Publication Name	World Journal of Nephrology
Manuscript ID	116879
Country	Chile

Category	Transplantation
Manuscript Type	Retrospective Study
Article Title	Prediction of graft outcomes after kidney transplantation: When standard statistics compare to machine learning techniques
Manuscript Source	Invited Manuscript
All Author List	Carolina Salgado, Francisca Gonzalez Cohens, Felipe A Vera, Rocío Ruiz, Juan D Velasquez and Fernando M Gonzalez
Funding Agency and Grant Number	Funding Agency Grant Number Agencia Nacional De Investigación Y Desarrollo No. ID23I10232
Corresponding Author	Fernando M Gonzalez, Department of Nephrology, Faculty of Medicine, Universidad de Chile, Avenida Salvador 486, Providencia, Santiago 7500922, Chile. fgonzalf@uc.cl
Key Words	Delayed graft function; Prediction; Logistic regression; Machine learning; Artificial intelligence
Core Tip	Machine learning (ML) is increasingly used in kidney transplantation research, including predicting delayed graft function. This study compares six ML models with logit across four donor, transplant, and recipient variable combinations. The dataset comprises 44.7% delayed graft function-positive cases. All methods have similar performances, with accuracies between 58%-70%. Important predictors included donor creatinine, age, and mean blood pressure, cold-ischemia time, and recipient smoking condition. Although ML approaches slightly outperformed logit, overall performance remained modest, likely due to limited sample-size. Further research should define dataset scale and quality for ML to become a primary analytic tool for predicting kidney transplant outcomes.
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; In press

Received	2025-11-24 04:23
Peer-Review Started	2025-11-24 04:23
First Decision by Editorial Office Director	2025-12-30 06:10
Return for Revision	2025-12-30 06:10
Revised	2026-01-12 22:02
Publication Fee Transferred
Second Decision by Editor	2026-02-09 02:41
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-02-09 08:49
Articles in Press	2026-02-09 08:49
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2220-6124 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Copyright	© The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com