Senanayake S, Kularatna S, Healy H, Graves N, Baboolal K, Sypek MP, Barnett A. Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index.
BMC Med Res Methodol 2021;
21:127. [PMID:
34154541 PMCID:
PMC8215818 DOI:
10.1186/s12874-021-01319-5]
[Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/17/2021] [Indexed: 12/23/2022] Open
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.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12874-021-01319-5.
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