Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

ABSTRACT:

Background

Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influencing waitlist outcomes. We utilized machine learning (ML) to identify factors associated with waitlist mortality, combining variables associated with institutional offer acceptance practices as well as candidate-specific risk factors.

Methods

We analyzed the Organ Procurement and Transplantation Network database for pediatric HTx candidates listed between 2010 and 2020. Various statistical and ML models were employed to identify predictors of waitlist mortality or clinical deterioration leading to waitlist removal. The dataset w

SUBMITTER: Haregu F 

PROVIDER: S-EPMC12035663 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature