Predicting Organ Rejections for Pediatric Heart Transplantations with a Combined Use of Transplant Registry Data and Electronic Health Records

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.

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.

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).

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.

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. Competing Interest Statement The authors have declared no competing interest. Funding Statement This project was partially supported by the National Library of Medicine under grant number R21LM013911, and the University of Florida-Florida State University Clinical and Translational Science Award, which is supported in part by the NIH National Center for Advancing Translational Sciences under award number UL1TR001427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study received approvals from the Institutional Review Board (IRB) at Florida State University under protocol number # STUDY00002444 and IRB at the University of Florida under the protocol number #IRB202101911. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Due to the HIPPA regulation, the study data cannot be shared.