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Articles Published Processes
3/14/2026 4:49:31 AM | Browse: 1 | Download: 1
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Received |
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2025-11-24 04:23 |
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Peer-Review Started |
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2025-11-24 04:23 |
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First Decision by Editorial Office Director |
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2025-12-30 06:10 |
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Return for Revision |
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2025-12-30 06:10 |
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Revised |
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2026-01-12 22:02 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-02-09 02:41 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-02-09 08:49 |
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Articles in Press |
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2026-02-09 08:49 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-02-28 03:32 |
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Publish the Manuscript Online |
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2026-03-14 04:49 |
| 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. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Transplantation |
| Manuscript Type |
Retrospective Study |
| Article Title |
Prediction of graft outcomes after kidney transplantation: When standard statistics compare to machine learning techniques
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Carolina Salgado, Francisca Gonzalez Cohens, Felipe A Vera, Rocío Ruiz, Juan D Velasquez and Fernando M Gonzalez |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Agencia Nacional De Investigación Y Desarrollo |
No. ID23I10232 |
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| 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. |
| Publish Date |
2026-03-14 04:49 |
| 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 |
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