Predicting Organ Rejections for Pediatric Heart Transplantations with a Combined Use of Transplant Registry Data and Electronic Health Records
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Objective
Pediatric heart transplantation is challenged by limited donor organ availability, prolonged waitlist times, and elevated risks of late acute rejection (LAR) and hospitalization. Current predictive models for post-transplant outcomes lack high accuracy due to reliance on registry data without integrating dynamic clinical and social factors. This study aimed to improve predictive performance and model interpretability by incorporating electronic health records (EHR), social determinants of health (SDoH), and United Network for Organ Sharing (UNOS) data.
Materials and Methods
We used EHR and UNOS data from 111 pediatric heart transplant patients (ages 0–18) at the University of Florida Health Shands Children’s Hospital to build predictive models for organ rejection at 1-, 3-, and 5-year intervals post-transplant. UNOS data includes pre- and post-transplant health and medical records, encompassing procedures, clinical evaluations, and post-transplant follow-up information, EHR data included evolving clinical parameters (e.g., comorbidities, medication adherence, and laboratory results), while SDoH encompassed socioeconomic status, living conditions, and healthcare access. Feature importance was assessed using Shapley Variable Importance Cloud (ShapleyVIC), which integrates Shapley Additive Explanations (SHAP) to provide robust, interpretable insights across nearly optimal models.
Results
Models integrating EHR, SDoH, and UNOS data outperformed those using UNOS data alone, with AUROC of 0.743 (0.607–0.879), 0.798 (0.725–0.871), and 0.760 (0.692–0.828). Key predictors of rejection included severe pre-transplant conditions (e.g., life support, prolonged waitlist times), elevated bilirubin and creatinine levels, and social factors (e.g., transportation barriers, BMI, insurance type).
Discussion
Findings reveal the importance of integrating clinical and social data to address multisystem dysfunction, disparities in healthcare access, and adherence challenges. ShapleyVIC enhanced model interpretability, providing actionable insights for improving post-transplant care.
Conclusion
Holistic, data-driven approaches that combine EHR, SDoH, and registry data significantly enhance predictive accuracy and interpretability, supporting improved long-term outcomes for pediatric heart transplant patients.
