Toward Predicting the Performance of Joint Arthroplasty

Joint arthroplasty is a common treatment for arthritis, involving the surgical implantation of prostheses to replace the bearing surfaces of diseased joints. While the clinical success of joint replacement is high, up to 25% of total knee replacement (TKR) patients remain dissatisfied with the outcome of their procedure. There is a recognized need for premarketing assessment tools capable of predicting performance outcomes of joint prostheses, in order to shorten development times, avoid disastrous prosthesis failures that threaten patient safety, and optimize design of components that are robust to patient and surgical variability. This chapter explores a novel computational framework that combines both experimental and computational approaches, describing subject-specific and probabilistic modeling techniques suitable for generating functional performances assessments of joint prostheses. Experimental and computational modeling approaches used in the past two decades are reviewed, including experimental kinematic and wear simulators, finite element models, lower limb musculoskeletal models, and deterministic and probabilistic techniques. The challenges of identifying and acquiring appropriate inputs for subject-specific computational models, experimentally verifying and validating model predictions, and incorporating population variability are discussed in detail. Throughout this chapter, the effectiveness of this modeling approach is documented through specific case studies of TKR prostheses, in which computational models are applied to questions of clinical and design importance.