Temporal validation of the Australian estimated post‐transplant survival score

Author:

Irish G. L.123ORCID,Campbell S.45,Kanellis J.67,Wyburn Kate89,Clayton Philip A.123

Affiliation:

1. Transplant Epidemiology Group (TrEG), Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry, South Australian Health and Medical Research Institute (SAHMRI) Adelaide Australia

2. Central and Northern Adelaide Renal and Transplantation Service, Royal Adelaide Hospital Adelaide Australia

3. Department of Medicine The University of Adelaide Adelaide Australia

4. Department of Nephrology Princess Alexandra Hospital Brisbane Queensland Australia

5. School of Medicine, The University of Queensland Brisbane Queensland Australia

6. Department of Nephrology Monash Health Melbourne Australia

7. Centre for Inflammatory Diseases, Department of Medicine Monash University Melbourne Australia

8. Faculty of Medicine and Health Central Clinical School, The University of Sydney Sydney New South Wales Australia

9. Department of Renal Medicine Royal Prince Alfred Hospital Camperdown New South Wales Australia

Abstract

AbstractAimsThe 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.MethodsUsing 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).ResultsSix 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.ConclusionsThe 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. image

Publisher

Wiley

Subject

Nephrology,General Medicine

Reference19 articles.

1. Comparison of Mortality in All Patients on Dialysis, Patients on Dialysis Awaiting Transplantation, and Recipients of a First Cadaveric Transplant

2. A study of the quality of life and cost-utility of renal transplantation

3. ANZDATA Registry.44th Report Chapter 1: incidence of kidney failure with replacement therapy. Australia and New Zealand Dialysis and Transplant Registry Adelaide Australia.2021Accessed July 12 2022.http://www.anzdata.org.au

4. Why do we have the kidney allocation system we have today? A history of the 2014 kidney allocation system

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