Thesis
On the quantification and objective classification of instability in the healthy, osteoarthritic and prosthetic knee
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2020
- Thesis identifier
- T16106
- Person Identifier (Local)
- 201651066
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Knee instability is a common complaint in osteoarthritis (OA), and a common reasonfor revision following total knee arthroplasty (TKA). Despite this, assessment ofinstability is hampered by the lack of a validated method of objective classification orquantification, with most research relying upon patient reports of frequency ofsymptoms. The aim of this thesis is to define a theoretical framework for instability inthe knee, and to develop a protocol for the classification and quantification of instabilityin the native and prosthetic knee.Instability of the knee in this thesis is understood as the failure of the joint to return to azero-state following perturbation using all the available active and passive mechanismsavailable to it, resulting in system collapse. Symptomatic instability is the awareness ofreaching the boundary between the stable and unstable state. The prevalence ofsubjective instability in the end stage OA knee was measured from a publicly availabledatabase of pre-operative knee scores from TKA patients, while the prevalence ofinstability as a cause of revision was assessed from case note review of TKA revisionpatients from a tertiary referral orthopaedic unit. A single channel, tibia mountedaccelerometer was selected for assessment of frontal plane knee movement duringnormal walking and a protocol developed its use. This was assessed for its repeatabilityand compared with standard gait analysis in healthy volunteers, and subjectively stableand unstable post-operative TKA patients. Found to be repeatable with differentiationof output between subjectively stable and unstable TKA, the protocol was adapted andused to compare subjectively stable and unstable OA knees prior to TKA. Using patientsubjective assessment as classifier, wavelet transforms, Principal Component Analysis and linear regression was used to produce a classification model from the accelerometerdata.The single accelerometer was found to produce classification with an accuracy of84.6%, sensitivity of 93.3% and specificity of 72.7%, with area under the curve (AUC)of 0.797. This classification model for instability produces the basis from which theprotocol can be adapted and developed to improve performance and ultimate quantifyinstability in the knee for use in clinical and research settings.
- Advisor / supervisor
- Riches, Philip
- Resource Type
- DOI
- Date Created
- 2019
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