Predictive Dynamic Simulation of Human Movement Following Hip and Knee Replacement

Abstract

Total joint arthroplasty is a surgical intervention that involves the removal of arthritic or damaged components of a joint and their replacement with an artificial joint. The ultimate goal is to restore the joint’s functionality to that of a healthy joint. The most commonly performed types of total joint arthroplasty are Total Hip Arthroplasty (THA) and Total Knee Arthroplasty (TKA). Despite technological advancements in these surgeries, there are several post-operative complications associated with improper positioning of the implants. In Canada, approximately 8.5% of the 50,000 THA cases and 6.8% of the 60,000 TKA cases require revision surgeries. These issues are partially attributed to the limited consideration of patient-specific characteristics in the pre-operative planning of THA and TKA. The relationship between the anatomical features unique to each patient and the optimal positioning of the implants is not yet fully understood. This problem underscores the need to develop engineering technologies that can prevent revision surgeries and enhance the quality of life for patients undergoing THA and TKA.

The objective of this research is to utilize subject-specific musculoskeletal models and predictive simulations to achieve optimal implant positioning, thereby reducing the risk of implant failure or patient dissatisfaction following THA and TKA. In this thesis, we define mathematical indices that encompass the key factors contributing to implant failure or patient dissatisfaction (such as impingement and edge-loading) and use them to quantify the effectiveness of a given implant positioning. Through predictive musculoskeletal simulations of common daily activities like sit-to-stand, we examine how patient-specific conditions influence the optimal placement of hip and knee implants for THA and TKA.

To the best of the author’s knowledge, this research represents the first study to employ optimal control-based fully-predictive simulations in order to conduct “what-if” analyses and investigate the impact of patient-specific characteristics on the optimal positioning of implants. The methods and techniques employed in this study can also be applied to explore the effects of various other pathological disorders, not covered in this particular study, on the optimal implant positioning.