DGF is a common complication after kidney transplant operations and is related to both short-term functional recovery and long-term survival of kidney transplantation. Yarlagadda et al.  systematically reviewed the definitions of DGF, concluding that the combination of Scr reduction and dialysis need comprised a reasonable definition. Our study adopted these views as the definition allowed simple identification of DGF. Consistent with the situation in America, the number of candidates waiting for kidney transplantation is also increasing annually in China. Given the strain on kidney resources, although the utilization of ECD is associated with increased cost and DGF , the inclusion of ECD has been recognized internationally. A systematic review by Tingle et al. confirmed that machine perfusion reduced the incidence of DGF ; thus, we used Lifeport machines for ECD kidney perfusion to address the risk as possible. In our study, ECD and DGF incidence were statistically correlated and the DGF incidence was reasonable with a proportional ECD.
The risk factors related to DGF can be divided into donor factors and recipient factors. The multiple interactions of these factors ultimately affect the recovery of graft function. Although numerous studies have assessed the causes of DGF, the results of these studies have not reached a consensus. In our study, prolonged CIT and WIT were most strongly associated with DGF, consistent with previous reports [11–18]. Additionally, longer duration of recipient pretransplant dialysis was likely to lead to DGF. Other donor predictive factors included BMI, cause of death, and terminal Scr.
While immunity induction is an important step before surgery to avoid acute rejection, the use of ATG induction remains controversial. ATG may be more likely to induce cytomegalovirus infections and hematological complications [19, 20]. Popat et al.  reported a lower DGF rate in the ATG-induced group among 45 patients in a single-center study. However, ATG induction did not reduce the risk of DGF in the study by de Sandes-Freitaseta’s . In our study, the use of ATG depended on the patient’s economic condition and the surgeon’s preferences since it costly; thus, this use was not predictive. The results of our study showed that donor factors were the main influencing factors of DGF, likely probably because the graft quality strongly affects renal function after transplantation. Terminal Scr is the most direct indicator of kidney quality. It is generally believed that the lower the value, the lower the incidence of DGF, as observed by Helfer  and in the present study.
Although long-term graft survival is expected, numerous complicated factors can cause graft loss. The relationship between DGF and deceased graft survival has been demonstrated recently [23–24]. In a 3-year DCD kidney registry analysis, Lim et al  reported that the recipients of DCD kidneys with DGF experienced a higher incidence of acute rejection and overall graft loss. Gill et al.  observed that the DGF-associated risk of graft failure was greatest in the first post-transplant year. A meta-analysis by Yarlagadda et al. verified the association between DGF, acute rejection, and graft survival. The present study performed patient follow-up in the training cohort for one year, with findings consistent with those reported by Gill et al.
Our nomogram model is a simple and visual prediction model of post-transplant factors. Maier et al. investigated the relationship between DGF and post-transplant indicators of neutrophil gelatinase-associated lipocalin (NGAL), reporting that early assessment of serum and urinary NGAL could predict DGF . In their retrospective cohort study, Cardinal et al.  used multivariate analysis to examine the predictors of DGF but did not distinguish the importance of each predictor. Irish et al.  applied a scoring system for predicting DGF of DD kidney transplantation, which was verified by a ROC curve. A previous scoring system is valuable; thus, we tried to incorporate as many variables as possible to create a nomogram suitable to the condition of the contributions in our country.
The present study applied a 10-fold cross-validation LASSO method to divide the data into 10 equal parts, with nine parts for the model and one part for validation. This process was repeated ten times to produce an accurate AUC.
Our model, in addition to identifying patients at higher risk of DGF before surgery, could also be used as a clinical tool to reduce the risk of DGF. CIT should be controlled when possible; for example, shortening the harvest and patient preoperative preparation times would reduce the CIT, which would help to decrease the incidence of DGF. More specifically, this model can be used as a strategy to select suitable donors and recipients by identifying reasonable matches between recipients’ conditions and CIT. Additionally, for high-risk DGF donors identified by the nomogram, the model can guide immunosuppression induction.
This study had some limitations. First was the limited sample size and data diversity since it was a single-center study. Second, the follow-up period of graft function was not long compared to 10–20 years. Finally, we did not provide solutions for predictors such as WIT. Future studies are needed to explore methods for shortening WIT and to investigate the influential factors of graft survival as prolongation of graft survival is our ultimate aim.