Prediction Scores for Risk of Allograft Loss in Patients Receiving Kidney Transplants: Nil Satis Nisi Optimum

Overview

Journal Clin Kidney J

Publisher Oxford University Press

Specialty Nephrology

Date 2020 Oct 30

PMID 33125003

Citations 2

Authors

Nuria Montero

Sergi Codina

Josep M Cruzado

Affiliations

Soon will be listed here.

Abstract

Long-term graft survival is the main concern of kidney transplantation. Some strategies have been tested to predict graft survival using estimated glomerular filtration rate or proteinuria at different time points, histologic assessment, non-invasive biomarkers or even machine-learning methods. However, the 'magical formulae' for allograft survival prediction does not exist yet.

Citing Articles

Molecular Markers of Kidney Transplantation Outcome: Current Omics Tools and Future Developments.

Lepoittevin M, Kerforne T, Pellerin L, Hauet T, Thuillier R Int J Mol Sci. 2022; 23(11).

PMID: 35682996 PMC: 9181061. DOI: 10.3390/ijms23116318.

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

Luo Y, Liang J, Hu X, Tang Z, Zhang J, Han L Front Med (Lausanne). 2022; 8:676461.

PMID: 35118080 PMC: 8804205. DOI: 10.3389/fmed.2021.676461.

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