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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. 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
The prevalence of chronic kidney disease is increasing globally
Incorporating the timing of the event to the prediction model, could lead to better prediction models. In this background. 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
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
Summary
The prevalence of chronic kidney disease is increasing globally. Along with this increment, the number of patients in end-stage of renal disease (ESRD) and the demand for kidney transplantation, along with other renal replacement therapies, have increased over recent years[1,2]. Machine learning techniques have been used to develop kidney graft outcome-prediction models[4,8,9,10,16]. Time-to-event (survival) information had not been modelled in any of the machine learning based prediction models available in the medical literature. 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 following hyper-parameters will be regularised until the optimal prediction is made: number of survival tree classifiers and maximum number of nodes
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