Abstract
A key issue in the field of kidney transplants is the analysis of transplant recipients’ survival. By means of the information obtained from transplant patients, it is possible to analyse in which cases a transplant has a higher likelihood of success and the factors on which it will depend. In general, these analyses have been conducted by applying traditional statistical techniques, as the amount and variety of data available about kidney transplant processes were limited. However, two main changes have taken place in this field in the last decade. Firstly, the digitalisation of medical information through the use of electronic health records (EHRs), which store patients’ medical histories electronically. This facilitates automatic information processing through specialised software. Secondly, medical Big Data has provided access to vast amounts of data on medical processes. The information currently available on kidney transplants is huge and varied by comparison to that initially available for this kind of study. This new context has led to the use of other non-traditional techniques more suitable to conduct survival analyses in these new conditions. Specifically, this paper provides a review of the main machine learning methods and tools that are being used to conduct kidney transplant patient and graft survival analyses.
Highlights
In 2002, the Kidney Disease Outcomes Quality Initiative (K/DOQI) defined the term “chronic kidney disease” (CKD) and classified its seriousness levels [1]
The purpose of this paper is to provide a narrative review of the main machine learning techniques used to conduct the survival analysis for kidney transplantation
The advantage found on the basis of the results presented by the authors is that the model obtained improves the estimated power of the Estimated Post-Transplant Survival (EPTS), a measure used to allocate some kidneys in the United States kidney allocation system [65] (0.724 against 0.697), proposing a new graft survival methodology
Summary
In 2002, the Kidney Disease Outcomes Quality Initiative (K/DOQI) defined the term “chronic kidney disease” (CKD) and classified its seriousness levels [1]. All CKD stages are associated with a higher risk of early death, cardiovascular morbidity and decreased quality of life. CKD can advance and cause a kidney failure, until, at a given point, the kidneys cease to work. The only option for survival in end-stage renal disease (ESRD) is Renal Replacement Therapy (RRT), such as haemodialysis, peritoneal dialysis and kidney transplant. These treatments extend patients’ lives, but do not cure the disease
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