CLINICIAN PERSPECTIVES ON FACTORS ASSOCIATED WITH DELAYED WAIT-LISTING FOR DECEASED DONOR RENAL TRANSPLANTATION IN AUSTRALIA
Bibliographic record
Abstract
Background: Patients with end-stage kidney disease have multiple treatment options including renal transplantation which offers superior survival and quality of life compared to dialysis with less financial burden. A recent analysis has identified only 12.1% of patients in Australia being actively listed for renal transplantation within 12 months of commencing dialysis with significant variations between state jurisdictions. We present a pilot study assessing, at a single centre level, clinician identified factors associated with delayed active listing for kidney transplantation in Australia. Methods: Data from National Organ Matching System and Australia and New Zealand Dialysis and Transplantation Registry were used to identify patients between the ages of 18-75 with an estimated 5-year post-transplant survival of greater than 90% receiving dialysis for more than 12 months. Individual surveys were forwarded to patient’s treating clinician requesting identification of reasons for prolonged time to wait-listing. Results: 92 patients were identified with 62 surveys completed. The average age was 45 years, 50% female patients, 83% of Caucasian ethnicity and an average of 4.09 years on dialysis. 54% of patients were current or previous cigarette smokers. 26 patients (31%) had been referred for transplant assessment and 37 patients (59%) had not been referred. Of the patients who had been referred for transplantation assessment the primary barrier for listing was secondary medical factors in 13 patients (52%). Of these medical factors, obesity was the barrier in 6 patients (24%) and cardiovascular disease was a barrier for 4 (16%) patients. Social factors accounted for barriers for 10 patients (40%). 3 patients (12%) were undergoing workup for live donor transplantation and 5 patients (20%) were noted to have geographical barriers accessing listing for transplantation. 1 patient (4%) declined workup for transplantation. For patients who had not been referred for deceased donor transplantation assessment the primary reason for not being referred was secondary medical factors which were identified as a reason in 24 patients (65%). Obesity was the leading cause for non-referral which was identified in 10 patients (27%) and uncontrolled infection was identified in 9 patients (24%). Social factors were also identified as a barrier to referral in 12 patients (49%). 8 patients (22%) did not want to proceed with renal transplantation. Conclusions: A number of barriers face patients accessing timely wait listing for deceased donor renal transplantation. We demonstrate in a pilot single centre study secondary medical factors are the leading driver of delays for timely wait listing, or non-referral, for deceased donor transplantation. Further analysis is required to determine centre-based trends within Australia particularly assessing the burden of obesity in the end-stage renal failure population looking to proceed with deceased donor renal transplantation. References: 1. Wolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LY, et al. Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med. 1999;341(23):1725-30. 2. Oniscu GC, Brown H, Forsythe JL. How great is the survival advantage of transplantation over dialysis in elderly patients? Nephrology, dialysis, transplantation: official publication of the European Dialysis and Transplant Association - European Renal Association. 2004;19(4):945-51. 3. Tonelli M, Wiebe N, Knoll G, Bello A, Browne S, Jadhav D, et al. Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes. American journal of transplantation: official journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2011;11(10):2093-109. 4. Wong G, Howard K, Chapman JR, Chadban S, Cross N, Tong A, et al. Comparative survival and economic benefits of deceased donor kidney transplantation and dialysis in people with varying ages and co-morbidities. PloS one. 2012;7(1):e29591. 5. Kim SJ, Gill JS, Knoll G, Campbell P, Cantarovich M, Cole E, et al. Referral for Kidney Transplantation in Canadian Provinces. Journal of the American Society of Nephrology: JASN. 2019;30(9):1708-21. 6. Clinical Guidelines for Organ Transplantation from Deceased Donors. The Transplantation Society of Australia and New Zealand; 2019 May 2019. 7. Sypek MP, Clayton PA, Lim W, Hughes P, Kanellis J, Wright J, et al. Access to waitlisting for deceased donor kidney transplantation in Australia. Nephrology (Carlton, Vic). 2019;24(7):758-66. 8. Campbell S, Pilmore H, Gracey D, Mulley W, Russell C, McTaggart S. KHA-CARI guideline: recipient assessment for transplantation. Nephrology (Carlton, Vic). 2013;18(6):455-62. 9. ANZDATA. Chapter 1: Incidence of Renal Replacement Therapy for End Stage Kidney Disease. Adelaide, Australia: Australia and New Zealand Dialysis and Transplant Registry; 2019. Report No.: 42nd Report. 10. Ducharlet K, Roberts MA, Lee D. Identifying the barriers to timely transplant waitlisting. Nephrology (Carlton, Vic). 2016;21(5):443. 11. Pussell BA, Bendorf A, Kerridge IH. Access to the kidney transplant waiting list: a time for reflection. Internal medicine journal. 2012;42(4):360-3. 12. ANZDATA. Chapter 6: Australian Transplant Waiting List. Adelaide, Australia: Australia and New Zealand Dialysis and Transpalnt Registry; 2018. Report No.: 41st Report.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Survey of clinicians on delays in transplant wait-listing; health services question about clinical care.
This studies barriers to kidney-transplant wait-listing, not the research system.
Clinical health-services study of delayed transplant wait-listing in Australia.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".