Time-Effect of Donor and Recipient Characteristics on Graft Survival after Kidney Transplantation

Abstract

The kidney allocation system (KAS) is based on quality-based “longevity matching” strategies that provide only a momentary snapshot of expected outcomes at the time of transplantation. The purpose of our study was to define on a continuous timeline the relative and mutual interactions of donor and recipient characteristics on graft survival.

Total 39,108 subjects who underwent kidney transplant between October 25, 1999 and January 1, 2007 were identified in the United Network for Organ Sharing dataset. Our primary outcome was graft survival. Time-dependent receiver operating characteristic (ROC) curves and area under time-dependent ROC curve (AUC) were used to compare the predictive ability of the two allocation systems.

During the first year after transplantation, both donor and recipient models showed identical relevance. From the first to the sixth years, although the two ROC curves were nearly identical, the donor model outweighed the recipient model. Both models intersected again at the sixth year. From that time onward, the ROC curve for recipient characteristics model predominated over the ROC curve for donor characteristics model. The predictive value of the recipient model (AUC = 0.752) was greater than that of the donor model (AUC = 0.673)

We hope that this model will provide additional guidance and risk stratification to further optimize organ allocation based on the dynamic interaction of both donor and recipient characteristics over time.

Keywords

renal transplantation - kidney transplantation - KAS - UNOS - graft survival - organ procurement - recipient characteristics

Author Contribution

Study conception and design: JY, CLSM, and EPM. Acquisition of data: JY, CLSM, HD, RR, and EPM. Analysis and interpretation of data: JY, CLSM, JC, EG, HR, LWT, EPM. Drafting of manuscript: JY, CLSM, JC, HD, and EPM. Critical revision of the manuscript: CLSM, JC, EG, HR, LWT, EPM.

^* Both the authors have equal contribution.

• References

• 1 Organ Procurement and Transplantation Network (OPTN): KDRI to KDPI Mapping Table. Available from: https://optn.transplant.hrsa.gov/media/2150/kdpimapping_table.pdf [accessed 02.10.19]
• 2 Organ Procurement and Transplant Network. A Guide to Calculating and Interpreting the Kidney Donor Profile Index (KDPI). https://optn.transplant.hrsa.gov/media/1512/guide_to_calculating_interpreting_kdpi.pdf (13 February 2019, date last accessed)
• 3 Heilman RL, Green EP, Reddy KS, Moss A, Kaplan B. Potential impact of risk and loss aversion on the process of accepting kidneys for transplantation. Transplantation 2017; 101 (07) 1514-1517
• 4 Tversky A, Kahneman D. Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 1992; 5: 297-323
• 5 Hellemans R, Stel VS, Jager KJ, Bosmans JL, Abramowicz D. Do elderly recipients really benefit from kidney transplantation?. Transplant Rev (Orlando) 2015; 29 (04) 197-201
• 6 Clayton PA, McDonald SP, Snyder JJ, Salkowski N, Chadban SJ. External validation of the estimated posttransplant survival score for allocation of deceased donor kidneys in the United States. Am J Transplant 2014; 14 (08) 1922-1926
• 7 Rao PS, Schaubel DE, Guidinger MK. , et al. A comprehensive risk quantification score for deceased donor kidneys: the Kidney Donor Risk Index. Transplantation 2009; 88 (02) 231-236
• 8 Hariharan S, Johnson CP, Bresnahan BA, Taranto SE, McIntosh MJ, Stablein D. Improved graft survival after renal transplantation in the United States, 1988 to 1996. N Engl J Med 2000; 342 (09) 605-612
• 9 KDPI Calculator: Organ Procurement and Transplantation Network; https://optn.transplant.hrsa.gov/media/1512/guide_to_calculating_interpreting_kdpi.pdf . Accessed January 2019
• 10 Massie AB, Luo X, Lonze BE. , et al. Early changes in kidney distribution under the new allocation system. J Am Soc Nephrol 2016; 27 (08) 2495-2501
• 11 Lee AP, Abramowicz D. Is the Kidney Donor Risk Index a step forward in the assessment of deceased donor kidney quality?. Nephrol Dial Transplant 2015; 30 (08) 1285-1290
• 12 Massie AB, Luo X, Chow EK, Alejo JL, Desai NM, Segev DL. Survival benefit of primary deceased donor transplantation with high-KDPI kidneys. Am J Transplant 2014; 14 (10) 2310-2316
• 13 Merion RM, Ashby VB, Wolfe RA. , et al. Deceased-donor characteristics and the survival benefit of kidney transplantation. JAMA 2005; 294 (21) 2726-2733
• 14 Hart A, Smith JM, Skeans MA. , et al. OPTN/SRTR 2015 annual data report: kidney. Am J Transplant 2017; 17 (Suppl. 01) 21-116
• 15 Calvillo-Arbizu J, Pérez-Valdivia MA, Gentil-Govantes MA. , et al. Does the Kidney Donor Profile Index (KDPI) predict graft and patient survival in a Spanish population?. Nefrologia 2018; 38 (06) 587-595
• 16 Wachterman MW, Marcantonio ER, Davis RB. , et al. Relationship between the prognostic expectations of seriously ill patients undergoing hemodialysis and their nephrologists. JAMA Intern Med 2013; 173 (13) 1206-1214
• 17 Wilk A, Beck J, Kucheryavaya A. The Kidney Allocation System (KAS): The First Two Years. Richmond, Virginia: Organ Procurement and Transplantation Network (OPTN) Kidney Transplantation Committee; 2017
• 18 Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000; 56 (02) 337-44