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Record W7055431961

Clinical Trials in Kidney Transplantation: Design Considerations and Novel Approaches

2019· dissertation· en· W7055431961 on OpenAlexaboutno aff

Bibliographic record

VenueThe Sydney eScholarship Repository (The University of Sydney) · 2019
Typedissertation
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsClinical trialKidney transplantationRandomized controlled trialTransplantationPsychological interventionClinical study designResearch designNephrology
DOInot available

Abstract

fetched live from OpenAlex

Background: Clinicians face considerable uncertainty in the management of common complications related to kidney transplantation including cardiovascular disease, cancer and rejection. A well-designed randomised controlled trial (RCT) is the ideal study type to help answer questions in areas of clinical equipoise. The deficiencies of nephrology RCTs are well known to the nephrology community. Together with the known issues of logistics, cost and lack of infrastructure, kidney transplantation trials face unique challenges that impact study design, trial feasibility and ascertainment of trial outcomes. Given current failure to improve long-term outcomes for kidney transplant recipients, there is a need to enhance trial design and feasibility by making trials more efficient and more focussed on what matters to patients and clinicians. Methods: This thesis utilises novel approaches to trial design in kidney transplantation and is presented as a thesis of published works. Various clinical research methods were used to test the hypothesis that improvements in trial design can lead to the generation of new data, at lower cost and greater efficiency to improve patient outcomes. The first section, presented in Chapters 2 and 3 of the thesis, reports on the trial design considerations and implementation of the Canadian-Australasian RCT of Screening Kidney Transplant Candidates for Coronary Artery Disease (CARSK). Trial procedures were incorporated into routine care, improving recruitment feasibility and complete outcome ascertainment. A pretrial Markov microsimulation model was also constructed using clinical, costs and utility estimates derived from published literature to estimate the benefits (e.g. survival) and trade-offs (e.g. costs and complications) of the two interventions being tested from a health systems perspective. The pre-trial model defined areas of evidence uncertainty in to help inform data collection for the trial. The second section of the thesis, presented in Chapters 4 and 5, describes a novel method for long-term outcome ascertainment of hard outcomes of kidney transplantation trials. Participant outcomes from five multi-centre RCTs of everolimus-based immunosuppression were linked to the Australian and New Zealand Dialysis and Transplant (ANZDATA) Registry. Meta-analyses using individual participant data were performed to ascertain long term cancer incidence, graft function and patient survival. The third section, chapter 6, presents a systematic review and meta-analysis of controlled trials in the treatment of antibody-mediated rejection (AMR). The risk of bias for all studies and the quality of evidence (for graft survival) were analysed for each treatment regimen, highlighting the deficiencies in existing data, and exemplifying the challenges facing kidney transplantation trials. Results: New data were generated from each of the studies. The CARSK trial was rolled out at 12 Australasian sites, achieving 525 (48%) of the Australasian recruitment target over 2.5 years. Recruitment remains on-going at these sites, with Canada commencing recruitment in early 2019. Further expansion of new sites into Asia and Europe are expected. The pretrial model for CARSK provided strong reassurances that the new intervention would be cost-effective and improve patient survival, based on evidence synthesised from current literature. The lack of robust evidence (in particular of the costs of transplantation in the modern era) provided strong justifications for doing the trial and also for including a strong health-economic component. By linking individual participant data to an established registry, novel evidence for the benefits and potential problems with everolimus-based immunosuppression were derived. Everolimus with reduced dose calcineurin inhibitor (CNI) combination was associated with a 56% reduction in the development of non-melanoma skin cancers compared with standard CNI-based triple therapy. However, the poor tolerability of everolimus in trials suggests widespread use of everolimus-based immunosuppression as first-line maintenance therapy may be problematic. In the systematic review and meta-analysis of the treatment of acute and chronic AMR, plasma exchange and IVIG have emerged as the standard-of-care, despite the lack of any highquality evidence supporting use of this combination. Furthermore, newer agents targeting B cells, plasma cells and complement blockade have failed to demonstrate efficacy in the treatment of AMR. The quality of evidence for the majority of treatment strategies for AMR is low and has not improved with time. This remains an area of great unmet clinical need. Conclusion: The thesis has demonstrated that careful planning and innovative use of available resources can improve future trial design and generate meaningful data. Multicentre collaborations are required to achieve acceptable power and generalisability. Pre-trial modelling should be considered as an adjunct to trials to examine evidence uncertainties and further improve the efficiency and quality of data collection. Trial procedures should be integrated into routine care where possible to maximise efficiency, engagement and potentially enhance translation. Linkage of outcome data to existing registries such as ANZDATA should be considered a priori so that long-term patient-centred outcomes, as opposed to short-term surrogate outcomes, can be ascertained with reliability and at acceptable cost.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.407
metaresearch head score (Gemma)0.509
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.407
Threshold uncertainty score0.731

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4070.509
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0050.005
Science and technology studies0.0020.020
Scholarly communication0.0160.014
Open science0.0060.008
Research integrity0.0120.017
Insufficient payload (model declined to judge)0.0080.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.106
GPT teacher head0.283
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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