An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors.

ABSTRACT:

Background

Kidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function (DGF), a common post-operative complication, may negatively impact the long-term outcomes of both the graft and the pediatric recipient. However, there is limited research on DGF in pediatric kidney transplant recipients. This study aims to develop a predictive model for the risk of DGF occurrence after pediatric kidney transplantation by integrating donor and recipient characteristics and utilizing machine learning algorithms, ultimately providing guidance for clinical decision-making.

Methods

This single-center retrospective cohort study includes all recipients under 18 years of age who underwent single-donor kidney transplan

SUBMITTER: Liu XY 

PROVIDER: S-EPMC11415997 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature