Luo You, Tang Zuofu, Hu Xiao, Lu Shuo, Miao Bin, Hong Songlin, Bai Haiyun, Sun Chen, Qiu Jiang, Liang Huiying, Na Ning
Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.
Fane Data Technology Corporation, Tianjin 300384, China.
Ann Transl Med. 2020 Feb;8(4):82. doi: 10.21037/atm.2020.01.09.
Pneumonia accounts for the majority of infection-related deaths after kidney transplantation. We aimed to build a predictive model based on machine learning for severe pneumonia in recipients of deceased-donor transplants within the perioperative period after surgery.
We collected the features of kidney transplant recipients and used a tree-based ensemble classification algorithm (Random Forest or AdaBoost) and a nonensemble classifier (support vector machine, Naïve Bayes, or logistic regression) to build the predictive models. We used the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to evaluate the predictive performance via ten-fold cross validation.
Five hundred nineteen patients who underwent transplantation from January 2015 to December 2018 were included. Forty-three severe pneumonia episodes (8.3%) occurred during hospitalization after surgery. Significant differences in the recipients' age, diabetes status, HBsAg level, operation time, reoperation, usage of anti-fungal drugs, preoperative albumin and immunoglobulin levels, preoperative pulmonary lesions, and delayed graft function, as well as donor age, were observed between patients with and without severe pneumonia (P<0.05). We screened eight important features correlated with severe pneumonia using the recursive feature elimination method and then constructed a predictive model based on these features. The top three features were preoperative pulmonary lesions, reoperation and recipient age (with importance scores of 0.194, 0.124 and 0.078, respectively). Among the machine learning algorithms described above, the Random Forest algorithm displayed better predictive performance, with a sensitivity of 0.67, specificity of 0.97, positive likelihood ratio of 22.33, negative likelihood ratio of 0.34, AUROC of 0.91, and AUPRC of 0.72.
The Random Forest model is potentially useful for predicting severe pneumonia in kidney transplant recipients. Recipients with a potential preoperative potential pulmonary infection, who are of older age and who require reoperation should be monitored carefully to prevent the occurrence of severe pneumonia.
肺炎是肾移植术后感染相关死亡的主要原因。我们旨在基于机器学习建立一个预测模型，用于预测术后围手术期死者供体肾移植受者发生重症肺炎的情况。
我们收集了肾移植受者的特征，并使用基于树的集成分类算法（随机森林或自适应提升算法）和非集成分类器（支持向量机、朴素贝叶斯或逻辑回归）来建立预测模型。我们通过十折交叉验证，使用精确召回率曲线下面积（AUPRC）和受试者工作特征曲线下面积（AUROC）来评估预测性能。
纳入了2015年1月至2018年12月期间接受移植的519例患者。术后住院期间发生了43例重症肺炎（8.3%）。有重症肺炎和无重症肺炎的患者在受者年龄、糖尿病状态、乙肝表面抗原水平、手术时间、再次手术、抗真菌药物使用情况、术前白蛋白和免疫球蛋白水平、术前肺部病变以及移植肾功能延迟方面存在显著差异，供者年龄也有差异（P<0.05）。我们使用递归特征消除方法筛选出与重症肺炎相关的8个重要特征，然后基于这些特征构建了一个预测模型。最重要的三个特征是术前肺部病变、再次手术和受者年龄（重要性得分分别为0.194、0.124和0.078）。在上述机器学习算法中，随机森林算法表现出更好的预测性能，敏感性为0.67，特异性为0.97，阳性似然比为22.33，阴性似然比为0.34，AUROC为0.91，AUPRC为0.72。
随机森林模型可能有助于预测肾移植受者发生重症肺炎的情况。对于术前有潜在肺部感染可能、年龄较大且需要再次手术的受者，应进行密切监测，以预防重症肺炎的发生。
