使用机器学习预测肾脏废弃。
Predicting Kidney Discard Using Machine Learning.
机构信息
Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL.
Center for Engineering and Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL.
出版信息
Transplantation. 2021 Sep 1;105(9):2054-2071. doi: 10.1097/TP.0000000000003620.
BACKGROUND
Despite the kidney supply shortage, 18%-20% of deceased donor kidneys are discarded annually in the United States. In 2018, 3569 kidneys were discarded.
METHODS
We compared machine learning (ML) techniques to identify kidneys at risk of discard at the time of match run and after biopsy and machine perfusion results become available. The cohort consisted of adult deceased donor kidneys donated between December 4, 2014, and July 1, 2019. The studied ML models included Random Forests (RF), Adaptive Boosting (AdaBoost), Neural Networks (NNet), Support Vector Machines (SVM), and K-nearest Neighbors (KNN). In addition, a Logistic Regression (LR) model was fitted and used for comparison with the ML models' performance.
RESULTS
RF outperformed other ML models. Of 8036 discarded kidneys in the test dataset, LR correctly classified 3422 kidneys, whereas RF correctly classified 4762 kidneys (area under the receiver operative curve [AUC]: 0.85 versus 0.888, and balanced accuracy: 0.681 versus 0.759). For the kidneys with kidney donor profile index of >85% (6079 total), RF significantly outperformed LR in classifying discard and transplant prediction (AUC: 0.814 versus 0.717, and balanced accuracy: 0.732 versus 0.657). More than 388 kidneys were correctly classified using RF. Including biopsy and machine perfusion variables improved the performance of LR and RF (LR's AUC: 0.888 and balanced accuracy: 0.74 versus RF's AUC: 0.904 and balanced accuracy: 0.775).
CONCLUSIONS
Kidneys that are at risk of discard can be more accurately identified using ML techniques such as RF.
背景
尽管肾脏供应短缺，但美国每年仍有 18%-20%的已故供体肾脏被丢弃。2018 年，有 3569 个肾脏被丢弃。
方法
我们比较了机器学习（ML）技术，以在匹配运行时以及活检和机器灌注结果可用时识别有丢弃风险的肾脏。该队列包括 2014 年 12 月 4 日至 2019 年 7 月 1 日期间捐赠的成年已故供体肾脏。所研究的 ML 模型包括随机森林（RF）、自适应增强（AdaBoost）、神经网络（NNet）、支持向量机（SVM）和 K-最近邻（KNN）。此外，还拟合了逻辑回归（LR）模型，并将其与 ML 模型的性能进行了比较。
结果
RF 优于其他 ML 模型。在测试数据集的 8036 个丢弃肾脏中，LR 正确分类了 3422 个肾脏，而 RF 正确分类了 4762 个肾脏（接收者操作特征曲线下面积 [AUC]：0.85 与 0.888，平衡准确性：0.681 与 0.759）。对于肾源评分指数>85%的肾脏（共 6079 个），RF 在分类丢弃和移植预测方面显著优于 LR（AUC：0.814 与 0.717，平衡准确性：0.732 与 0.657）。RF 正确分类了 388 多个肾脏。包含活检和机器灌注变量可提高 LR 和 RF 的性能（LR 的 AUC：0.888 和平衡准确性：0.74 与 RF 的 AUC：0.904 和平衡准确性：0.775）。
结论
可以使用 RF 等 ML 技术更准确地识别有丢弃风险的肾脏。
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