Surgery (St Vincent's) - Theses
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ItemCost effectiveness of reducing length of stay for total joint arthroplasty( 2023-12)Background: The utilisation of total joint arthroplasty (TJA) and incidence of osteoarthritis are both increasing. Globally, marked increases in the rate of arthroplasty being performed are predicted within the next ten years. Reducing length of stay (LOS) has been proposed as a method of increasing throughput and to match demand. However, the cost-effectiveness of reducing LOS from the perspective of patients, clinicians, hospitals, and government stakeholders remains to be understood. Objectives: The overall aim of this thesis is to investigate the cost-effectiveness of reducing LOS for TJA. To comprehensively analyse this broad objective, this thesis aimed to: (1) identify factors that affect LOS for TJA; (2) explore strategies that can be utilised to reduce LOS; (3) understand the clinical and cost-effectiveness of earlier discharge; (4) explore trial characteristics and meta-bias amongst trials investigating enhanced recovery after surgery pathways. Methods: This thesis adopts a mixed-methods approach, incorporating multiple quantitative studies and a qualitative study. The perspective of key stakeholders was engaged through each phase of this thesis. Firstly, a narrative review was performed to identify patient-level, clinical, and hospital-level factors that may inform LOS for TJA. In addition, the effect of complications on economic and clinical outcomes, including LOS, was explored. Patients’ and surgeons’ opinions regarding LOS were engaged to explore barriers and enablers for short stay arthroplasty. The impact of enhanced recovery after surgery pathways on outcomes after TJA was systematically reviewed among randomised clinical trials. Expanding the scope to a hospital and government perspective, a policy-level change targeting a one-day reduction in LOS was simulated using overlap propensity score weighted analysis. Finally, a cross-sectional study was performed using registered randomised clinical trials to understand the trial characteristics and meta-biases amongst trials implementing enhanced recovery after surgery. Findings: A diverse array of patient, hospital, and clinical factors were associated with LOS. Expectations of patients and surgeons, which were reinforced by the healthcare system, helped to inform LOS. When specifically testing shorter stay, earlier discharge did not change odds of complication or readmission. Overall, earlier discharge was cost neutral as cost savings were shifted onto inpatient rehabilitation. Systematic review and meta-analysis of clinical pathways combining several factors thought to individually reduce LOS, in the form of enhanced recovery after surgery pathways, were significantly associated with reduced LOS without a commensurate change in complications, readmission, or mortality after arthroplasty. Very low certainty evidence in this systematic review was reinforced when assessing the broader landscape of randomised trials investigating enhanced recovery after surgery – finding poor registration practices, and evidence of publication bias and selective outcome reporting. Conclusion: This thesis demonstrated LOS is determined by a complex interplay of patient, clinician, hospital, and government factors. In the current model of care for arthroplasty, early discharge was cost neutral. For a true cost saving to be realised with earlier discharge, significant changes are required in patient expectations, practice of arthroplasty, and organisation of services for arthroplasty.
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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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ItemNovel mitochondrial Drp1 inhibitors for cardioprotection( 2023)Mitochondria are dynamic organelles, constantly undergoing fusion and fission in a balanced manner to maintain cellular health. In the setting of myocardial ischaemia-reperfusion injury, mitochondrial morphology shifts towards excessive fission, which is associated with cardiomyocyte death and heart dysfunction. Inhibiting the mitochondrial fission protein dynamin-related protein 1 (Drp1) has been shown to reduce excessive mitochondrial fission and attenuate the pathological consequences of myocardial ischaemia-reperfusion injury. However, the most widely used inhibitor, Mdivi-1, is an unreliable inhibitor of Drp1 because of its off-target effects and inconsistent cytoprotection in different cell types, including mammalian cells. Mdivi-1 was originally developed to inhibit the GTPase enzymatic activity of Dnm1, a yeast homologue of human Drp1 protein, which has less than 50% similarity compared to human Drp1. These lines of evidence indicate that Mdivi-1 may not be a specific inhibitor of human Drp1. The overall aim of this thesis is to identify potential inhibitors of Drp1 that directly bind to, and inhibit the GTPase activity of human Drp1, and impart protection against in vitro and in vivo models of acute myocardial ischaemia-reperfusion injury. In Chapter 3, I investigated the interaction between Mdivi-1, yeast Dnm1 and human Drp1 using molecular modelling. Molecular docking analysis predicted that Mdivi-1 is docked more consistently in an open binding site conformation of both species with greater number of molecular interactions between the compound and yeast Dnm1 compared to human Drp1. Biological analysis of Mdivi-1 to human Drp1 was inconclusive due to differing results in direct binding assays, GTPase activity assay and mitochondrial morphology assays in Drp1 wildtype and knockout mouse embryonic fibroblasts. These results are likely confounded by the formation of Mdivi-1 aggregates at concentrations above 18.5 uM. These findings suggest that studies employing Mdivi-1 as an inhibitor of Drp1 warrant cautious interpretation as its effect may not be entirely Drp1-specific. In Chapter 4, further study was then conducted to identify a novel potential inhibitor of human Drp1. The drug discovery campaign for this project had already begun prior to my PhD study and three hit compounds, DRP1i1, DRP1i2 and DRP1i3 were previously identified. The three hit compounds represent three compound classes with distinct scaffolds, namely the diazabicyclic scaffold, tryptophan-like scaffold and the diazaspirocyclic scaffold. Direct binding assays, GTPase activity assays and mitochondrial morphology assays using Drp1 wildtype and knockout mouse embryonic fibroblasts indicate that DRP1i1, DRP1i2 and DRP3 directly bind to human Drp1, can inhibit its GTPase activity and supress Drp1-mediated mitochondrial fission. The most potent hit compound, DRP1i1 (KD value 3.23 uM), was selected for further investigation in in vitro and in vivo models of acute ischaemia-reperfusion injury in Chapter 5. In Chapter 5, DRP1i1 reduced cell death of HL1 cells and human cardiomyocytes derived from induced pluripotent stem cells subjected to hydrogen peroxide-induced oxidative stress and simulated ischaemia-reperfusion injury. In general, this protection was accompanied by reduced mitochondrial fragmentation, decreased mitochondrial superoxide production and improved mitochondrial membrane potential. The protective effect of DRP1i1 was also demonstrated in an in vivo mouse model of acute myocardial ischaemia-reperfusion injury, where I observed a reduction of infarct size accompanied by reduced phosphorylation of Drp1 at Ser616 and reduced circularity of myocardial interfibrillar mitochondria. Collectively, these results suggest that direct inhibition of the Drp1 protein with DRP1i1 possess a cytoprotective effect in in vitro and in vivo models of myocardial ischaemia-reperfusion injury. Due to the moderate affinity of the three hit compounds (within micromolar range; 3.23 uM for DRP1i1, 352 uM for DRP1i2 and 215 uM for DRP1i3), our lab had previously searched for structural analogues of each compound class in a two-dimensional analogue search based on the Tanimoto similarity index of 0.8. A total of 26 structural analogues of DRP1i1, 7 of DRP1i2 and 30 of DRP1i3 were identified. 10 additional analogues of DRP1i2 were also designed by our collaborator, giving us a total of 17 structural analogues for DRP1i2. In Chapter 6, I assessed these analogues for direct binding to human Drp1 and conducted molecular docking studies against human Drp1 to elucidate their structure activity relationship. Molecular docking analysis showed that DRP1i2 and its active analogues displayed the most consistently docked binding mode to the open conformation of human Drp1, whereas analogues of DRP1i1 and DRP1i3 did not show a clear consistency in binding mode. Regardless, hydrogen bond interactions between active compounds and amino acids Lys38 and Ser39 could be important for compound activity in all compound class and the effect of stereochemistry on binding affinity to human Drp1 protein was clearly demonstrated. Among all compound classes, only structural analogues of DRP1i2 and DRP1i3 that could potentially be more potent than their parent compounds. Collectively, the information on the structure activity relationship of these structural analogues will provide the essential fundamental knowledge to design better and more potent inhibitors of human Drp1 in future studies.