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8/13/2026 9:18:04 AM | Browse: 5 | Download: 0
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Received |
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2026-02-26 02:58 |
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Peer-Review Started |
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2026-02-26 03:06 |
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First Decision by Editorial Office Director |
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2026-03-03 09:30 |
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Return for Revision |
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2026-03-03 09:30 |
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Revised |
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2026-03-31 01:24 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-04-16 02:35 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-04-16 06:46 |
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Articles in Press |
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2026-04-16 06:46 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-07-16 08:18 |
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Publish the Manuscript Online |
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2026-08-13 07:25 |
| 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 |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Editorial |
| Article Title |
Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Muhammad Abdul Mabood Khalil, Nihal Mohammed Sadagah, Jackson Tan and Salem H Al-Qurashi |
| Funding Agency and Grant Number |
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| Corresponding Author |
Muhammad Abdul Mabood Khalil, Center of Renal Diseases and Transplantation, King Fahad Armed Forces Hospital, Al Kurnaysh Br Road, Al Andalus, Jeddah 23311, Makkah al Mukarramah, Saudi Arabia. doctorkhalil1975@hotmail.com |
| Key Words |
Delayed graft function; Kidney transplantation; Predictive modeling; Machine learning; Logistic regression; Donor–recipient risk factors; Data quality; Clinical utility |
| Core Tip |
Delayed graft function significantly impacts kidney transplant outcomes, yet predicting it remains challenging. Recent evidence shows that machine learning (ML) models offer only modest improvements over the traditional logistic regression model when the dataset is of limited quality. High-quality, comprehensive data and interpretable models are critical for accurate risk stratification. Integrating ML with transparent statistical approaches may optimize predictive performance and support clinically meaningful decision-making in transplantation. |
| Publish Date |
2026-08-13 07:25 |
| Citation |
Khalil MAM, Sadagah NM, Tan J, Al-Qurashi SH. Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics? World J Nephrol 2026; 15(3): 120300
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| URL |
https://www.wjgnet.com/2220-6124/full/v15/i3/120300.htm |
| DOI |
https://doi.org/10.5527/wjn.120300 |
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