Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.777935
Title: The role of mechanical factors in unexplained pain after total knee arthroplasty
Author: Keller, Richard
ISNI:       0000 0004 7963 6991
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2017
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Abstract:
The work described in this thesis is motivated by the high impact of the painful degeneration of soft tissues of the knee, leading to osteoarthritis. Today, this is one of the most common causes of disability in what could be considered the developed world. The financial impact in lost productivity alone tops US$ 128 billion annually. End stage treatment for the painful osteoarthritic knee involves replacement of the articulating surfaces. For many patients this can relieve pain and restore a degree of mobility. Nevertheless, some 20% of patients who have had a total knee arthroplasty (TKA) suffer from chronic knee pain, often without a readily apparent aetiology, leaving the clinician without a clear therapeutic plan. The objective is to create a patient specific model of a painful knee with a TKA using finite element analysis (FEA). By applying boundary conditions of a failed TKA to an in silico model, associated factors such as implant placement and joint geometry can be evaluated and analysed. This computational method can be further supported by medical imaging modalities, providing a source of information for validation of an FEA model. Nuclear medical imaging, which relies on the variable metabolic uptake of unstable nu- clides, can highlight areas of increased metabolic activity in the joint, which may indicate areas of supraphysiological loading. We hypothesise that knee pain related to localised elevation of bone stresses correlate with increased bone metabolic activity. Specifically, studying unexplained pain after TKA using single photon emission computer tomography used conjointly with computer tomography (SPECT/CT) hybrid scans is used to compare bone stresses replicated in compuational modelling using FEA. FEA has the potential to elucidate joint loading issues that may be related to persistent pain after TKA surgery. Therefore, in order to better understand why a TKA procedure may result in an unsatisfactory outcome, we are developing patient specific FEA models of knee joints from SPECT/CT scans in order to analyse stresses and strains at the bone-implant interface. Comparing the nuclear imaging uptake to the equivalent computer modelling results, elevated bone metabolic activity may be able to be related to increased bone stress simulations. This bears a predictive value for further reaching simulations and future FEA models. In addition to the results obtained from patient specific FEA models, relating elevated bone metabolic activity to pain using nuclear imaging in conjunction with computational modelling may allow us to apprehend a clear picture of the relationship. A prospective follow up study was performed to compare the obtained clinical data to computer simulation results. The development of patient specific in silico models is presented, and their performance demonstrated in evaluation studies where surrounding and contributing factors are taken into account. The computer model utilises a strain adaptive algorithm that takes into account local bone remodelling effects to better reproduce the time dependent changes in bone geometry and mechanical properties and provide insights into possible causes for painful TKA outcomes. A significant clinical impact has not been able to be achieved by means of identifying relevant stress and strain levels at the bone implant interface of TKA procedures. There are no statistically relevant parameters relating nuclear imaging to the mechanical stimulii simulated by means of using FEA. The vision of supporting TKA outcome by means of prior analysis and being able to identify and localise sources of pain prior to a procedure could not be verified. However, the methodology developed can be utilised for both a closer look into similar hypotheses with a narrower, more selective patient population, as well for the further development of endoprostheses.
Supervisor: Amis, Andrew ; Hansen, Ulrich Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.777935  DOI:
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