Enabling Individualised Surgical Treatment of Osteoarthritis

Lead Research Organisation: University of Leeds
Department Name: Mechanical Engineering

Abstract

Osteoarthritis affects over eight million people in the UK alone, with nearly three quarters of patients reporting some form of constant pain. Treatment for arthritis is estimated to cost the UK healthcare system over £10 billion per year, with significant additional societal costs for lost working hours and welfare payments.

Although hip and knee replacement surgeries are considered successful, these treatments are not suitable for all patients and some devices fail early, requiring costly and less successful revision surgery. There are over 15,000 revision surgeries performed in the UK alone each year. Younger and more active patients, as well as rising numbers with obesity, are placing greater demands on these treatments: implants need to last for longer and withstand more extreme loading than ever before. There is evidence that both individual patient biomechanics and surgical choices influence the outcomes of these treatments. Improved outcomes, particularly for more challenging patient groups, can only be achieved by better matching the treatment to the functional requirements of the individual patient.

This proposal will bring together complementary research expertise from two of the world's leading research institutes in the field to build the evidence needed to enable treatments for osteoarthritis to be better tailored to individual patient needs.

The Institute of Medical and Biological Engineering at the University of Leeds has developed unique capability and expertise to evaluate artificial and natural joints. These include the world's largest academic facility for experimentally testing joint replacements, as well as computational modelling methods to simulate how implants perform in the body. These capabilities enable the mechanical performance of implants to be evaluated under a range of different conditions, for example to study how the implant wears over time or becomes damaged with usage.
The Center for Orthopaedic Biomechanics at the University of Denver has developed world-leading capability in measuring patient joint mechanics in vivo, including methods of imaging patient joints as they undertake different activities, and parallel computational methods for deriving biomechanical information. These methods enable the forces and motions on an individual patient's hip or knee joints to be derived and, by collecting data on many patients, examine how these differ from one individual to another.

By combining the expertise across both groups, this Centre-to-Centre Research Collaboration will enable relationships to be developed between an individual patient's characteristics (e.g. their anatomy and how they load their joints) and the mechanical performance of the implant. Specifically, in the hip we will combine methodologies developed at the two centres to evaluate how patient and surgical factors affect the risk of early failure in hip replacements due to the device components pushing into each other or the surrounding bone (impingement), or the way the components are aligned. We will also examine how different choices of implant can influence the outcomes. In the knee, we will combine methodologies to identify how patient factors (such as the anatomy of the knee and the way it is loaded during different activities) affect early-stage treatments for knee osteoarthritis. We will also examine the effects of a greater range of activities, such as squatting and stair climbing, on the outcomes of knee replacements. These studies will bring together different methodologies and build new pathways for acquiring and sharing data that can be adopted more widely and applied to other musculoskeletal systems in the future. The work will build the evidence needed to improve hip and knee implant design, inform clinical decision-making, enhance patient quality of life and reduce early complications.

Publications

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Pryce GM (2022) Impingement in total hip arthroplasty: A geometric model. in Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine

 
Title Dataset supporting the publication 'Dynamic finite element analysis of hip replacement edge loading: balancing precision and run time in a challenging model' 
Description This dataset contains the full set of output values from a modelling study, summarised as follows. An important aspect in evaluating the resilience of hip replacement designs is testing their performance under adverse conditions that cause edge loading of the acetabular liner. The representation of edge loading conditions in computational models is computationally challenging, however, due to the changing contact locations, need for mesh refinement, and dynamic nature of the system. In the associated paper, an initial starting point for finite element modelling of this type of testing was developed. This consisted of recommendations for aspects such as the element type and shape, and to set up the head as the translating component and fix the liner using boundary conditions. It also included recommendations for element sizing for the bulk of the liner and the contacted rim, and a mass-scaling target time increment for the elements of the liner. In the paper we present a relatively clean sensitivity study to justify these choices and describe the sensitivity of results around them so that someone putting together a new model can hopefully do so far more quickly. There was a large amount of underpinning sensitivity testing, however, which was less structured but important for getting to this final recommendation. This data packet therefore contains results from two preliminary load cases (A and B) as well as the data used in the supported paper (case C). 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
Impact Dataset has enabled reuse of material for further research and training. 
URL https://archive.researchdata.leeds.ac.uk/1065/
 
Description University of Denver 
Organisation University of Denver
Country United States 
Sector Academic/University 
PI Contribution Analysis of data (in vivo data and model outputs) provided by the University of Denver using models developed at the University of Leeds
Collaborator Contribution Provision of data and expertise
Impact None as yet
Start Year 2022