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. 2019 May 17;24:273-290.
doi: 10.12659/AOT.913217.

Prediction of Three-Year Mortality After Deceased Donor Kidney Transplantation in Adults With Pre-Transplant Donor and Recipient Variables

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Free PMC article

Prediction of Three-Year Mortality After Deceased Donor Kidney Transplantation in Adults With Pre-Transplant Donor and Recipient Variables

Ysabell Schwager et al. Ann Transplant. .
Free PMC article

Abstract

BACKGROUND Prognostic models for 3-year mortality after kidney transplantation based on pre-transplant donor and recipient variables may avoid futility and thus improve donor organ allocation. MATERIAL AND METHODS There were 1546 consecutive deceased-donor kidney transplants in adults (January 1, 2000 to December 31, 2012) used to identify pre-transplant donor and recipient variables with significant independent influence on long-term survival (Cox regression modelling). Detected factors were used to develop a prognostic model for 3-year mortality in 1289 patients with follow-up of >3 years (multivariable logistic regression). The sensitivity and specificity of this model's prognostic ability was assessed with the area under the receiver operating characteristic curve (AUROC). RESULTS Highly immunized recipients [hazard ratio (HR: 2.579, 95% CI: 1.272-4.631], high urgency recipients (HR: 3.062, 95% CI: 1.294-6.082), recipients with diabetic nephropathy (HR: 3.471, 95% CI: 2.476-4.751), as well as 0, 1, or 2 HLA DR mismatches (HR: 1.349, 95% CI: 1.160-1.569) were independent and significant risk factors for patient survival. Younger recipient age ≤42.1 years (HR: 0.137, 95% CI: 0.090-0.203), recipient age 42.2-52.8 years (HR: 0.374, 95% CI: 0.278-0.498), recipient age 52.9-62.8 years (HR: 0.553, 95% CI: 0.421-0.723), short cold ischemic times ≤11.8 hours (HR: 0.602, 95% CI: 0.438-0.814) and cold ischemic times 11.9-15.3 hours (HR: 0.736, 95% CI: 0.557-0.962) reduced this risk independently and significantly. The AUROC of the derived model for 3-year post-transplant mortality with these variables was 0.748 (95% CI: 0.689-0.788). CONCLUSIONS Older, highly immunized or high urgency transplant candidates with anticipated longer cold ischemic times, who were transplanted with the indication of diabetic nephropathy should receive donor organs with no HLA DR mismatches to improve their mortality risk.

Conflict of interest statement

Conflicts of interest

None.

Figures

Figure 1
Figure 1
Shown is the patient study inclusion and exclusion flow chart.
Figure 2
Figure 2
Shown is the ROC curve of the proposed prognostic model for the prediction of 3-year mortality after kidney transplantation. The AUROC is 0.748 (AUROC 95% CI: 0.689–0.788, best Youden index: sensitivity of prediction: 50.8%; specificity of prediction: 86.1%; overall correctness of prediction: 68.5%). ROC – receiver operating characteristic; AUROC – area under the receiver operating characteristic curve.
Figure 3
Figure 3
Shown is the Kaplan-Meier curve demonstrating significantly worse long-term survival for those patients with a predicted risk of 3-year mortality greater than 15.7% (continuous line, n=214) as had been determined with the proposed prognostic model for 3-year mortality when compared to those patients with a lesser predicted risk of 3-year mortality in Study Cohort 2 (dotted line, n=1012) (P<0.001, log rank test). Patients with lacking data for variables that are contained in the proposed prognostic model have been excluded due to inability to calculate the predicted risk (n=63).
Figure 4
Figure 4
Shown are the Kaplan-Meier curves demonstrating statistically significant effects of the number of HLA-DR mismatches on patient survival (P<0.001, log rank test). Zero mismatches result in the red line, 1 mismatch in the green line and 2 mismatches in the blue line.

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References

    1. Merion RM, Ashby VB, Wolfe RA, et al. Deceased-donor characteristics and the survival benefit of kidney transplantation. JAMA. 2005;294(21):2726–33. - PubMed
    1. Gourishankar S, Grebe SO, Mueller TF. Prediction of kidney graft failure using clinical scoring tools. Clin Transplant. 2013;27(4):517–22. - PubMed
    1. Metzger RA, Delmonico FL, Feng S, et al. Expanded criteria donors for kidney transplantation. Am J Transplant. 2003;3(Suppl 4):114–25. - PubMed
    1. Cohen B, Smits JM, Haase B, et al. Expanding the donor pool to increase renal transplantation. Nephrol Dial Transplant. 2005;20(1):34–41. - PubMed
    1. Pieloch D, Dombrovskiy V, Osband AJ, et al. The Kidney Transplant Morbidity Index (KTMI): A simple prognostic tool to help determine outcome risk in kidney transplant candidates. Prog Transplant. 2015;25(1):70–76. - PubMed

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