Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index.

ABSTRACT:

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 an

SUBMITTER: Senanayake S 

PROVIDER: S-EPMC8215818 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature