Evaluation of predictive models for delayed graft function of deceased kidney transplantation

Huanxi Zhang, Linli Zheng, Shuhang Qin, Longshan Liu, Xiaopeng Yuan, Qian Fu, Jun Li, Ronghai Deng, Suxiong Deng, Fangchao Yu, Xiaoshun He, Changxi Wang

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Background: This study aimed to evaluate the predictive power of five available delayed graft function (DGF)-prediction models for kidney transplants in the Chinese population. Results: Among the five models, the Irish 2010 model scored the best in performance for the Chinese population. Irish 2010 model had an area under the receiver operating characteristic (ROC) curve of 0.737. Hosmer-Lemeshow goodnessof- fit test showed that the Irish 2010 model had a strong correlation between the calculated DGF risk and the observed DGF incidence (p = 0.887). When Irish 2010 model was used in the clinic, the optimal upper cut-off was set to 0.5 with the best positive likelihood ratio, while the lower cut-off was set to 0.1 with the best negative likelihood ratio. In the subgroup of donor aged ≤ 5, the observed DGF incidence was significantly higher than the calculated DGF risk by Irish 2010 model (27% vs. 9%). Materials and Methods: A total of 711 renal transplant cases using deceased donors from China Donation after Citizen's Death Program at our center between February 2007 and August 2016 were included in the analysis using the five predictive models (Irish 2010, Irish 2003, Chaphal 2014, Zaza 2015, Jeldres 2009). Conclusions: Irish 2010 model has the best predictive power for DGF risk in Chinese population among the five models. However, it may not be suitable for allograft recipients whose donor aged ≤ 5-year-old.

Original languageEnglish (US)
Pages (from-to)1735-1744
Number of pages10
JournalOncotarget
Volume9
Issue number2
DOIs
StatePublished - 2018

Keywords

  • Deceased kidney transplantation
  • Delayed graft function
  • Graft survival
  • Prediction models

ASJC Scopus subject areas

  • Oncology

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