Prediction of graft outcomes after kidney transplantation: When standard statistics compare to machine learning techniques

doi:10.5527/wjn.v15.i1.116879

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World Journal of Nephrology

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2220-6124

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Nephrol. Mar 25, 2026; 15(1): 116879
Published online Mar 25, 2026. doi: 10.5527/wjn.v15.i1.116879

Table 2 Clinical characteristics of kidney transplants

Transplant feature	Summary statistics, mean ± SD (range)	Completeness (%)
Origin of the kidney	Local: 38.6%, national: 61.4%	83
Cold ischemia time (hours)	18.99 ± 6.05 (3.50, 40.15)	93
Warm ischemia time (minutes)	39.49 ± 11.57 (10, 90)	71

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Citation: Salgado C, Gonzalez Cohens F, Vera FA, Ruiz R, Velasquez JD, Gonzalez FM. Prediction of graft outcomes after kidney transplantation: When standard statistics compare to machine learning techniques. World J Nephrol 2026; 15(1): 116879
URL: https://www.wjgnet.com/2220-6124/full/v15/i1/116879.htm
DOI: https://dx.doi.org/10.5527/wjn.v15.i1.116879

Salgado C, Gonzalez Cohens F, Vera FA, Ruiz R, Velasquez JD, Gonzalez FM. Prediction of graft outcomes after kidney transplantation: When standard statistics compare to machine learning techniques. World J Nephrol 2026; 15(1): 116879 [PMID: 41884255 DOI: 10.5527/wjn.v15.i1.116879]

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