You Luo1, Zuofu Tang1, Xiao Hu1, Shuo Lu1, Bin Miao1, Songlin Hong2, Haiyun Bai2, Chen Sun2, Jiang Qiu3, Huiying Liang4, Ning Na1. 1. Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China. 2. Fane Data Technology Corporation, Tianjin 300384, China. 3. Department of Kidney Transplantation, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China. 4. Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou 510623, China.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. 2020 Annals of Translational Medicine. All rights reserved.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. 2020 Annals of Translational Medicine. All rights reserved.
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