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Risk Stratification for Graft Failure After Pediatric Heart Transplantation Using Random Survival Forests.

Omar Altamimi¹, Ali Ahmad¹, Reem Badran¹

¹School of Medicine, The University of Jordan, Amman, Jordan.

Pediatric Transplantation

|May 23, 2026

Summary

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This summary is machine-generated.

Machine learning, specifically Random Survival Forests, can identify risk patterns in pediatric heart transplant recipients. This approach aids in developing risk-adapted monitoring for better patient outcomes.

Area of Science:

Cardiology
Biostatistics
Machine Learning

Background:

Pediatric heart transplantation outcomes are difficult to predict due to patient variability.
Traditional methods struggle with non-linear relationships and complex data structures.
Survival-based machine learning offers a novel approach for time-to-event predictions.

Purpose of the Study:

To develop and evaluate a Random Survival Forest model for predicting graft failure in pediatric heart transplant recipients.
To assess the model's performance using metrics like Harrell's C-index and Time-dependent AUC.
To determine the clinical utility of the model for risk stratification.

Main Methods:

Utilized the United Network for Organ Sharing registry data (post-July 2016).

Keywords:

UNOS registry decision curve analysis machine learning pediatric heart transplantation random survival forest risk stratification

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Developed a Random Survival Forest model using the scikit-survival implementation.

Randomly divided data into training (75%) and testing (25%) sets.

Main Results:

The Random Survival Forest model achieved a Harrell's C-index of 0.60 on the test set.
1- and 2-year Time-dependent AUCs were 0.636 and 0.630, respectively.
Risk stratification identified distinct low- and high-risk groups with significant survival differences (log-rank p=0.003).

Conclusions:

Random Survival Forests can identify clinically relevant patterns in pediatric heart transplantation, despite moderate discrimination.
The model demonstrates potential for risk-adapted monitoring frameworks.
Machine learning offers a promising avenue for improving prediction accuracy and patient management in this population.