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Research Article
Sameera Senanayake
Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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
Risk prediction, Machine learning, Graft failure, Kidney transplant
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No competing interests reported.
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CURRENT STATUS: Under Review
Version 1
Posted 04 Mar, 2021
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Editorial decision: Major revision
On 12 Apr, 2021
Review #1 received
Received 11 Apr, 2021
Review #3 received
Received 11 Apr, 2021
Review #2 received
Received 11 Apr, 2021
Reviewer #5 agreed
On 18 Mar, 2021
Reviewer #3 agreed
On 18 Mar, 2021
Reviewer #1 agreed
On 18 Mar, 2021
Reviewer #7 agreed
On 18 Mar, 2021
Reviewer #6 agreed
On 18 Mar, 2021
Reviewer #4 agreed
On 18 Mar, 2021
Reviewers invited
Invitations sent on 25 Feb, 2021
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On 25 Feb, 2021
Editor invited
On 25 Feb, 2021
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This preprint is under consideration at BMC Medical Research Methodology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Sameera Senanayake
Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology (QUT), Brisbane
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 04 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 12 Apr, 2021
Review #1 received
Received 11 Apr, 2021
Review #3 received
Received 11 Apr, 2021
Review #2 received
Received 11 Apr, 2021
Reviewer #5 agreed
On 18 Mar, 2021
Reviewer #3 agreed
On 18 Mar, 2021
Reviewer #1 agreed
On 18 Mar, 2021
Reviewer #7 agreed
On 18 Mar, 2021
Reviewer #6 agreed
On 18 Mar, 2021
Reviewer #4 agreed
On 18 Mar, 2021
Reviewers invited
Invitations sent on 25 Feb, 2021
Editor assigned
On 25 Feb, 2021
Editor invited
On 25 Feb, 2021
Submission checks complete
On 25 Feb, 2021
First submitted
On 23 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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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