Systematic review of predictive models for delayed graft function after kidney transplantation
10.12464/j.issn.1674-7445.2025306
- VernacularTitle:肾移植术后移植肾功能延迟恢复预测模型的系统评价
- Author:
Qimeng ZHU
1
;
Wei JIANG
1
;
Ying CHEN
1
;
Danfeng TANG
1
;
Yi XU
1
;
Jian SHI
1
Author Information
1. Kidney Transplantation Center of the Third Affiliated Hospital of Soochow University, the First People's Hospital of Changzhou, Changzhou 213000, China.
- Publication Type:SystematicReview
- Keywords:
Kidney transplantation;
Delayed graft function;
Predictive model;
Predictor;
Serum creatinine;
Cold ischemia time;
Bias risk;
Applicability
- From:
Organ Transplantation
2026;17(3):495-502
- CountryChina
- Language:Chinese
-
Abstract:
Objective To systematically review the studies on predictive models for delayed graft function (DGF) after kidney transplantation. Methods Databases including China Biology Medicine Database, China National Knowledge Infrastructure, Wanfang Database, VIP Database, PubMed, Web of Science and CINAHL were searched to collect studies on predictive models for DGF after kidney transplantation published from the establishment of each database to June 29, 2025. Two researchers screened the literatures according to the inclusion and exclusion criteria, evaluated the quality of the literatures using the prediction model risk of bias assessment tool (PROBAST), and conducted a meta-analysis of the common predictors of the models using R software. Results A total of 12 literatures were included, involving 14 predictive models with sample sizes ranging from 103 to 24 653 cases. Donor serum creatinine level, cold ischemia time, donor age and donor body mass index were the top four common predictors. All the predictive models were at high risk of bias and low in applicability. The results of meta-analysis showed that abnormal donor body mass index, advanced donor age, prolonged cold ischemia time and elevated donor serum creatinine level were all associated with an increased risk of DGF after transplantation (all P<0.01), but there was high heterogeneity among the studies. Fixed-effect model and random-effect model were used to re-pool the effect sizes separately. The results indicated that the fixed-effect model and random-effect model had good consistency in terms of donor body mass index, donor age and cold ischemia time, while there was a significant difference in the effect sizes of the two models for donor serum creatinine level. Conclusions The predictive models for DGF risk after kidney transplantation have good predictive performance, but the overall risk of bias is high. In the future, large-sample, multicenter and high-quality prospective clinical studies should be carried out to optimize the predictive models, so as to improve their predictive ability and clinical application value.
