Predicting 1-Year Mortality After Pediatric Heart Transplantation Using Machine Learning: A Systematic Review and Meta-Analysis.

ABSTRACT:

Background

Accurate prediction of post-heart transplant (HT) mortality is essential for donor selection, perioperative management, and equitable organ allocation in children. Traditional risk models often fail to capture nonlinear and temporally dynamic patterns, whereas machine learning (ML) approaches may improve predictive precision across eras.

Objectives

The purpose of this study was to systematically evaluate ML-based models predicting 1-year mortality after pediatric HT, synthesize pooled diagnostic performance, and identify temporal shifts in major risk predictors.

Methods

PubMed, Scopus, and Embase were searched through December 2024. Five studies (n = 33,286) applying ML methods-including semisupervised forests, ensemble algorithms, SHapley Additive exPlanat

SUBMITTER: Das BB 

PROVIDER: S-EPMC12765434 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature