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Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?
Muhammad Abdul Mabood Khalil, Nihal Mohammed Sadagah, Salem H Al-Qurashi, Renal Diseases and Transplantation Center, King Fahad Armed Forces Hospital, Jeddah 23311, Makkah al Mukarramah, Saudi Arabia
Jackson Tan, Department of Nephrology, RIPAS Hospital Brunei Darussalam, Bander Seri Begawan BA1712, Brunei Darussalam
Author contributions: Khalil MAM, Sadagah NM, Al-Qurashi SH, and Tan J planned and designed the outline of the manuscript; Khalil MAM wrote the manuscript; Sadagah NM, Tan J, and Al-Qurashi SH helped in the literature search and supported in writing; all authors read and agreed to the final manuscript.
Conflict-of-interest statement: All authors declare that they have no conflict of interest to disclose.
Corresponding author: Muhammad Abdul Mabood Khalil, FRCP, Renal Diseases and Transplantation Center, King Fahad Armed Forces Hospital, Al Kurnaysh Br Road, Al Andalus, Jeddah 23311, Makkah al Mukarramah, Saudi Arabia.
Received: February 26, 2026
Revised: March 31, 2026
Accepted: April 16, 2026
Published online: September 25, 2026
Processing time: 168 Days and 6.1 Hours
Revised: March 31, 2026
Accepted: April 16, 2026
Published online: September 25, 2026
Processing time: 168 Days and 6.1 Hours
Core Tip
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.