Research Papers:

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 and Changxi Wang _

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Oncotarget. 2018; 9:1735-1744. https://doi.org/10.18632/oncotarget.22711

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Huanxi Zhang1,*, Linli Zheng2,*, Shuhang Qin2,*, Longshan Liu1, Xiaopeng Yuan1, Qian Fu1, Jun Li1, Ronghai Deng1, Suxiong Deng1, Fangchao Yu1, Xiaoshun He1,3 and Changxi Wang1,3

1Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China

2Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China

3Guangdong Provincial Key Laboratory on Organ Donation and Transplant Immunology, Guangzhou 510080, China

*These authors contributed equally to this work

Correspondence to:

Changxi Wang, email: [email protected]

Xiaoshun He, email: [email protected]

Keywords: delayed graft function; graft survival; deceased kidney transplantation; prediction models

Received: September 16, 2017     Accepted: October 27, 2017     Published: November 27, 2017


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 goodness-of-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.

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