Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches

Abstract

Heart transplantation is a life-saving procedure for children affected by end-stage heart failure. However, despite recent improvements in long-term outcomes, 1-year post-transplantation mortality has remained relatively high. Accurate prediction of post-transplantation mortality is crucial to evaluating risks related to recipient-donor matches. Machine learning techniques can potentially improve the current allocation system through the integration of a larger set of features. In this work, we improve 1-year mortality prediction after pediatric heart transplantation using a new self-training approach, based on generating artificial recipient-donor pairs as synthetic unlabeled observations. We tested and compared our approach to several baselines using the cohort of pediatric patients in the UNOS database. Our study suggests that augmenting the dataset with proper synthetic observations can improve the prediction of 1-year mortality after pediatric heart transplantation. Our findings have implications for the future of heart transplantation in children, offering a potential path to refine recipient-donor matching and improve survival rates. This study contributes to the growing field of advanced machine learning techniques applied to medical decision-making, specifically in the context of organ transplantation.

Keywords

• Heart transplantation
• machine-learning
• post-transplantation mortality
• self-training
• synthetic observations