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An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors.
Liu, Xiao-You; Feng, Run-Tao; Feng, Wen-Xiang; Jiang, Wei-Wei; Chen, Jian-An; Zhong, Guang-Li; Chen, Chao-Wei; Li, Zi-Jian; Zeng, Jia-Dong; Liu, Ding; Zhou, Song; Hu, Jian-Min; Liao, Guo-Rong; Liao, Jun; Guo, Ze-Feng; Li, Yu-Zhu; Yang, Si-Qiang; Li, Shi-Chao; Chen, Hua; Guo, Ying; Li, Min; Fan, Li-Pei; Yan, Hong-Yan; Chen, Jian-Rong; Li, Liu-Yang; Liu, Yong-Guang.
Afiliação
  • Liu XY; Department of Organ Transplantation, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510163, China.
  • Feng RT; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Feng WX; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Jiang WW; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Chen JA; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Zhong GL; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Chen CW; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Li ZJ; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Zeng JD; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Liu D; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Zhou S; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Hu JM; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Liao GR; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Liao J; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Guo ZF; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Li YZ; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Yang SQ; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Li SC; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Chen H; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Guo Y; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Li M; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Fan LP; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Yan HY; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Chen JR; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Li LY; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
  • Liu YG; Department of Organ Transplantation, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China. liuyg168@smu.edu.cn.
BMC Med ; 22(1): 407, 2024 Sep 20.
Article em En | MEDLINE | ID: mdl-39304842
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 < 0.05). Based on these six features, the random forest model (mtry = 5, 75%p) exhibited the best predictive performance among 97 machine learning models, with the area under the curve values reaching 0.983, 1, and 0.905 for the entire cohort, training set, and validation set, respectively. This model significantly outperformed single indicators. The DCA curve confirmed the clinical utility of this model.

CONCLUSIONS:

In this study, we developed a machine learning-based predictive model for DGF following pediatric kidney transplantation, termed DGF-RS, which integrates both donor and recipient characteristics. The model demonstrated excellent predictive accuracy and provides essential guidance for clinical decision-making. These findings contribute to our understanding of the pathogenesis of DGF.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doadores de Tecidos / Transplante de Rim / Função Retardada do Enxerto / Aprendizado de Máquina Limite: Adolescent / Child / Child, preschool / Female / Humans / Infant / Male Idioma: En Revista: BMC Med Assunto da revista: MEDICINA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doadores de Tecidos / Transplante de Rim / Função Retardada do Enxerto / Aprendizado de Máquina Limite: Adolescent / Child / Child, preschool / Female / Humans / Infant / Male Idioma: En Revista: BMC Med Assunto da revista: MEDICINA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido