Wu Xiaoting, Zhang Hailing, Cai Minglong, Zhang Ying, Xu Anlan
Department of Gynecology, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China.
Department of Nursing, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China.
Int Urol Nephrol. 2025 Mar;57(3):947-955. doi: 10.1007/s11255-024-04264-6. Epub 2024 Nov 2.
Pulmonary infection is the most common and serious complication after kidney transplantation that affects the survival of the transplanted kidney and the quality of life of patients. This study aims to construct a machine learning model for predicting the risk of pulmonary infection after kidney transplantation.
We recruited 857 kidney transplant recipients from January 1, 2016, to December 31, 2021, in the Department of Nephrology, the First Affiliated Hospital of the University of Science and Technology of China. First, the distribution of baseline characteristics between patients with and without postoperative pulmonary infections was analyzed. Subsequently, six machine learning models were constructed to predict the risk of postoperative pulmonary infections. Finally, these models were subjected to external validation using an independent cohort. The performance of the models was evaluated by area under the receiver operating characteristic curve (AUC).
Among kidney transplant recipients, a total of 186 individuals developed pneumonia, with 144 cases in the training cohort and 42 cases in the external validation cohort. The AUC range of the six machine learning models for predicting the risk of postoperative pulmonary infection was 0.758-0.822 for the training cohort and 0.642-0.795 for the testing cohort. Among the models assessed, the gradient boosting machine demonstrated the most favorable predictive accuracy.
Our study has developed a predictive model for assessing the risk of pulmonary infection after kidney transplantation, thereby providing a valuable foundation for the effective management of kidney transplant recipients.
肺部感染是肾移植后最常见且严重的并发症，影响移植肾的存活及患者生活质量。本研究旨在构建一个用于预测肾移植后肺部感染风险的机器学习模型。
我们于2016年1月1日至2021年12月31日在中国科学技术大学附属第一医院肾内科招募了857名肾移植受者。首先，分析术后发生肺部感染和未发生肺部感染患者的基线特征分布。随后，构建六个机器学习模型来预测术后肺部感染风险。最后，使用独立队列对这些模型进行外部验证。通过受试者操作特征曲线下面积（AUC）评估模型性能。
在肾移植受者中，共有186人发生肺炎，其中训练队列中有144例，外部验证队列中有42例。六个机器学习模型预测术后肺部感染风险的AUC范围在训练队列中为0.758 - 0.822，在测试队列中为0.642 - 0.795。在评估的模型中，梯度提升机表现出最有利的预测准确性。
我们的研究开发了一种用于评估肾移植后肺部感染风险的预测模型，从而为肾移植受者的有效管理提供了有价值的基础。
