Machine Learning for Predicting Long-Term Cardiovascular Outcomes in Kidney Transplant Recipients.

ABSTRACT:

Background

Cardiovascular disease is the leading cause of morbidity and mortality in kidney transplant recipients (KTRs). Conventional risk assessment tools often underperform in this population due to their unique cardiovascular milieu shaped by end-stage kidney disease and immunosuppression.

Objectives

This study aimed to evaluate the performance of machine learning (ML) models in predicting major adverse cardiovascular events in KTRs.

Methods

We analyzed a data set of 518 adult KTRs from a single center (2015-2024) incorporating 1,014 demographic, clinical, laboratory, and imaging parameters in addition to ischemic work-up and revascularization details. After preprocessing, 44 features were used to train and validate ML models using AutoGluon with 5-fold cross-vali

SUBMITTER: Park WD 

PROVIDER: S-EPMC12666815 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature