Bayesian Modeling of Pretransplant Variables Accurately Predicts Kidney Graft Survival
Bayesian Modeling of Pretransplant Variables Accurately Predicts Kidney Graft Survival
复制标题
DOI：
10.1159/000345552
复制
发表时间：
2012-01-01
影响因子：
4.2
通讯作者：
Jindal, Rahul M.
中科院分区：
文献类型：
--
作者：
Brown, Trevor S.;Elster, Eric A.;Jindal, Rahul M.
简介：机器学习可以实现预测模型的开发，这些模型将多个变量纳入器官分配的系统方法。我们探讨了贝叶斯信念网络（BBN）的原理，以确定是否可以使用移植前变量推导出移植物存活的预测模型。我们的假设是，移植前供者和受者的变量，当一起考虑作为一个网络，增加价值的分类移植物存活。研究方法：我们对2000年至2001年期间从美国肾脏数据系统数据库中随机选择的5,144例患者（仅年龄死亡供体肾脏，首次接受者）进行了回顾性分析。使用这个数据集，我们开发了一个机器学习的BBN，作为移植前的器官匹配工具。结果如下：构建了一个包含48个临床变量的网络，并使用另外2，204名具有匹配人口统计学特征的患者进行了外部验证。该模型能够预测第一年或3年内的移植失败（灵敏度40%;特异性80%;曲线下面积，AUC，0.63）。BMI、性别、种族和供体年龄是移植前变量中与结果相关性最强的变量。10倍内部交叉验证显示1年（灵敏度24%;特异性80%; AUC 0.59）和3年（灵敏度31%;特异性80%; AUC 0.60）移植物衰竭的结果相似。结论：我们发现受者BMI、性别、种族和供体年龄是影响预后的预测因素，而等待时间和人类白细胞抗原匹配与预后的相关性要小得多。BBN使我们能够检查来自大型数据库的变量，以开发一个强大的预测模型。
Introduction: Machine learning can enable the development of predictive models that incorporate multiple variables for a systems approach to organ allocation. We explored the principle of Bayesian Belief Network (BBN) to determine whether a predictive model of graft survival can be derived using pretransplant variables. Our hypothesis was that pretransplant donor and recipient variables, when considered together as a network, add incremental value to the classification of graft survival. Methods: We performed a retrospective analysis of 5,144 randomly selected patients (age deceased donor kidney only, first-time recipients) from the United States Renal Data System database between 2000 and 2001. Using this dataset, we developed a machine-learned BBN that functions as a pretransplant organ-matching tool. Results: A network of 48 clinical variables was constructed and externally validated using an additional 2,204 patients of matching demographic characteristics. This model was able to predict graft failure within the first year or within 3 years (sensitivity 40%; specificity 80%; area under the curve, AUC, 0.63). Recipient BMI, gender, race, and donor age were amongst the pretransplant variables with strongest association to outcome. A 10-fold internal cross-validation showed similar results for 1-year (sensitivity 24%; specificity 80%; AUC 0.59) and 3-year (sensitivity 31%; specificity 80%; AUC 0.60) graft failure. Conclusion: We found recipient BMI, gender, race, and donor age to be influential predictors of outcome, while wait time and human leukocyte antigen matching were much less associated with outcome. BBN enabled us to examine variables from a large database to develop a robust predictive model.