Surgery (St Vincent's) - Theses
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ItemTechniques and technologies in joint replacement surgery: evaluating the value proposition of computer navigation in total knee replacement( 2023-01)This thesis examined the role of computer navigation technologies in total knee replacement. I provide an overview of osteoarthritis and its impact across our healthcare system, the role of total knee replacement in the current management of knee osteoarthritis, and the value that total knee replacement delivers. I then examined the value proposition of computer navigation technologies used in total knee replacement, and the implications of this with respect to current surgical practices in total knee replacement. This was undertaken through a range of perspectives including patient-reported outcomes, complications, and resource utilisation. Finally, I evaluated the cost-effectiveness of computer navigation in total knee replacement surgery through a decision analysis using a Markov-based model informed by my preceding works. This body of work relied largely on the St Vincent’s Melbourne Arthroplasty Outcomes Registry (SMART), an institutional lower limb joint arthroplasty registry, based at St Vincent’s Hospital Melbourne under the stewardship of the University of Melbourne Department of Surgery and the Department of Orthopaedic Surgery at St Vincent’s Hospital Melbourne. I employed a variety of statistical and health economic strategies in performing these investigations and utilised propensity-score methods to ensure that the analyses conducted herein formed a valid and robust contribution to expanding the literature on techniques and technologies in joint replacement surgery.
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ItemOptimising Preoperative Decision-Making in Total Knee Arthroplasty Using a Machine Learning Approach: Development, internal validation, and clinical acceptability evaluation of a clinician-informed machine learning model for the prediction of 30-day readmission following total knee arthroplasty( 2023-06)Background: Total knee arthroplasty is an effective treatment for advanced osteoarthritis of the knee joint, leading to reduced pain, improved function, and better quality of life for affected patients. Following a total knee arthroplasty (TKA) procedure, 30-day readmissions indicate a suboptimal postoperative course which negatively impacts upon the patient’s recovery and poses a significant burden to the healthcare system. Machine learning techniques can be used to predict readmission risk for individual patients and therefore can be implemented in tools to support shared clinical decision-making between patient and orthopaedic surgeon. Objectives: 1. To utilise the experience and expertise of clinicians involved in the care of TKA patients in the identification and appraisal of risk factors for 30-day day readmission. 2. To develop a statistical model to predict 30-day readmission in TKA patients, utilising machine learning techniques and clinical insight for use in shared clinical decision-making. 3. To evaluate the performance of clinicians regularly involved in the care of TKA patients on predicting 30-day readmission following TKA for individual patients then compare the predictive performance of a risk prediction model with that of clinicians. 4. To explore the understanding of TKA patients regarding what AI is and what are its perceived benefits and potential pitfalls in the context of shared clinical decision-making. Methods: Mixed methods approach involving five stages, adapted from literature pertaining to the development and implementation of complex interventions. Stakeholder involvement was utilised throughout the project to engage clinicians, hospital administrative staff, and patients themselves. Patient involvement was embedded throughout the project by means of a research buddy program, and this was detailed in a perspective piece included in the Methods. Stage 1 involved risk factor identification and evaluation, comprising two stages: first, a narrative review, systematic review protocol, and systematic review and meta-analysis on patient-related risk factors for 30-day readmission following TKA; second, a modified Delphi survey and focus group study based on systematic review findings. Stage 2 involved dataset acquisition and description, comprising a cohort profile for the institutional arthroplasty registry and a narrative description of the process of accessing and utilising hospital administrative data. Stage 3 involved a multivariable predictive model development study based utilising machine learning techniques as well as clinical insight gained in Stage 1. Stage 4 involved clinical acceptability evaluation in the form of a computer vs clinician comparison study. Finally, Stage 5 involved clinical acceptability evaluation, capturing the patient perspective in a qualitative semi-structured interview study. Findings: Clinicians provided insight into the complexity of predicting readmission on account of the diverse range of risk factors. Together with machine learning and statistical techniques, this insight was applied to arthroplasty registry and hospital administrative data to develop a predictive model which i) outperformed clinicians’ predictive capabilities and ii) was adequately calibrated to facilitate implementation in the clinical setting. The qualitative study, co-designed with a consumer advocate, found that TKA patients were open to the use of AI in shared clinical decision-making, and these findings were contextualised in prior literature to generate recommendations for future implementation. Conclusions: This thesis demonstrated the development of a bespoke readmission risk prediction model for TKA patients in a process involving broad stakeholder involvement in recognition of the intrinsic value of involving stakeholders in research and development initiatives that impact upon them, and in recognition of the responsibility of researchers to do so. This process primed the model for future implementation to enhance shared clinical decision-making.
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ItemAssessing the suitability of Australian general practice electronic health record data for clinical prediction model development: A case study in Osteoarthritis( 2022)Australian general practice electronic health records contain a wealth of patient information. However, the suitability of these data for developing clinical prediction tools is unclear. This research focussed on determining the suitability of these data for prediction model development using osteoarthritis as a case-study. A comprehensive data quality assessment was conducted and a prediction model for total knee replacement surgery developed. The thesis demonstrated that suitability of Australian general practice electronic health record data for prediction model development should be assessed on a case-by-case basis. A decision aid was developed to assist researchers in determining whether their electronic health record data are fit for prediction model development.