TY - JOUR
T1 - Evaluation of predictive models for delayed graft function of deceased kidney transplantation
AU - Zhang, Huanxi
AU - Zheng, Linli
AU - Qin, Shuhang
AU - Liu, Longshan
AU - Yuan, Xiaopeng
AU - Fu, Qian
AU - Li, Jun
AU - Deng, Ronghai
AU - Deng, Suxiong
AU - Yu, Fangchao
AU - He, Xiaoshun
AU - Wang, Changxi
N1 - Publisher Copyright:
© Zhang et al.
PY - 2018/1/5
Y1 - 2018/1/5
N2 - 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.
AB - 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.
KW - Deceased kidney transplantation
KW - Delayed graft function
KW - Graft survival
KW - Prediction models
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U2 - 10.18632/oncotarget.22711
DO - 10.18632/oncotarget.22711
M3 - Article
C2 - 29416727
AN - SCOPUS:85040014418
SN - 1949-2553
VL - 9
SP - 1735
EP - 1744
JO - Oncotarget
JF - Oncotarget
IS - 2
ER -