DGF is one of the common postoperative complications after kidney transplantation and exerts a substantial effect on graft function and long-term graft survival [2, 3]. The occurrence of DGF was significantly reduced by changing the organ preservation strategy [12]. Therefore, building a reliable and accurate prediction model is particularly important to address the problems encountered by clinical decision-makers.

In our study, IL-2 was innovatively added to the prediction model. In 1983, IL-2 was discovered as an autocrine growth factor for cultured T cells [13]. IL-2 is involved in the proliferation of T cells and natural killer (NK) cells in the immune system, and may indirectly lead to kidney damage. First, weakening the ability of IL-2 to stimulate T cell proliferation has been shown to reduce ischemia–reperfusion injury (IRI) [14,15,16,17]. A study found that in the process of renal ischemia and reperfusion, IL-2 promotes NK cell proliferation, and NK cells directly kill renal tubular epithelial cells (TECs) [18]. Because renal parenchymal cells are mostly TECs, thus excessive apoptosis of TECs may lead to impaired renal function, indicating a potential link between NK cells and kidney injury. In addition to causing kidney damage by inducing the proliferation of immune cells, IL-2 has been reported to regulate TECs directly, leading to kidney injury. According to a previous study, IL-2 regulates cellular FLICE-inhibitory protein (C-FLIP) in TECs to increase the expression of endogenous caspase-8 in TECs, leading to TEC apoptosis and impaired renal function [19]. IL-2 is also widely used to treat immunodeficiency diseases, but the complications of impaired renal function often occur during treatment [20]. In our study, the level of IL-2 in the donor serum was positively correlated with DGF. This result may be related to the involvement of IL-2 in renal injury.

Creatinine is the product of phosphocreatine decomposition in muscle. It is produced at a fairly constant rate in the body, filtered freely through the glomerular membrane, and discharged almost completely through the kidney [21]. It is a clinically recognized sign of renal function [22]. The use of donor serum creatinine levels to evaluate renal function is a simple, efficient and cost-effective method. However, serum creatinine levels are influenced by other factors, such as diet, age, medications, and other factors, and the effects of other variables must be considered when evaluating kidney functions [23]. In our study, the donor serum creatinine level was included in the prediction model, and other risk factors were combined to improve the accuracy of the prediction. Similarly, the donor serum creatinine level was included in the KDRI model [8], and the model reported by Irish et al. [5].

Elevated blood glucose levels potentially lead to renal microvascular formation, glomerular basement membrane thickening, mesangial dilation, nodular glomerulosclerosis and tubulointerstitial fibrosis, which are the main pathological changes associated with diabetic nephropathy, and diabetes is the most common cause of ESRD [24]. Studies have shown that diabetes is a risk factor for AKI, and kidney damage directly increases the risk of DGF after transplantation [25]. Multiple studies have identified links between diabetes and inflammatory markers, such as tumor necrosis factor-α, interleukin-6, and C-reactive protein [26,27,28,29,30]. These inflammatory markers are independent risk factors for DGF and are associated with kidney injury [31,32,33]. An animal study found that elevated glucose levels in diabetic mice cause persistent kidney damage during warm ischemia–reperfusion, and diabetic mice are more prone to DGF than nondiabetic mice [34]. In our study, the number of donors with a history of diabetes in the DGF group was greater than that in the IGF group. In the multivariate analysis, a donor history of diabetes was an independent risk factor for DGF.

CIT is an important factor affecting the recovery of renal graft function, and a CIT reaching 12 h will increase the probability of primary graft failure and vascular complications [35]. Most studies have found that CIT is a susceptibility factor for renal injury and is closely related to the occurrence of DGF [36,37,38]. Thus, a shorter CIT may reduce the incidence of DGF and improve graft outcomes. In this study, the mean CIT (10.61 ± 2.82) in the DGF group was approximately 12 h, which represented a significant difference compared with the mean CIT (8.36 ± 2.27) of the IGF group. In the multivariate analysis, CIT was an independent risk factor for DGF. During the management of high-risk organs, CIT should be minimized as much as possible, and the occurrence of DGF might be reduced by controlling CIT within 12 h.

Jesper et al. [39] used four predictive models reported by Irish et al. [40], Jeldres et al. [41], Chapal et al. [6], and Zaza et al. [7] to predict the occurrence of DGF, and the range of the C-statistic was 0.567–0.761, indicating that different parameter combinations might improve the predictive value of DGF in different populations. In 2009, the KDRI was proposed by Rao et al. for graft assessment and decision-making using donor factors. Although KDRI reflects graft and patient survival, its AUC for predicting the occurrence of DGF in this study was 0.764, which is limited because it only included a few clinical factors affecting prognosis. Chapal et al. [6] published a simple DGFS based on a multicenter and prospective cohort in France. It has a good predictive capacity (AUC at 0.73). By calculating the five explanatory variables of the model, a patient with DGFS < -0.50 was predicted to have no DGF, and the accuracy rate was 88%. In contrast, among patients with a DGFS > 1.2, half will experience a DGF. Therefore, this model is widely used in Europe. In the present study, we found that donor terminal creatinine levels, a donor history of diabetes mellitus, CIT and donor IL-2 levels were related to the occurrence of DGF. The AUC, a measure of the diagnostic accuracy of risk factors for DGF, was 0.753, 0.655, 0.706 and 0.714 for donor terminal creatinine levels, a donor history of diabetes mellitus, CIT and donor IL-2 levels, respectively.). We constructed a nomogram using these four independent risk factors to improve the accuracy of the prediction. The predictive ability of the model (AUC = 0.894) was better than that of the KDRI and DGFS.

This study has several limitations. First, this study was performed at a single-center with a small sample size, and data from other centers have not been tested. Therefore, we hope that these findings will encourage external validation of the proposed model using data from more centers. In addition, this sample only covers Asian races, which has certain limitations in the process of promotion and application. Laboratory data are not dynamically observed and may cause bias. Finally, the scarce possibility of widespread use of the model due to the challenge represented by IL-2 determination in real life scenarios.

In summary, we constructed a DGF prediction model with high accuracy by including four factors (donor terminal creatinine levels, donor history of diabetes mellitus, CIT, and donor IL-2 levels) to provide clinicians with a useful tool that helps clinical decision-makers intervene more quickly and reduce the occurrence of DGF. IL-2 also participates in the kidney injury process and may be a potential marker of kidney injury.

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