Restoring rotatory laxity is critical to reestablishing joint function after knee ligament injuries such as rupture of the anterior cruciate ligament (ACL). In fact, excessive rotatory laxity is associated with a nearly two-fold increase in risk of early ACL graft failure. Unfortunately, patient-to-patient variability in rotatory laxity is immense and the mechanisms driving rotatory laxity are difficult to unravel. This complexity stems from the numerous soft tissues of the knee, the interplay of ligament slack length (the length at which the ligament begins to carry force) and stiffness, and the tremendous variability in ligament structural properties. Consequently, no clear clinical guidelines exist for how to best reestablish an individual patient?s rotatory laxity when treating knee ligament injuries, especially to the ACL. Our long-term goal is to develop clinical guidelines to treat knee ligament injury based on the patient-specific stabilizing mechanisms controlling rotatory laxity. This mechanistic understanding would set the foundation for more personalized, predictable treatments by enabling a surgeon to tailor ligament reconstruction to restore rotatory laxity on an individual basis. The specific goal of the proposed research is to develop a novel computer algorithm to identify critical combinations of ligaments and ligament properties (slack length and stiffness) that control rotatory laxity on a patient-specific basis. Inputs to this computer algorithm are: 1) geometric data including articular and meniscal shapes as well as ligament attachment sites obtained from 3D imaging; and 2) the rotatory laxity of the knee obtained through clinical examination. A statistically- augmented computational model of the knee will then output the specific ligaments and ligament properties that drive rotatory laxity.
The first aim focuses on: 1) obtaining measurements of rotatory laxity in cadaver knees and 2) building baseline computer models of these knees.
The second aim derives subject-specific ligament properties via statistical calibration and validates the properties using independent cadaveric biomechanical data from Aim 1. The hypothesis guiding this work is that ligaments and ligament properties driving rotatory laxity interact nonlinearly and vary from knee-to-knee. The expected outcome will be an algorithm to identify the ligaments and the ligament properties that are most important to restore rotatory laxity on a patient-specific basis. The contribution is significant because it will enable personalized, precise treatments of ligament injury tailored to the ligaments and ligament properties of individual patients. This contribution has high potential for clinical translation because it will guide surgeons on how to tune their surgeries to reduce high failure rates following ACL reconstruction. This statistically-enhanced, patient-specific modeling framework will provide the clinical and research communities with an innovative tool to identify the key drivers of rotatory laxity, thereby providing a unique opportunity to personalize ligament reconstruction to improve knee function and reduce risk of graft failure.
This research is relevant to public health because treatment for knee ligament injuries remains suboptimal and unpredictable with unacceptably high rates of reinjury within two years of reconstructive surgery. The discovery of innovative clinical guidelines for conservative and surgical treatments that account for an individual?s unique rotatory laxity could set the foundation for more personalized, precise, and predictable care. This is relevant to NIH?s mission to develop fundamental knowledge to enhance health and reduce disability.
