Pediatric transplantation · 2026
Risk Stratification for Graft Failure After Pediatric Heart Transplantation Using Random Survival Forests.
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Altamimi Omar, Ahmad Ali, Badran Reem, Sleihat Yasmeen, Altamimi Ali, Al-Ammouri Iyad
Аннотация
BACKGROUND: Prediction in pediatric heart transplantation is challenging due to clinical heterogeneity. This presents the dilemma of modeling non-linear relationships and complex variable structures. It may be overcome by survival-based machine learning techniques designed to accommodate time to event outcomes, contrary to traditional approaches.
METHODS: In this study, we developed and evaluated a Random Survival Forest model to predict graft failure among pediatric heart transplant recipients using United Network for Organ Sharing registry data (post-July 2016). The primary outcome was graft failure.
RESULTS: Data was divided randomly into training (n = 3121) and testing (n = 1041) sets in a 75:25 ratio, and the scikit-survival implementation of the Random Survival Forest algorithm was chosen for this analysis. On the held-out test set, the final model achieved a Harrell's C-index of 0.60, and recorded 1- and 2-year Time-dependent Area Under the Curve (AUC) of 0.636 and 0.630 respectively; the 1- and 2-year brier scores were 0.078 and 0.097 respectively. Risk stratification analysis showed that the model-derived risk separated pediatric patients into low- and high-risk groups, with significant divergence in Kaplan-Meier survival curves (log-rank p = 0.003) and 1-year estimated survival of 92.8% and 88.1%. Clinical utility of the model was further evaluated using decision curve analysis at 1-year post transplant, the model suggested greater net benefit.
CONCLUSIONS: These results suggest that, even in the absence of high discrimination, machine learning approaches like Random Survival Forests can uncover clinically relevant patterns and may support future development of a framework for risk-adapted monitoring in pediatric heart transplantation.
METHODS: In this study, we developed and evaluated a Random Survival Forest model to predict graft failure among pediatric heart transplant recipients using United Network for Organ Sharing registry data (post-July 2016). The primary outcome was graft failure.
RESULTS: Data was divided randomly into training (n = 3121) and testing (n = 1041) sets in a 75:25 ratio, and the scikit-survival implementation of the Random Survival Forest algorithm was chosen for this analysis. On the held-out test set, the final model achieved a Harrell's C-index of 0.60, and recorded 1- and 2-year Time-dependent Area Under the Curve (AUC) of 0.636 and 0.630 respectively; the 1- and 2-year brier scores were 0.078 and 0.097 respectively. Risk stratification analysis showed that the model-derived risk separated pediatric patients into low- and high-risk groups, with significant divergence in Kaplan-Meier survival curves (log-rank p = 0.003) and 1-year estimated survival of 92.8% and 88.1%. Clinical utility of the model was further evaluated using decision curve analysis at 1-year post transplant, the model suggested greater net benefit.
CONCLUSIONS: These results suggest that, even in the absence of high discrimination, machine learning approaches like Random Survival Forests can uncover clinically relevant patterns and may support future development of a framework for risk-adapted monitoring in pediatric heart transplantation.