Literature DB >> 29661423

Living-Donor Kidney Transplantation: Donor-Recipient Function Correlation.

I Godinho1, J Guerra2, M J Melo2, M Neves2, J Gonçalves2, M A Santana2, A Gomes da Costa2.   

Abstract

BACKGROUND: With the rising prevalence of living-donor kidney transplantation, evaluation of factors correlated with renal function in the donor-recipient pair constitutes a main goal for kidney transplantation clinicians. Our objective was to analyze the more relevant donor characteristics that contribute to donor and recipient estimated glomerular filtration rates (eGFR) after 1 year.
METHODS: We evaluated 48 consecutive donor-recipient pairs from our unit.
RESULTS: Mean donor age was 46 ± 11 years, with 71% being women. Mean recipient age was 35 ± 12 years, with 54% being men. Mean duration of donor hospitalization was 7 ± 2 days. Donor eGFR was 104 ± 11 mL/min/1.73 m2 before donation and 70 ± 14 mL/min/1.73 m2 at discharge. After 1 year, donor eGFR was 71 ± 12 mL/min/1.73 m2 and recipient eGFR was 69 ± 10 mL/min/1.73 m2. Donor eGFR <100 mL/min/1.73 m2 before donation and age >50 years correlated with 17.7- and 8.9-fold increased risks, respectively, of recipient eGFR <60 mL/min/1.73 m2 after 1 year. Donor being female, although statistically associated with worse graft function, compared with a male donor (P = .020), did not represent a significantly increased risk of recipient eGFR <60 mL/min/1.73 m2. Higher donor body mass index (BMI) also associated with a lower kidney function for donors (P = .048). In multivariate linear regression to predict pairs' eGFRs after 1 year, only donor eGFR before donation and at discharge retained statistical significance (P ≤ .001 and P = .045, respectively).
CONCLUSIONS: Excluding unpredictable complications in the post-transplantation period, donor eGFR before donation, eGFR at discharge, and age were the best parameters to predict recipient and donor eGFRs after 1 year and can be used as a tool for managing expectations regarding the post-transplantation period.
Copyright © 2018 Elsevier Inc. All rights reserved.

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Year:  2018        PMID: 29661423     DOI: 10.1016/j.transproceed.2018.02.003

Source DB:  PubMed          Journal:  Transplant Proc        ISSN: 0041-1345            Impact factor:   1.066


  2 in total

1.  Prediction model of compensation for contralateral kidney after living-donor donation.

Authors:  Kenji Okumura; Shigeyoshi Yamanaga; Kosuke Tanaka; Kohei Kinoshita; Akari Kaba; Mika Fujii; Masatomo Ogata; Yuji Hidaka; Mariko Toyoda; Soichi Uekihara; Akira Miyata; Akito Inadome; Hiroshi Yokomizo
Journal:  BMC Nephrol       Date:  2019-07-26       Impact factor: 2.388

Review 2.  How good is a living donor? Systematic review and meta-analysis of the effect of donor demographics on post kidney transplant outcomes.

Authors:  Maria Irene Bellini; Mikhail Nozdrin; Liset Pengel; Simon Knight; Vassilios Papalois
Journal:  J Nephrol       Date:  2022-01-24       Impact factor: 3.902

  2 in total

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