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.
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No competing interests reported.
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Posted 04 Mar, 2021
On 12 Apr, 2021
Received 11 Apr, 2021
Received 11 Apr, 2021
Received 11 Apr, 2021
On 18 Mar, 2021
On 18 Mar, 2021
On 18 Mar, 2021
On 18 Mar, 2021
On 18 Mar, 2021
On 18 Mar, 2021
Invitations sent on 25 Feb, 2021
On 25 Feb, 2021
On 25 Feb, 2021
On 25 Feb, 2021
On 23 Feb, 2021
Posted 04 Mar, 2021
On 12 Apr, 2021
Received 11 Apr, 2021
Received 11 Apr, 2021
Received 11 Apr, 2021
On 18 Mar, 2021
On 18 Mar, 2021
On 18 Mar, 2021
On 18 Mar, 2021
On 18 Mar, 2021
On 18 Mar, 2021
Invitations sent on 25 Feb, 2021
On 25 Feb, 2021
On 25 Feb, 2021
On 25 Feb, 2021
On 23 Feb, 2021
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.
Figure 1
Figure 2
Figure 3
Figure 4
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