been associated with anterior cruciate ligament (ACL) injury. However, accurately capturing this measurement is expensive and technically rigorous. Less complex variables that lend themselves to easier clinical integration are desirable. Purpose: To corroborate in vitro cadaveric simulation and in vivo knee abduction angles from landing tasks to allow for estimation of ACL strain in live participants during a landing task. Study Design: Descriptive laboratory study. Methods: A total of 205 female high school athletes previously underwent prospective 3-dimensional motion analysis and subsequent injury tracking. Differences in knee abduction angle between those who went on to develop ACL injury and healthy controls were assessed using Student t tests and receiver operating characteristic analysis. A total of 11 cadaveric specimens underwent mechanical impact simulation while instrumented to record ACL strain and knee abduction angle. Pearson correlation coefficients were calculated between these variables. The resultant linear regression model was used to estimate ACL strain in the 205 high school athletes based on their knee abduction angles. Results: Knee abduction angle was greater for athletes who went on to develop injury than for healthy controls ( P < .01). Knee abduction angle at initial contact predicted ACL injury status with 78% sensitivity and 83% specificity, with a threshold of 4.6° of knee abduction. ACL strain was significantly correlated with knee abduction angle during cadaveric simulation ( P < .01). Subsequent estimates of peak ACL strain in the high school athletes were greater for those who went on to injury (7.7-8.1% ± 1.5%) than for healthy controls (4.1-4.5% ± 3.6%) ( P < .01). Conclusion: Knee abduction angle exhibited comparable reliability with knee abduction moment for ACL injury risk identification. Cadaveric simulation data can be extrapolated to estimate in vivo ACL strain. Athletes who went on to ACL injury exhibited greater knee abduction and greater ACL strain than did healthy controls during landing. Clinical Relevance: These important associations between the in vivo and cadaveric environments allow clinicians to estimate peak ACL strain from observed knee abduction angles. Neuromuscular control of knee abduction angle during dynamic tasks is imperative for knee joint health. The present associations are an important step toward the establishment of a minimal clinically important difference value for ACL strain during landing.

Rehabilitation and Return to Sport Assessment after Anterior Cruciate Ligament Injury: Quantifying Joint Kinematics during Complex High-Speed Tasks through Wearable Sensors

“…These findings are in line with previous studies which focused on either gait or countermovements [22,38]. The evaluation of frontal plane angles is crucial in the assessment of ACL injury risk and RTS [39,40]. Primary attention is paid to knee and hip frontal plane kinematics.…”

Anterior Cruciate Ligament Loading Increases With Pivot-Shift Mechanism During Asymmetrical Drop Vertical Jump in Female Athletes

“…Knee abduction angle during DVJ is a predictor of ACL injury. 3 , 25 The present study confirmed that athletes with a high knee abduction angle have higher ACL strain during the DVJ. Note that this study does not prove a single effect of knee abduction angle but rather that other featured biomechanical variables are linked in high ACL strain trials.…”

“…While ACL strain is influenced by multiplane loading, 14 , 29 , 33 knee abduction moment during landing is a predictor of ACL injury, and it increases ACL strain in cadaveric simulations. 2 – 5 , 25 , 46 , 47 , 50 A recent musculoskeletal modeling study demonstrated that knee abduction moment was larger during landing in the frontal plane, with higher vertical and lateral ground-reaction force, lower gluteus medius force, and a laterally shifted and tilted pelvic position. 51 This musculoskeletal modeling study 51 supported a previously proposed ACL injury mechanism in frontal plane mechanics.…”

Linear Discriminant Analysis Successfully Predicts Knee Injury Outcome From Biomechanical Variables

“…The model may have improved accuracy with addition of other joint moments (eg, hip or ankle) or joint kinematics. 7 However, the performance of the discriminant analysis was good to excellent even with the current data set, with accurate prediction of 94.1% of injuries, a 4.7 times increase from chance. Future application of an artificial neural network machine learning algorithm would be valuable to assess these data; however, because this data set was limited to 41 injury events, more outcomes would be needed to properly validate the constructed model after algorithm training.…”