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. 2021 Dec 16;135(2):181-186.
doi: 10.1097/CM9.0000000000001867.

Predictive value of hypothermic machine perfusion parameters combined perfusate biomarkers in deceased donor kidney transplantation

Yuxi Qiao  1 , Chenguang Ding  1   2 , Yang Li  1   2 , Xiaohui Tian  1   2 , Puxun Tian  1   2 , Xiaoming Ding  1   2 , Heli Xiang  1   2 , Jin Zheng  1   2 , Wujun Xue  1   2
Affiliations

Predictive value of hypothermic machine perfusion parameters combined perfusate biomarkers in deceased donor kidney transplantation

Yuxi Qiao et al. Chin Med J (Engl). .

Abstract

Background: Delayed graft function (DGF) is the main cause of renal function failure after kidney transplantation. This study aims at investigating the value of hypothermic machine perfusion (HMP) parameters combined with perfusate biomarkers on predicting DGF and the time of renal function recovery after deceased donor (DD) kidney transplantation.

Methods: HMP parameters, perfusate biomarkers and baseline characteristics of 113 DD kidney transplantations from January 1, 2019 to August 31, 2019 in the First Affiliated Hospital of Xi'an Jiaotong University were retrospectively analyzed using univariate and multivariate logistic regression analysis.

Results: In this study, the DGF incidence was 17.7% (20/113); The multivariate logistic regression results showed that terminal resistance (OR: 1.879, 95% CI 1.145-3.56) and glutathione S-transferase (GST)(OR = 1.62, 95% CI 1.23-2.46) were risk factors for DGF; The Cox model analysis indicated that terminal resistance was an independent hazard factor for renal function recovery time (HR = 0.823, 95% CI 0.735-0.981). The model combining terminal resistance and GST (AUC = 0.888, 95% CI: 0.842-0.933) significantly improved the DGF predictability compared with the use of terminal resistance (AUC = 0.756, 95% CI 0.693-0.818) or GST alone (AUC = 0.729, 95% CI 0.591-0.806).

Conclusion: According to the factors analyzed in this study, the combination of HMP parameters and perfusate biomarkers displays a potent DGF predictive value.

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Conflict of interest statement

None.

Figures

Figure 1
Figure 1
Kaplan–Meier survival curves of the DGF and non-DGF groups. Log-Rank test was used to test the statistical significance between the two groups. DGF: Delayed graft function.
Figure 2
Figure 2
ROC of terminal resistance (A), GST (B) and the combined model (C) predicts DGF occurrence were shown respectively. The AUC of these three models implied that the combined model significantly improved prognostic efficacy. GST: Glutathione thiotransferase; DGF: Delayed graft function; AUC: Area under curve.
See this image and copyright information in PMC

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