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Tools for Predicting Kidney Transplant Outcomes

Bergler, Tobias MD1; Hutchinson, James A. MD, PhD2

doi: 10.1097/TP.0000000000001891
In View: eResources

1 Department of Nephrology, University Hospital Regensburg, Regensburg, Germany.

2 Department of Surgery, University Hospital Regensburg, Regensburg, Germany.

Received 7 July 2017. Revision received 12 July 2017. Accepted 13 July 2017.

The authors declare no funding or conflicts of interest.

Correspondence: Tobias Bergler, MD, Department of Nephrology, University Hospital Regensburg, Franz-Josef-Strauß-Allee-11, 93053 Regensburg, Germany. (; James A. Hutchinson, MD, PhD, Department of Surgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee-11, 93053 Regensburg, Germany. (

A raft of clinical decisions could be simplified if it were possible to accurately predict individual clinical outcomes after kidney transplantation. There are now many alternative models based on clinical parameters available at the time of transplantation that were developed to predict time-to-graft failure1-13 or patient survival.14-18 Several such methods have been made publicly available as online tools [A-C]. Anecdotally, at least, these tools are accessed by kidney transplant recipients. Some attempts have been made to internally validate or compare the performance of these predictive models using registry data, but as yet, few have been externally validated in prospective studies.11,19-21 Estimated posttransplant survival time is controversially used to allocate organs for adult, single-kidney transplantation in some countries.22 Even where Kidney Donor Risk Index5 is not used for organ allocation, we are aware of nephrologists using this tool to gain an impression whether offered kidneys from “marginal” donors are likely to be suitable as single-organ transplants. We conclude that further studies are clearly needed to determine the accuracy of predictive tools based on common clinical parameters and to relate their performance to more sophisticated biomarker-based predictors of clinical outcome.





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