Experimental and Numerical Models of Complex Clinical Scenarios; Strategies to Improve Relevance and Reproducibility of Joint Replacement Research

Abstract

This research review aims to focus attention on the effect of specific surgical and host factors on implant fixation, and the importance of accounting for them in experimental and numerical models. These factors affect (a) eventual clinical applicability and (b) reproducibility of findings across research groups. Proper function and longevity for orthopedic joint replacement implants relies on secure fixation to the surrounding bone. Technology and surgical technique has improved over the last 50 years, and robust ingrowth and decades of implant survival is now routinely achieved for healthy patients and first-time (primary) implantation. Second-time (revision) implantation presents with bone loss with interfacial bone gaps in areas vital for secure mechanical fixation. Patients with medical comorbidities such as infection, smoking, congestive heart failure, kidney disease, and diabetes have a diminished healing response, poorer implant fixation, and greater revision risk. It is these more difficult clinical scenarios that require research to evaluate more advanced treatment approaches. Such treatments can include osteogenic or antimicrobial implant coatings, allo- or autogenous cellular or tissue-based approaches, local and systemic drug delivery, surgical approaches. Regarding implant-related approaches, most experimental and numerical models do not generally impose conditions that represent mechanical instability at the implant interface, or recalcitrant healing. Many treatments will work well in forgiving settings, but fail in complex human settings with disease, bone loss, or previous surgery. Ethical considerations mandate that we justify and limit the number of animals tested, which restricts experimental permutations of treatments. Numerical models provide flexibility to evaluate multiple parameters and combinations, but generally need to employ simplifying assumptions. The objectives of this paper are to (a) to highlight the importance of mechanical, material, and surgical features to influence implant–bone healing, using a selection of results from two decades of coordinated experimental and numerical work and (b) discuss limitations of such models and the implications for research reproducibility. Focusing model conditions toward the clinical scenario to be studied, and limiting conclusions to the conditions of a particular model can increase clinical relevance and research reproducibility.