实用器官移植电子杂志

实用器官移植电子杂志 ›› 2023, Vol. 11 ›› Issue (5): 457-463.DOI: 10.3969/j.issn.2095-5332.2023.05.013

• 论著 • 上一篇    下一篇

基于逻辑回归算法的移植肾功能延迟恢复发生风险因素分析及预测模型的建立

陈剑霖 1 ，付睿 2 ，陈青 3 ，马浩铭 3 ，张利民 1 ，郭晖 1    

  1. 1. 华中科技大学同济医学院附 属同济医院器官移植研究所，器官移植教育部重点实验室，国家卫生健康委员会器官移植重点实验室，中国医学科学院器官移植重点实验室，湖北 武汉 430030 ；

    2. 武汉软件工程职业学院，湖北 武汉 430205 ；

    3. 武汉工程大学计算机科学与工程学院，湖北武汉 430205

  • 出版日期:2023-09-20 发布日期:2023-09-20
  • 基金资助:

    中国医学科学院中央级公益性科研院所基本科研业务费专项资金（2019PT320014） 

Risk factors analysis and predict model related to delayed graft function after kidney transplantation based on logistic regression 

Chen Jianlin1 ，Fu Rui2 ，Chen Qing3 ， Ma Haoming3 ， Zhang Limin1 ， Guo Hui1 .    

  1. 1. Institute of Organ Transplantation， Tongji Hospital， Tongji Medical College， Huazhong University of Science and Technology；Key Laboratory of Organ Transplantation， Ministry of Education; NHC Key Laboratory of Organ Transplantation; Key Laboratory of Organ Transplantation， Chinese Academy of Medical Sciences， Wuhan 430030，Hu Bei，China；

    2. Wuhan Vocational College of Software and Engineering， Wuhan 430205，Hu Bei，China；

    3.Wthan Institute of Tethnology， School of Computer Science & Engineering Wuhan 430205，Hu Bei，China. Corresponding author：Guo Hui，Email：zcguo@tjh.tjmu.edu.cn 

  • Online:2023-09-20 Published:2023-09-20

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摘要：

目的 利用机器学习算法探究与移植肾功能延迟恢复（delayed graft function，DGF）发生相关的风险因素并建立预测模型。方法 收集 2018 年 1 月至 2020 年 3 月华中科技大学附属同济医院器官移植研究所实施的公民逝世后捐献供肾和肾移植受者的临床资料以及供肾活检病理资料，通过贪心算法筛选与 DGF发生相关的因素的贡献度，再利用逻辑回归算法建立预测模型并利用模型精确度，受试者工作特性曲线下面积（area under the receiver operating characteristic curve，AUROC）对模型效果进行评估。结果 术后 DGF 的发生率为 21.9%。与术后 DGF 发生相关性较高的因素包括供者体型、末次尿素氮、冷缺血时间、供器官小动脉病变范围、慢性肾小管萎缩评分（ct）和慢性间质纤维化评分（ci）。使用上述因素建立预测模型，模型的AUROC 约为 0.71，预测准确率约为 0.73。结论 利用机器学习算法可以分析 DGF 发生的风险因素并建立预测模型，以供临床预测 DGF 的发生风险。 

关键词:

肾移植 , 逻辑回归 , 移植肾功能延迟恢复 , 预测模型

Abstract:

Objective To explore the risk factors related to delayed graft function （DGF） after kidney transplantation using machine learning algorithms and to establish a predictive model. Methods Clinical data of kidney transplant donors and recipients and pathological data of donor kidney biopsy from January 2018 to December 2020 at the Institute of Organ Transplantation of Tongji Hospital Affiliated to Tongji Medical College， Huazhong University of Science and Technology were collected. The contribution of factors related to DGF were calculated through greedy algorithm，and logistic regression predict model were fitted using the reduced data set. Performance of the models is assessed by accuracy and area under the receiver operating characteristic curve （AUROC）. Results The observed incidence of DGF was 21.9%. Risk factors that were highly correlated with DGF include donor body type， blood urea nitrogen， cold ischemia time， extent of artery lesions， as well as tubular atrophy score（ct） and interstitial fibrosisscores （ci）. The prediction model was established using the above tested factors，the AUROC of the predict model was 0.71， and the prediction accuracy was 0.73. Conclusion Machine learning algorithms can be used to analyze the risk factors of DGF occurrence and establish predictive models. 

Key words:

 , Kidney transplantation； Logistic regression； Delayed graft function； Predict model

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