Predicting the outcome of renal transplantation
Predicting the outcome of renal transplantation
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DOI：
10.1136/amiajnl-2010-000004
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发表时间：
2012-03-01
影响因子：
6.4
通讯作者：
Hinrichs, Carl
中科院分区：
文献类型：
--
作者：
Lasserre, Julia;Arnold, Steffen;Hinrichs, Carl
目的肾移植显著提高了血液透析患者的存活率。然而，随着边缘器官比例的增加和免疫抑制的改善，有必要验证已建立的主要基于人类白细胞抗原匹配的分配系统是否仍能满足当今的需求。作者求助于机器学习技术，从供受者的数据中预测移植后1年受者的估计肾小球滤过率(EGFR)。设计使用移植时供者特有的特征来预测患者的EGFR。捐赠者的数据来自欧洲移植组织的数据库，而接受者的详细信息则来自Charite Campus Virchow-Klinikum的数据库。共有707例身体供者的肾移植被纳入。测量创建了两个独立的数据集，采用了以下特征
Objective Renal transplantation has dramatically improved the survival rate of hemodialysis patients. However, with a growing proportion of marginal organs and improved immunosuppression, it is necessary to verify that the established allocation system, mostly based on human leukocyte antigen matching, still meets today's needs. The authors turn to machine-learning techniques to predict, from donor-recipient data, the estimated glomerular filtration rate (eGFR) of the recipient 1 year after transplantation.Design The patient's eGFR was predicted using donorerecipient characteristics available at the time of transplantation. Donors' data were obtained from Eurotransplant's database, while recipients' details were retrieved from Charite Campus Virchow-Klinikum's database. A total of 707 renal transplantations from cadaveric donors were included.Measurements Two separate datasets were created, taking features with