Literature DB >> 27418011

A postoperative 1-Year eGFR of More Than 45 ml/min May be the Cutoff Level for a Favorable Long-Term Prognosis in Renal Transplant Patients.

Chung Hee Baek1, Hyosang Kim1, Won Seok Yang1, Duck Jong Han1, Su-Kil Park1.   

Abstract

BACKGROUND One-year renal function after kidney transplantation (KT) classified by the Kidney Disease: Improving Global Outcomes (KDIGO) chronic kidney disease (CKD) staging has been reported to be associated with graft survival. However, the outcomes of KT are improving. Therefore, the distribution and prognostic value of 1-year estimated glomerular filtration rate (eGFR) in recently performed transplants were re-evaluated in this study. MATERIAL AND METHODS We reviewed all patients who received KT between 2008 and 2011 at our institution, and followed them until June 2015. The distribution of 1-year eGFR, graft survival according to CKD staging, the cutoff level for a favorable prognosis, and the occurrence of rejection and infection were analyzed. RESULTS A total of 758 patients were included in this study. Unlike previous studies, most patients (56.2%) were in the CKD stage 2 (eGFR 60-89) rather than stage 3 (eGFR 30-59). In addition, the CKD stage 3a (eGFR 45-59) group showed better graft survival than the CKD stage 3b (eGFR 30-44) group. However, CKD stage 2 and CKD stage 3a groups did not show significant differences in graft survival. Patients with postoperative 1-year eGFR ≥45 ml/min showed a more favorable outcome compared with those with postoperative 1-year eGFR <45 ml/min. One-year eGFR<45 ml/min, acute cellular rejection, antibody-mediated rejection, and CMV infection after 1 year were adjusted risk factors for graft failure. CONCLUSIONS A 1-year eGFR ≥45 ml/min may be the appropriate cutoff level for predicting favorable outcomes in KT. In addition, KDIGO CKD staging may no longer be useful in recently performed KT.

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Year:  2016        PMID: 27418011     DOI: 10.12659/aot.897938

Source DB:  PubMed          Journal:  Ann Transplant        ISSN: 1425-9524            Impact factor:   1.530


  1 in total

1.  Deep Learning Algorithms for the Prediction of Posttransplant Renal Function in Deceased-Donor Kidney Recipients: A Preliminary Study Based on Pretransplant Biopsy.

Authors:  You Luo; Jing Liang; Xiao Hu; Zuofu Tang; Jinhua Zhang; Lanqing Han; Zhanwen Dong; Weiming Deng; Bin Miao; Yong Ren; Ning Na
Journal:  Front Med (Lausanne)       Date:  2022-01-18
  1 in total

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