Association of admission, nadir, and terminal donor creatinine with kidney transplantation outcomes

Introduction: When assessing deceased kidney donors, a key factor in organ acceptance and allocation is donor kidney function. It is unclear whether terminal, admission, or the highest of terminal and admission donor estimated glomerular filtration rate (eGFR) most predicts recipient outcomes. Methods: We examined which measurement best predicts outcomes. Using data from the Australia and New Zealand Organ Donation and Dialysis and Transplant Registries, we included adult recipients of deceased donor kidney-only transplants over 2003 to 2019. We compared the 3 different exposure vari ables of admission, terminal, or highest eGFR. We created logistic regression models for delayed graft function (DGF), multilinear regression models for 6- and 12-month eGFR, and Cox proportional hazards models for graft loss, death censored graft failure and patient death. Results: A total of 8971 transplant recipients were included. There was strong evidence of an association between terminal, admission, and highest donor eGFR and DGF and recipient eGFR at 6 and 12 months. The eGFR was a strong predictor of graft and death censored graft failure, but not patient death. Terminal was a better predictor than admission and highest eGFR particularly for more contemporaneous outcomes. Conclusion: In assessing kidney donors, terminal eGFR were marginally better than admission and highest at predicting outcomes. Terminal eGFR should be used in risk equations to predict hard clinical endpoints.Georgina L. Irish, P. Toby Coates and Philip A. Clayto

Projecting the future: modelling Australian dialysis prevalence 2021–30

To project the prevalence of people receiving dialysis in Australia for 2021–30 to inform service planning and health policy. Methods. Estimates were based on data from 2011 to 2020 from the Australia & New Zealand Dialysis & Transplant (ANZDATA) Registry and the Australian Bureau of Statistics. We projected dialysis and functioning kidney transplant recipient populations for the years 2021–30. Discrete-time, non-homogenous Markov models were built on probabilities for transition between three mutually exclusive states (Dialysis, Functioning Transplant, Death), for five age groups. Two scenarios were employed – stable transplant rate vs a continued increase – to assess the impact of these scenarios on the projected prevalences. Results. Models projected a 22.5–30.4% growth in the dialysis population from 14 554 in 2020 to 17 829 (‘transplant growth’) – 18 973 (‘transplant stable’) by 2030. An additional 4983–6484 kidney transplant recipients were also projected by 2030. Dialysis incidence per population increased and dialysis prevalence growth exceeded population ageing in 40–59 and 60–69 year age groups. The greatest dialysis prevalence growth was seen among those aged ≥70 years. Conclusion. Modelling of the future prevalence of dialysis use highlights the increasing demand on services expected overall and especially by people aged ≥70 years. Appropriate funding and healthcare planning must meet this demand.Dominic Keuskamp, Christopher E. Davies, Georgina L. Irish, Shilpanjali Jesudason and Stephen P. McDonal

Do patient decision aids help people who are facing decisions about solid organ transplantation? A systematic review

Published April 2023Background: Decisions about solid organ transplantation are complex.Patient decision aids (PDAs) enhance traditional education, by improving knowledge and supporting patients to align their values with treatments. There are increasing numbers of transplantation PDAs, however, it is unclear whether these are effective. We conducted a systematic review of studies assessing the impact of PDA use in transplantation. Methods:We searched the Cochrane Register of Controlled Trials, CINAHL, EMBASE, MEDLINE, and PsycINFO databases from database inception to October 26, 2020.We included primary studies of solid organ transplantation PDAs defined by the International Patient Decision Aids Standards. All comparators and reported outcomes were included. Mean difference in knowledge (before vs. after) was standardized on a 100-point scale. Pooled-effect for PDAs was calculated and compared to the standard of care for randomized controlled trials (RCTs) and meta-analyzed using random effects. Analysis of all other outcomes was limited due to heterogeneity (PROSPERO registration, CRD42020215940). Results: Seven thousand four hundred and sixty-three studies were screened, 163 underwent full-text review, and 15 studies with 4278 participants were included. Nine studies were RCTs. Seven RCTs assessed knowledge; all demonstrated increased knowledge withPDAuse (mean difference, 8.01;95%CI 4.69–11.34, p<.00001). There were many other outcomes, including behavior and acceptability, but these were too heterogenous and infrequently assessed for meaningful synthesis. Conclusions: This review found that PDAs increase knowledge compared to standard education, though the effect size is small. PDAs are mostly considered acceptable; however, it is difficult to determine whether they improve other decision-making components due to the limited evidence about non-knowledge-based outcomes.Georgina L. Irish, Alison Weightman, Jolyn Hersch, P. Toby Coates, Philip A Clayto

The kidney failure risk equation predicts kidney failure: Validation in an Australian cohort

Aims: Predicting progression to kidney failure for patients with chronic kidney disease is essential for patient and clinicians' management decisions, patient prognosis, and service planning. The Tangri et al Kidney Failure Risk Equation (KFRE) was developed to predict the outcome of kidney failure. The KFRE has not been independently validated in an Australian Cohort. Methods: Using data linkage of the Tasmanian Chronic Kidney Disease study (CKD. TASlink) and the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA), we externally validated the KFRE. We validated the 4, 6, and 8-variable KFRE at both 2 and 5 years. We assessed model fit (goodness of fit), discrimination (Harell's C statistic), and calibration (observed vs predicted survival). Results: There were 18 170 in the cohort with 12 861 participants with 2 years and 8182 with 5 years outcomes. Of these 2607 people died and 285 progressed to kidney replacement therapy. The KFRE has excellent discrimination with C statistics of 0.96–0.98 at 2 years and 0.95–0.96 at 5 years. The calibration was adequate with well-performing Brier scores (0.004–0.01 at 2 years, 0.01–0.03 at 5 years) however the calibration curves, whilst adequate, indicate that predicted outcomes are systematically worse than observed. Conclusion: This external validation study demonstrates the KFRE performs well in an Australian population and can be used by clinicians and service planners for individualised risk prediction.Georgina L. Irish, Laura Cuthbertson, Alex Kitsos, Tim Saunder, Philip A. Clayton, Matthew D. Jos

Should You Accept What Others Reject?

CommentaryStephen P. McDonald, Georgina L. Iris

Cancer post kidney transplant: the question of risk

Abstract not availableGeorgina L. Irish, P. Toby Coates and Philip A. Clayto

Quantifying lead time bias when estimating patient survival in pre-emptive living kidney donor transplantation

Pre-emptive kidney transplantation is the preferred initial renal replacement therapy, by avoiding dialysis and reportedly maximizing patient survival. Lead time bias may account for some or all of the observed survival advantage, but the impact of this has not been quantified. Using the Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry, we included adult recipients of living donor kidney transplants over 1998-2017. Patients were transplanted pre-emptively (n=1435) or after receiving up to 6 months of dialysis (n=712). We created a matched cohort using propensity scores, and accounted for lead time (dialysis and estimated pre-dialysis) using left-truncated Cox models with the primary outcome of patient survival. The median eGFR at transplantation was 6.9 mL/min/1.73m2 in the non pre-emptive, and 9.6 mL/min/1.73m2 in the pre-emptive group. In the matched cohort (n=1398) pre-emptive transplantation was not associated with a survival advantage ((hazard ratio (HR) for pre-emptive vs. non pre-emptive 1.12 (95% CI 0.79-1.61)). Accounting for lead time, moved the point estimates towards a survival disadvantage for pre-emptive transplantation (eg. HR assuming 4mL/min/1.732 /year eGFR decline, 1.21 (0.85,1.73)), but in all cases the 95% CIs crossed 1. The optimal timing of pre-emptive living donor kidney transplantation requires further study.Georgina L. Irish, Steve Chadban, Stephen McDonald, Philip A. Clayto

Temporal validation of the Australian estimated post-transplant survival score

Aims: The Australian estimated post-transplant survival (EPTS-AU) prediction score was developed by re-fitting the United States of America EPTS, without diabetes, to the Australian and New Zealand kidney transplant population over 2002–2013. The EPTS-AU score incorporates age, previous transplantation and time on dialysis. Diabetes was excluded from the score, as this was not previously recorded in the Australian allocation system. In May 2021, the EPTS-AU prediction score was incorporated into the Australian kidney allocation algorithm to optimize utility for recipients (maximized benefit). We aimed to temporally validate the EPTS-AU prediction score to ensure it can be used for this purpose. Methods: Using the Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry, we included adult recipients of deceased donor kidney-only transplants between 2014 and 2021. We constructed Cox models for patient survival. We assessed validation using measures of model fit (Akaike information criterion and misspecification), discrimination (Harrell's C statistic and Kaplan–Meier curves), and calibration (observed vs. predicted survival). Results: Six thousand four hundred and two recipients were included in the analysis. The EPTS-AU had moderate discrimination with a C statistic of 0.69 (95% CI 0.67, 0.71), and clear delineation between Kaplan–Meier's survival curves of EPTS-AU. The EPTS was well calibrated with the predicted survivals equating with the observed survival outcomes for all prognostic groups. Conclusions: The EPTS-AU performs reasonably well in choosing between recipients (discrimination) and to predict a recipient's survival (calibration). Reassuringly, the score is functioning as intended to predict post-transplant survival for recipients as part of the national allocation algorithm.G. L. Irish, S. Campbell, J. Kanellis, Kate Wyburn, Philip A. Clayto