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Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

Firezer Haregu¹, R Jerome Dixon², Michael McCulloch¹

¹Pediatric Cardiology, University of Virginia Children's Hospital, Charlottesville, Virginia, USA.

Pediatric Transplantation

|April 28, 2025

Summary

This summary is machine-generated.

Waitlist mortality in pediatric heart transplant candidates is influenced by institutional organ offer acceptance. Centers refusing more offers have worse outcomes, highlighting the need for standardized criteria and addressing modifiable risks.

Keywords:

heart transplantation pediatric waitlist mortality

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Area of Science:

Pediatric Cardiology
Transplant Surgery
Health Services Research

Background:

Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, especially those with congenital heart disease.
Listing center organ offer acceptance practices significantly impact waitlist outcomes.
Machine learning (ML) can identify factors associated with waitlist mortality, integrating institutional and candidate-specific variables.

Purpose of the Study:

To utilize machine learning to identify key predictors of waitlist mortality in pediatric heart transplant candidates.
To investigate the association between institutional organ offer acceptance practices and waitlist outcomes.
To combine candidate-specific risk factors with institutional practices for a comprehensive analysis.

Main Methods:

Analysis of the Organ Procurement and Transplantation Network database (2010-2020) for pediatric HTx candidates.
Application of various statistical and ML models (including CatBoost) to identify predictors of waitlist mortality or clinical deterioration.
Utilized SHAP values to assess variable importance and model performance (AUC-ROC 0.74, recall 0.75).

Main Results:

Overall waitlist mortality was 9.8% among 5523 pediatric candidates.
Key predictors included candidate diagnosis, age/size, ventilator use, eGFR, serum albumin, and ECMO.
Institutional factors like high offer refusal rates and low transplant volume were significant predictors of adverse outcomes.

Conclusions:

Institutional organ offer acceptance practices directly influence pediatric heart transplant waitlist outcomes.
Higher organ refusal rates at centers are linked to worse outcomes, irrespective of candidate-specific risks.
Standardizing organ acceptance criteria and addressing modifiable risks (malnutrition, renal dysfunction) are crucial for improving waitlist survival.