#2201 Access to waitlisting & kidney transplantation for patients with incident kidney failure in Australia and the United Kingdom—a binational comparative analysis
Bibliographic record
Abstract
Abstract Background and Aims System processes for evaluating and managing patient access to kidney transplantation (KTx) presents complex policy challenges. These processes are influenced by system frameworks operating at national, regional and centre levels, where both explicit policy directives and implicit clinical decisions shape patient evaluation and access pathways. Research has highlighted regional and centre-level differences in practice resulting in variations in access to kidney transplantation. Limited comparative studies have been conducted at an international level to assess the impact of national policy decisions on evaluation and access to kidney transplantation. The purpose of this study was to compare access to and predictors of waitlisting and kidney transplantation among patients with incident kidney failure in the United Kingdom (UK) and Australia. Method Incident adult patients commencing kidney replacement therapy (KRT) between 2010–2020 recorded in the United Kingdom Renal Registry (UKRR) and Australian & New Zealand Dialysis & Transplant (ANZDATA) Registry were included for analysis. The primary outcome was time-to-waitlisting with death and living donor kidney transplantation (LDKT) prior to waitlisting treated as competing risks. Secondary analysis included time-to-deceased donor transplantation. The cumulative incidence of the first observed outcome was recorded for each country. Multivariable competing risk time-to-event models were used to compare predictors of waitlisting between countries. Results The study cohort comprised 29,901 & 70,583 patients from Australia & the UK, respectively. Similar clinical and demographic characteristics were seen across the two groups. In Australia, 7,044 (23.6%) patients were waitlisted and 1,743 (5.8%) received a LDKT. In the UK, 22,745 (32.2%) patients waitlisted and 4,336 (6.1%) receiving a LDKT. (Table 1 and Fig. 1a). In examining predictors of waitlisting/LDKT, the difference between women and men in the chance of waitlisting/LDKT was bigger in Australia, with women less likely than men (Australia sub-distribution hazard ratio (SHR) 0.78 (95% CI 0.74–0.81), UK SHR 0.91 (95% CI 0.89–0.93)). There was a disparity in the likelihood of waitlisting/LDKT for patients with diabetic kidney disease (Australia SHR 0.35 (95% CI 0.33–0.37), UK SHR 0.55 (95% CI 0.53–0.57). Age, socio-economic status and smoking status were similar between the two countries. A secondary analysis examined deceased donor transplantation as the primary event with competing events of LDKT and death. In Australia, 5,254 (17.6%) and 2,053 (6.9%) patients had deceased and LDKT compared to 14,872 (21.1%) and 6,511 (9.2%) in the UK (Fig. 1b). Mortality was higher in the UK compared to Australia in both analyses (Fig. 1). Conclusion Incident KRT patients in Australia had lower rates of waitlisting and living donor transplantation compared to UK patients. Higher mortality rates were observed in the UK. Policy initiatives in the UK that prioritise pre-emptive waitlisting and access to pre-emptive deceased donor transplantation may support earlier waitlisting/LDKT for Australian KRT patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".