Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jun 21;21(1):127.
doi: 10.1186/s12874-021-01319-5.

Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index

Sameera Senanayake  1   2 , Sanjeewa Kularatna  3 , Helen Healy  4   5 , Nicholas Graves  6 , Keshwar Baboolal  4   5 , Matthew P Sypek  7 , Adrian Barnett  3
Affiliations

Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index

Sameera Senanayake et al. BMC Med Res Methodol. .

Abstract

Background: Kidney graft failure risk prediction models assist evidence-based medical decision-making in clinical practice. Our objective was to develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia.

Methods: Data included donor and recipient characteristics (n = 98) of 7,365 deceased donor transplants from January 1st, 2007 to December 31st, 2017 conducted in Australia. Seven variable selection methods were used to identify the most important independent variables included in the model. Predictive models were developed using: survival tree, random survival forest, survival support vector machine and Cox proportional regression. The models were trained using 70% of the data and validated using the rest of the data (30%). The model with best discriminatory power, assessed using concordance index (C-index) was chosen as the best model.

Results: Two models, developed using cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure. The best fitting Cox model used seven independent variables and showed moderate level of prediction accuracy (calibration).

Conclusion: This index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools.

Keywords: Graft failure; Kidney transplant; Machine learning; Risk prediction.

PubMed Disclaimer

Conflict of interest statement

The authors of this manuscript have no conflicts of interest to disclose.

Figures

Fig. 1
Fig. 1
Model development and validation workflow. EO: Expert opinion; PCA: Principal component analysis; EN: Elastic net
Fig. 2
Fig. 2
Calculation of the risk index using the Cox model. Peripheral vascular disease is defined as presence of claudication symptoms
Fig. 3
Fig. 3
Kaplan–Meier survival curves indicating death-censored kidney graft failure by different risk prediction levels in the best fitting Cox model. The y-axis starts at a survival of 0.5 and not zero in order to more clearly show the separation between groups
Fig. 4
Fig. 4
Mean predicted survival (dashed line) versus the mean actual survival at 3 years and 5 years following transplantation
See this image and copyright information in PMC

References

    1. Tonelli M, Wiebe N, Knoll G, Bello A, Browne S, Jadhav D, Klarenbach S, Gill J. Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes. Am J Transplant. 2011;11(10):2093–2109. doi: 10.1111/j.1600-6143.2011.03686.x. - DOI - PubMed
    1. Clayton PA, Dansie K, Sypek MP, White S, Chadban S, Kanellis J, Hughes P, Gulyani A, McDonald S. External validation of the US and UK kidney donor risk indices for deceased donor kidney transplant survival in the Australian and New Zealand population. Nephrol Dial Transplant. 2019;34(12):2127–2131. doi: 10.1093/ndt/gfz090. - DOI - PubMed
    1. Rao PS, Schaubel DE, Guidinger MK, Andreoni KA, Wolfe RA, Merion RM, Port FK, Sung RS. A comprehensive risk quantification score for deceased donor kidneys: the kidney donor risk index. Transplantation. 2009;88(2):231–236. doi: 10.1097/TP.0b013e3181ac620b. - DOI - PubMed
    1. Moore J, He X, Shabir S, Hanvesakul R, Benavente D, Cockwell P, Little MA, Ball S, Inston N, Johnston A. Development and evaluation of a composite risk score to predict kidney transplant failure. Am J Kidney Dis. 2011;57(5):744–751. doi: 10.1053/j.ajkd.2010.12.017. - DOI - PubMed
    1. Parsons RF, Locke JE, Redfield RR, III, Roll GR, Levine MH. Kidney transplantation of highly sensitized recipients under the new kidney allocation system: A reflection from five different transplant centers across the United States. Hum Immunol. 2017;78(1):30–36. doi: 10.1016/j.humimm.2016.10.009. - DOI - PMC - PubMed

Publication types