Summary

Computational models can be used to quantifying heightened ACL force and unique kinematics patterns in targeted, high risk male and female athletes presenting with geometric risk factors for ACL injury.

Abstract

Background

A recent prospective clinical study of 88 ACL-injured and 88 uninjured matched-control subjects (61 female pairs and 27 male pairs) has shown that increased sagittal slope of the lateral tibial cartilage in females and decreased posterior wedge angle of the lateral meniscus in males are risk factors for noncontact ACL rupture. Unfortunately, little is known about how the contacting articular and meniscal geometries impact ACL loading and tibiofemoral kinematics in individuals at elevated risk of ACL injury.

Purpose

To develop a framework to quantify ACL loads and tibiofemoral kinematics in male and female subjects exhibiting risk factors for non-contact ACL rupture.

Study Design: Case-control comparison of two injured and two injured athletes in a computational model.

Methods

This work involved the evaluation of two athletes (17-year-old female basketball player and a 17-year-old male soccer player) suffering first-time noncontact ACL rupture and corresponding uninjured age- and team-matched controls. These athletes were a subset of a larger group of 88 pairs of ACL-injured and uninjured subjects in a previous prospective clinical case-control study whose goal was to identify geometric risk factors for noncontact ACL rupture. The cases and controls were selected based on having the greatest difference in risk of ACL injury as identified via a multivariate risk model. Multibody dynamics computer models of the tibiofemoral joint were developed from geometric data obtained from MRI of each subject and using population average soft tissue properties to represent the cruciates, collaterals, posterior capsule, and menisci (Fig. 1). The computer models were loaded virtually, and ACL force and tibiofemoral kinematics were predicted in response to a simulated clinical pivot shift maneuver. The simulated pivot shift consisted of serially applied compression (100 N), valgus (8 Nm), internal rotation (2 Nm), and anterior (30 N) loads with the knee held at 15° of flexion.

Results

ACL force predictions for the female and male case knee models exceeded their matched controls by 61 N and 41 N, respectively (Fig. 2). For the female case, ACL force was 19 N greater than the matched control with applied compression, then rapidly increased and was 65 N greater than the matched control with the addition of valgus torque. Compared to the uninjured control, the female case knee model exhibited greater anterior tibial translation and internal tibial rotation (ITR) with compression and valgus. In the case and control male knee models, addition of internal rotation torque increased ACL force to 45 and 44 N, respectively. The male case knee model exhibited increased ITR with internal rotation torque. Adding the anterior force led to the largest difference in ACL force (41 N) between the male case and control knee models.

Discussion And Conclusion

This work provides proof-of-concept data supporting the ability of computational models to quantifying heightened ACL force and unique kinematics patterns in targeted, high risk male and female athletes presenting with geometric risk factors for injury. A key strength of this integrated clinical and computational framework presented is the ability to establish a direct mechanical link between geometric risk factors for injury and knee kinematics and ACL force.