Functioning menisci are critical to knee joint health. If a damaged meniscus cannot be repaired, a section is removed in a ?partial meniscectomy? (PM) procedure. While PM can relieve pain and restore function, the long- term result is an increased predisposition to osteoarthritis (OA), the development of which is often clinically silent in the early years. The prevalence of joint degeneration subsequent to meniscal surgery is highly variable across patients, with up to 40% of patients manifesting radiological evidence of the disease at 5 years post- operatively. Despite a vast number of clinical studies, the risk factors associated with developing OA after PM are essentially unknown. With such varied responses, it is impossible to counsel patients on expected outcome after meniscal surgery. Given the fundamental role of the meniscus in distributing forces across the knee joint, changes in knee mechanics that occur with PM have been implicated. The goal of this study is to determine which knee-specific mechanical factors are predictors for the development of post-PM OA. Our primary hypothesis is that the changes in the distribution of knee joint forces (e.g., contact stresses) after PM will constitute a key risk factor for articular cartilage and meniscal degeneration, independent of compartment. We will test this hypothesis by determining which mechanical factors adversely change the distribution of knee joint forces (in vitro studies) and which are strong predictors for the development of post- PM OA (in vivo studies). We will use: (a) a statistically driven experimentally validated computational approach to identify geometric features, tissue properties, and kinematic characteristics that influence contact force distribution across intact and PM knees, and (b) a mechanobiological analysis of a cohort of patients from pre- to post- PM surgery. By using a broad array of synergistic models (cadaveric, computational, patient-based, and statistical), our study will determine which ?clinically identifiable? mechanical features are responsible for significant changes in contact mechanics for post-PM knees. Our experimentally validated computational models will be shared with clinicians and the wider scientific community for the classification of patients at ?high risk? for post-PM OA in whom modified surgical, pharmacological, and/or rehabilitation techniques could help to mitigate these risk factors.
This project will identify knee-specific risk factors associated with the development of osteoarthritis (OA) after partial meniscectomy. Our goal is to classify knee features that can be quantified prior to surgery which are predictive of a high likelihood of early to mid-term post-operative joint tissue degeneration. The data generated will allow surgeons to counsel patients as to their risk of joint tissue degeneration after proposed partial meniscectomy, and help to identify patients most likely to benefit from additional surgical procedures or interventions.
