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Abstract
Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients. Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature. However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia. Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models (for live donor and deceased donor transplants). The best predictive model will be selected based on the model's performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.
Highlights
A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients
All electronic data will be saved with password protection on Queensland University of Technology’s secure server in encrypted folders only accessible to the nominated research staff. This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia
The live donor risk prediction model will be the first machine learning based predictive model developed using a large national dataset, and the deceased donor risk prediction model will be the only machine learning based predictive model that used more than 7,000 patient records outside the United States
Summary
Any reports and responses or comments on the article can be found at the end of the article. Machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia. Possible combinations of the three sets of selected features from the three different feature selection mechanisms (i.e., medical literature and expert opinion, principal component analysis and elastic net) will be considered as input variables for the four methods of predictive models and the four methods of machine learning algorithms. The outputs of different machine leaning predictive models will be compared with Kidney Donor Risk Index (KDPI), a commonly used index which quantify graft failure risk before transplantation. All electronic data will be saved with password protection on Queensland University of Technology’s secure server in encrypted folders only accessible to the nominated research staff
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