Korean J Transplant.  2021 Jun;35(2):86-92. 10.4285/kjt.21.0011.

Development of predictive score for posttransplant survival based on pre-transplant recipient characteristics

• Koo TY
• Lee J
• Yang J

• KMID: 2517290
• DOI: http://doi.org/10.4285/kjt.21.0011

Abstract

Background
The new kidney allocation system in the United States has introduced longevity matching, which gives priority to allocating the best quality organs to wait-listed candidates with the longest predicted survival for the efficient utilization of organs that are of limited availability. The estimated post-transplant survival (EPTS) score was developed in the United States to risk-stratify all wait-listed patients. However, prognostic indices used in Western countries were derived from data that may be different for Korea and do not necessarily reflect prognostic values for Korean deceased donor kidney transplantation. Prognostic indices for Korean wait-listed candidates therefore need to be developed from Korean data.
Methods
We analyzed 6,731 adult solitary kidney transplant patients for candidate risk prediction using the national data from the Korean Network for Organ Sharing (KONOS) and National Health Insurance Data Sharing Service (NHISS). Cox regression analysis was used to model the risk of patient death.
Results
The Korean EPTS (K-EPTS) score was developed based on four recipient parameters (age, diabetes mellitus, hepatitis C virus, and duration of dialysis) that showed a significant association with post-transplant survival. K-EPTS scores showed good discrimination (C-statistics: 0.690; 95% confidence interval, 0.666–0.715). Moreover, the ability of the K-EPTS score to discriminate patient survival was better than that of the EPTS according to the criteria of the United Network for Organ Sharing (US-EPTS) score (P<0.001).
Conclusions
The K-EPTS score, which was developed based on Korean national data, is expected to contribute to the assessment of recipient prognosis and efficient utilization of deceased donor kidneys.

Keyword

Kidney transplantation; Prognosis; Survival; Transplant recipient

Figure

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  Fig. 1 Study profile. DDKT, deceased donor kidney transplantation; BMI, body mass index.

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  Fig. 2 Patient survival in deceased donor kidney transplantation by US-EPTS or K-EPTS scores. (A) The lower US-EPTS score group (≤20%) showed a significant survival benefit as compared with the higher US-EPTS score group (>20%) (P<0.001). (B) The lower K-EPTS score group (≤20%) showed a significant survival benefit as compared with the higher K-EPTS score group (>20%) (P<0.001). US-EPTS, estimated post-transplant survival according to the criteria of the United Network for Organ Sharing; K-EPTS, Korean estimated post-transplant survival.

Reference

1. United Network for Organ Sharing (UNOS). 2021. OPTN policy 3.5: allocation of deceased kidneys [Internet]. UNOS;Richmond, VA: Available from: https://optn.transplant.hrsa.gov/media/1200/optn_policies.pdf. cited 2021 Jun 21.

2. Organ Procurement and Transplantation Network (OPTN). 2012. Proposal to substantially revise the national kidney allocation system [Internet]. Department of Health and Human Services;Available from: http://optn.transplant.hrsa.gov/PublicComment/pubcommentPropSub_311.pdf. cited 2021 Jun 21.

3. Rao PS, Schaubel DE, Guidinger MK, Andreoni KA, Wolfe RA, Merion RM, et al. 2009; A comprehensive risk quantification score for deceased donor kidneys the kidney donor risk index. Transplantation. 88:231–6. DOI: 10.1097/TP.0b013e3181ac620b. PMID: 19623019.

4. Port FK, Bragg-Gresham JL, Metzger RA, Dykstra DM, Gillespie BW, Young EW, et al. 2002; Donor characteristics associated with reduced graft survival: an approach to expanding the pool of kidney donors. Transplantation. 74:1281–6. DOI: 10.1097/00007890-200211150-00014. PMID: 12451266.

5. Molnar MZ, Nguyen DV, Chen Y, Ravel V, Streja E, Krishnan M, et al. 2017; Predictive score for posttransplantation outcomes. Transplantation. 101:1353–64. DOI: 10.1097/TP.0000000000001326. PMID: 27391198. PMCID: PMC5219861.

6. Organ Procurement and Transplantation Network (OPTN). 2020. A guide to calculating and interpreting the Estimated Post-Transplant Survival (EPTS) score used in the kidney allocation system (KAS) [Internet]. Department of Health and Human Services;Available from: https://optn.transplant.hrsa.gov/media/1511/guide_to_calculating_interpreting_epts.pdf. cited 2021 Jun 21.

7. Friedewald JJ, Samana CJ, Kasiske BL, Israni AK, Stewart D, Cherikh W, et al. 2013; The kidney allocation system. Surg Clin North Am. 93:1395–406. DOI: 10.1016/j.suc.2013.08.007. PMID: 24206858.

8. Min SI, Ahn C, Han DJ, Kim SI, Chung SY, Lee SK, et al. 2015; To achieve national self-sufficiency: recent progresses in deceased donation in Korea. Transplantation. 99:765–70. DOI: 10.1097/TP.0000000000000412. PMID: 25226175.

9. Orlandi PF, Huang J, Fukagawa M, Hoy W, Jha V, Oh KH, et al. 2019; A collaborative, individual-level analysis compared longitudinal outcomes across the International Network of Chronic Kidney Disease (iNETCKD) cohorts. Kidney Int. 96:1217–33. DOI: 10.1016/j.kint.2019.07.024. PMID: 31570197.

10. Korean Network for Organ Sharing (KONOS). 2020. 2019 Annual data report [Internet]. KONOS;Seoul: Available from: http://konos.go.kr. cited 2021 Jun 21.

11. Pencina MJ, D'Agostino RB Sr. 2015; Evaluating discrimination of risk prediction models: the C statistic. JAMA. 314:1063–4. DOI: 10.1001/jama.2015.11082. PMID: 26348755.

12. Uno H, Cai T, Pencina MJ, D'Agostino RB, Wei LJ. 2011; On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med. 30:1105–17. DOI: 10.1002/sim.4154. PMID: 21484848. PMCID: PMC3079915.

13. Bae S, Massie AB, Thomas AG, Bahn G, Luo X, Jackson KR, et al. 2019; Who can tolerate a marginal kidney? Predicting survival after deceased donor kidney transplant by donor-recipient combination. Am J Transplant. 19:425–33. DOI: 10.1111/ajt.14978. PMID: 29935051. PMCID: PMC6309666.

14. Li B, Cairns JA, Johnson RJ, Watson CJ, Roderick P, Oniscu GC, et al. 2020; Equity-efficiency trade-offs associated with alternative approaches to deceased donor kidney allocation: a patient-level simulation. Transplantation. 104:795–803. DOI: 10.1097/TP.0000000000002910. PMID: 31403554. PMCID: PMC7147404.

15. Coca A, Arias-Cabrales C, Valencia AL, Burballa C, Bustamante-Munguira J, Redondo-Pachón D, et al. 2020; Validation of a survival benefit estimator tool in a cohort of European kidney transplant recipients. Sci Rep. 10:17109. DOI: 10.1038/s41598-020-74295-3. PMID: 33051519. PMCID: PMC7555860.

16. Clayton PA, McDonald SP, Snyder JJ, Salkowski N, Chadban SJ. 2014; External validation of the estimated posttransplant survival score for allocation of deceased donor kidneys in the United States. Am J Transplant. 14:1922–6. DOI: 10.1111/ajt.12761. PMID: 24903739.