1. An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors
- Author
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Xiao-You Liu, Run-Tao Feng, Wen-Xiang Feng, Wei-Wei Jiang, Jian-An Chen, Guang-Li Zhong, Chao-Wei Chen, Zi-Jian Li, Jia-Dong Zeng, Ding Liu, Song Zhou, Jian-Min Hu, Guo-Rong Liao, Jun Liao, Ze-Feng Guo, Yu-Zhu Li, Si-Qiang Yang, Shi-Chao Li, Hua Chen, Ying Guo, Min Li, Li-Pei Fan, Hong-Yan Yan, Jian-Rong Chen, Liu-Yang Li, and Yong-Guang Liu
- Subjects
Pediatric kidney transplantation ,Machine learning ,Delayed graft function ,Predict ,DGF ,Medicine - Abstract
Abstract Background Kidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function (DGF), a common post-operative complication, may negatively impact the long-term outcomes of both the graft and the pediatric recipient. However, there is limited research on DGF in pediatric kidney transplant recipients. This study aims to develop a predictive model for the risk of DGF occurrence after pediatric kidney transplantation by integrating donor and recipient characteristics and utilizing machine learning algorithms, ultimately providing guidance for clinical decision-making. Methods This single-center retrospective cohort study includes all recipients under 18 years of age who underwent single-donor kidney transplantation at our hospital between 2016 and 2023, along with their corresponding donors. Demographic, clinical, and laboratory examination data were collected from both donors and recipients. Univariate logistic regression models and differential analysis were employed to identify features associated with DGF. Subsequently, a risk score for predicting DGF occurrence (DGF-RS) was constructed based on machine learning combinations. Model performance was evaluated using the receiver operating characteristic curves, decision curve analysis (DCA), and other methods. Results The study included a total of 140 pediatric kidney transplant recipients, among whom 37 (26.4%) developed DGF. Univariate analysis revealed that high-density lipoprotein cholesterol (HDLC), donor after circulatory death (DCD), warm ischemia time (WIT), cold ischemia time (CIT), gender match, and donor creatinine were significantly associated with DGF (P
- Published
- 2024
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