Predictive models for assessing kidney allograft survival: A narrative review

  • M. Kolesnyk State Institution “O.O. Shalimov National Scientific Centre for Surgery and Transplantology of the National Academy of Medical Sciences of Ukraine,” Kyiv, Ukraine https://orcid.org/0000-0001-6658-3729
  • L. Korol State Institution “O.O. Shalimov National Scientific Centre for Surgery and Transplantology of the National Academy of Medical Sciences of Ukraine,” Kyiv, Ukraine https://orcid.org/0000-0002-5484-0326
  • N. Stepanova State Institution “O.O. Shalimov National Scientific Centre for Surgery and Transplantology of the National Academy of Medical Sciences of Ukraine,” Kyiv, Ukraine https://orcid.org/0000-0002-1070-3602
  • I. Shifris State Institution “O.O. Shalimov National Scientific Centre for Surgery and Transplantology of the National Academy of Medical Sciences of Ukraine,” Kyiv, Ukraine https://orcid.org/0000-0002-9413-7301
  • I. Shuba Educational Scientific Institute of High Technologies, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
DOI: https://doi.org/10.31450/ukrjnd.2(90).2026.10
Keywords: kidney transplantation, graft survival, delayed graft function, prognosis, risk assessment, predictive value of tests, machine learning.

Abstract

This review aimed to provide a clinically oriented evaluation of predictive models, scores, indices, nomograms and online calculators used for kidney allograft function and graft survival.

Methods. A targeted search was performed in PubMed/MEDLINE, Scopus, Embase, Web of Science Core Collection, Cochrane Library and Google Scholar. Priority was given to original model-development and validation studies, clinically used calculators, consensus documents and reviews addressing applicability, calibration and limitations of prognostic tools.

Results. Predictive instruments are most useful when applied according to the clinical time point: donor and recipient indices before transplantation; early functional markers and delayed graft function calculators during the perioperative period; eGFR, proteinuria, biopsy and immunological markers during the first year; and composite or dynamic models after the first year.

Conclusions. No single calculator can replace clinical judgment. The most practical strategy is a stepwise risk-oriented approach combining simple routine markers with validated and dynamically updated models.

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Колесник М.О, Степанова Н.М., Король Л.В., Шифріс І.М., Вороняк О.С., Кропельницький В.О., Красюк. Е.К., Коваль Т.О., Чепур Н.В. Основи трансплантаційної нефрології К.: ТОВ « Поліграф Плюс», 2025.-252 с.:ил. ISBN 978-617-7903-27.6


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Published
2026-06-10
How to Cite
Kolesnyk, M., Korol, L., Stepanova, N., Shifris, I., & Shuba, I. (2026). Predictive models for assessing kidney allograft survival: A narrative review. Ukrainian Journal of Nephrology and Dialysis, (2(90), 94-108. https://doi.org/10.31450/ukrjnd.2(90).2026.10
Issue
No 2(90) (2026): Ukrainian Journal of Nephrology and Dialysis
Section
Nephrology School

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