Phase-specific kidney graft failure prediction with machine learning model.

ABSTRACT:

Background

Accurate prediction of kidney graft failure at different phases post-transplantation is critical for timely intervention and long-term allograft preservation. Traditional survival models offer limited capacity for dynamic, time-specific risk estimation. Machine learning (ML) approaches, with their ability to model complex patterns, present a promising alternative.

Methods

This study developed and dynamically evaluated phase-specific ML models to predict kidney graft failure across five post-transplant intervals: 0-3 months, 3-9 months, 9-15 months, 15-39 months, and 39-72 months. Clinically relevant retrospective data from deceased donor kidney transplant recipients were used for training and internal validation, with performance further confirmed on a blinded ext

SUBMITTER: Salybekov AA 

PROVIDER: S-EPMC12528114 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature