Department of Engineering, Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK.
Nuffield Department of Surgical Sciences, University of Oxford, Oxford, OX3 9DU, UK.
Sci Rep. 2024 Jul 29;14(1):17356. doi: 10.1038/s41598-024-66976-0.
The decision to accept a deceased donor organ offer for transplant, or wait for something potentially better in the future, can be challenging. Clinical decision support tools predicting transplant outcomes are lacking. This project uses interpretable methods to predict both graft failure and patient death using data from previously accepted kidney transplant offers. Using more than 25 years of transplant outcome data, we train and compare several survival analysis models in single risk settings. In addition, we use post hoc interpretability techniques to clinically validate these models. Neural networks show comparable performance to the Cox proportional hazard model, with concordance of 0.63 and 0.79 for prediction of graft failure and patient death, respectively. Donor and recipient ages, the number of mismatches at DR locus, dialysis type, and primary renal disease appear to be important features for transplant outcome prediction. Owing to their good predictive performance and the clinical relevance of their post hoc interpretation, neural networks represent a promising core component in the construction of future decision support systems for transplant offering.
接受已故供体器官移植的决定，或者等待未来可能更好的选择，可能具有挑战性。目前缺乏预测移植结果的临床决策支持工具。本项目使用可解释的方法，利用先前接受的肾移植供体数据来预测移植物失败和患者死亡。利用 25 年以上的移植结果数据，我们在单一风险环境中训练和比较了几种生存分析模型。此外，我们还使用事后可解释性技术对这些模型进行临床验证。神经网络与 Cox 比例风险模型的性能相当，预测移植物失败和患者死亡的一致性分别为 0.63 和 0.79。供体和受体年龄、DR 位点错配数量、透析类型和原发性肾脏疾病似乎是移植结果预测的重要特征。由于神经网络具有良好的预测性能和事后解释的临床相关性，因此代表了构建未来移植供体决策支持系统的有前途的核心组成部分。
