Bhasuran Balu, Wang Xiaoyu, Gupta Dipankar, Killian Michael, He Zhe
Florida State University, Tallahassee FL.
University of Florida, Gainesville, FL.
medRxiv. 2025 May 1:2025.04.29.25326701. doi: 10.1101/2025.04.29.25326701.
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.
小儿心脏移植面临供体器官可用性有限、等待名单时间延长以及晚期急性排斥反应（LAR）和住院风险升高的挑战。当前用于移植后结果的预测模型由于依赖登记数据而未整合动态临床和社会因素，缺乏高准确性。本研究旨在通过纳入电子健康记录（EHR）、健康的社会决定因素（SDoH）和器官共享联合网络（UNOS）数据来提高预测性能和模型可解释性。
我们使用了佛罗里达大学健康珊兹儿童医院111名小儿心脏移植患者（年龄0 - 18岁）的EHR和UNOS数据，构建移植后1年、3年和5年时器官排斥的预测模型。UNOS数据包括移植前后的健康和医疗记录，涵盖手术、临床评估和移植后随访信息，EHR数据包括不断变化的临床参数（如合并症、药物依从性和实验室结果），而SDoH包括社会经济地位、生活条件和医疗保健可及性。使用Shapley变量重要性云图（ShapleyVIC）评估特征重要性，该方法整合了Shapley加法解释（SHAP），以在几乎最优模型中提供强大、可解释的见解。
整合EHR、SDoH和UNOS数据的模型优于仅使用UNOS数据的模型，其曲线下面积（AUROC）分别为0.743（0.607 - 0.879）、0.798（0.725 - 0.871）和0.760（0.692 - 0.828）。排斥反应的关键预测因素包括移植前的严重状况（如生命支持、等待名单时间延长）、胆红素和肌酐水平升高以及社会因素（如交通障碍、体重指数、保险类型）。
研究结果揭示了整合临床和社会数据以解决多系统功能障碍、医疗保健可及性差异和依从性挑战的重要性。ShapleyVIC增强了模型的可解释性，为改善移植后护理提供了可操作的见解。
结合EHR、SDoH和登记数据的整体、数据驱动方法显著提高了预测准确性和可解释性，支持改善小儿心脏移植患者的长期结果。
