Projecting the future: modelling Australian dialysis prevalence 2021-30.

Objectives 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.

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.

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

BACKGROUND: 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 with PDA use (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.

The living kidney donor profile index fails to discriminate allograft survival: implications for its use in kidney paired donation programs.

The inclusion of blood group- and human leukocyte antigen-compatible donor and recipient pairs (CPs) in kidney paired donation (KPD) programs is a novel strategy to increase living donor (LD) transplantation. Transplantation from a donor with a better Living Donor Kidney Profile Index (LKDPI) may encourage CP participation in KPD programs. We undertook parallel analyses using data from the Scientific Registry of Transplant Recipients and the Australia and New Zealand Dialysis and Transplant Registry to determine whether the LKDPI discriminates death-censored graft survival (DCGS) between LDs. Discrimination was assessed by the following: (1) the change in the Harrell C statistic with the sequential addition of variables in the LKDPI equation to reference models that included only recipient factors and (2) whether the LKDPI discriminated DCGS among pairs of prognosis-matched LD recipients. The addition of the LKDPI to reference models based on recipient variables increased the C statistic by only 0.02. Among prognosis-matched pairs, the C statistic in Cox models to determine the association of the LKDPI with DCGS was no better than chance alone (0.51 in the Scientific Registry of Transplant Recipient and 0.54 in the Australia and New Zealand Dialysis and Transplant Registry cohorts). We conclude that the LKDPI does not discriminate DCGS and should not be used to promote CP participation in KPD programs.

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 variables 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.

Quantifying lead time bias when estimating patient survival in preemptive living kidney donor transplantation.

Preemptive 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 during 1998-2017. Patients were transplanted preemptively (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 predialysis) using left-truncated Cox models with the primary outcome of patient survival. The median eGFR at transplantation was 6.9 mL/min per 1.73 m2 in the non-pre-emptive, and 9.6 mL/min per 1.73 m2 in the preemptive group. In the matched cohort (n = 1398), preemptive transplantation was not associated with a survival advantage hazard ratio (HR) for preemptive vs non-pre-emptive 1.12 (95% confidence interval [CI] 0.79-1.61). Accounting for lead time moved the point estimates toward a survival disadvantage for preemptive transplantation (eg, HR assuming 4 mL/min per 1.73 m2 /year eGFR decline, 1.21 [0.85, 1.73]), but in all cases the 95% CIs crossed 1. The optimal timing of preemptive living donor kidney transplantation requires further study.

Developing the future Indigenous health workforce: The feasibility and impact of a student-led placement programme in remote Indigenous communities.

OBJECTIVE: To describe and evaluate a programme where medical students designed and implemented Indigenous health placements for students with an interest in rural/Indigenous health. DESIGN, SETTING AND PARTICIPANTS: In 2011, a student-led programme at the University of Adelaide was set up to give medical students the opportunity to undertake outreach trips and clinical placements in remote Indigenous communities. Twenty-four medical students attended trips to remote communities between 2012 and 2014. Here we evaluate our programme using a single-arm experimental design. MAIN OUTCOME MEASURES: Responses to questionnaire items before and after attending an outreach placement, scored on 6-point Likert scales. RESULTS: Following their remote Indigenous health placement, participants expressed a significantly higher mean likelihood of working in an Indigenous community in the future (3.17 (2.69-3.64) versus 4.00 (3.65-4.35); P < 0.007). Furthermore, after their placement participants felt better prepared to work in Indigenous communities (mean 1.79 (1.44-2.14) versus 3.21 (2.88-3.54); P < 0.001). CONCLUSIONS: A placement programme initiated and run by medical students can provide meaningful exposure to Indigenous health. Implementation of this student-led model in other medical schools may encourage nationwide development of the Indigenous health workforce.