Publication Name	World Journal of Nephrology
Manuscript ID	120300
Country	Saudi Arabia

Category	Transplantation
Manuscript Type	Editorial
Article Title	Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?
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
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.
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; In press

Received	2026-02-26 02:58
Peer-Review Started	2026-02-26 03:06
First Decision by Editorial Office Director	2026-03-03 09:30
Return for Revision	2026-03-03 09:30
Revised	2026-03-31 01:24
Publication Fee Transferred
Second Decision by Editor	2026-04-16 02:35
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-04-16 06:46
Articles in Press	2026-04-16 06:46
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2220-6124 (online)
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