Predictive Value of Hypothermic Machine Perfusion Parameters Combined with Perfusate Biomarkers in Deceased Donor Kidney Transplantation
Delayed graft function (DGF) is a significant complication following kidney transplantation, often leading to prolonged hospitalization, increased healthcare costs, and adverse long-term outcomes. The ability to predict DGF accurately is crucial for optimizing post-transplant management and improving patient outcomes. This study investigates the predictive value of hypothermic machine perfusion (HMP) parameters combined with perfusate biomarkers in deceased donor (DD) kidney transplantation, focusing on their ability to forecast DGF and the time required for renal function recovery.
The study was conducted at the First Affiliated Hospital of Xi’an Jiaotong University, involving 113 DD kidney transplantations performed between January 1, 2019, and August 31, 2019. The incidence of DGF in this cohort was 17.7% (20/113), which is consistent with previously reported rates. The primary objective was to evaluate the predictive potential of HMP parameters and perfusate biomarkers in identifying DGF and determining the recovery time of renal function post-transplantation.
HMP is a widely used preservation technique for kidneys from deceased donors. It provides real-time data on perfusion parameters, which can offer insights into the quality of the donor kidney. The study analyzed several HMP parameters, including initial flux, terminal flux, initial pressure, terminal pressure, initial resistance, terminal resistance, and perfusion time. Among these, terminal resistance emerged as a significant predictor of DGF. The multivariate logistic regression analysis revealed that higher terminal resistance was associated with an increased risk of DGF (OR: 1.879, 95% CI 1.145–3.56). Additionally, terminal resistance was identified as an independent hazard factor for prolonged renal function recovery time (HR = 0.823, 95% CI 0.735–0.981).
Perfusate biomarkers were also evaluated for their predictive value. Glutathione S-transferase (GST) was found to be an independent risk factor for DGF (OR = 1.62, 95% CI 1.23–2.46). The study measured the concentrations of GST, N-acetyl-b-D-glucosaminidase (NAG), and interleukin-18 (IL-18) in the perfusate. While both GST and NAG concentrations were significantly higher in the DGF group compared to the non-DGF group, IL-18 did not show a significant difference between the two groups. The univariate logistic regression analysis confirmed that both GST and NAG were risk factors for DGF, but only GST remained significant in the multivariate analysis.
The study further explored the combined predictive value of HMP parameters and perfusate biomarkers. A model incorporating terminal resistance and GST demonstrated superior predictive efficacy for DGF compared to using either parameter alone. The area under the receiver operating characteristic curve (AUC) for the combined model was 0.888 (95% CI: 0.842–0.933), significantly higher than the AUCs for terminal resistance alone (0.756, 95% CI 0.693–0.818) and GST alone (0.729, 95% CI 0.591–0.806). The optimal cut-off value for the combined model was 0.344, with a diagnostic sensitivity of 83.3% and specificity of 79.5%.
The Kaplan-Meier survival curves illustrated the difference in renal function recovery time between the DGF and non-DGF groups. The recovery time in the DGF group was significantly longer (23.2 days) compared to the non-DGF group (5.3 days). The Cox proportional hazards model analysis confirmed that terminal resistance was an independent risk factor affecting the recovery time of renal allograft function.
The study’s findings highlight the importance of combining HMP parameters and perfusate biomarkers for predicting DGF and assessing the recovery time of renal function post-transplantation. While HMP parameters provide valuable information on the perfusion status and initial injury of the donor kidney, perfusate biomarkers offer insights into the cellular damage and inflammatory response. The combined model, which includes terminal resistance and GST, significantly improves the predictive accuracy for DGF, offering a more comprehensive assessment of donor kidney quality.
In conclusion, this study demonstrates that the combination of HMP parameters and perfusate biomarkers, particularly terminal resistance and GST, provides a robust predictive model for DGF in deceased donor kidney transplantation. The findings underscore the potential of this approach to enhance the evaluation of donor kidney quality, optimize organ allocation, and improve post-transplant outcomes. Future research with larger sample sizes and stratified analyses could further refine the predictive model and validate its clinical applicability.
doi.org/10.1097/CM9.0000000000001867
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