ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1682639
This article is part of the Research TopicEnhancing Kidney Transplant Outcomes through Machine Learning InnovationsView all 6 articles
Phase-Specific Kidney Graft Failure Prediction with Machine Learning Model
Provisionally accepted
- 1Shonan Kamakura General Hospital, Kamakura, Japan
- 2Technische Universitat Dresden, Dresden, Germany
- 3Al-Farabi Kazakh National University, Almaty, Kazakhstan
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Background: Accurate prediction of kidney graft failure at different phases post-transplantation is critical for timely intervention and long-term allograft preservation. Traditional survival models offer limited capacity for dynamic, time-specific risk estimation. Machine learning (ML) approaches, with their ability to model complex patterns, present a promising alternative. Methods: This study developed and dynamically evaluated phase-specific ML models to predict kidney graft failure across five post-transplant intervals: 0–3 months, 3–9 months, 9–15 months, 15– 39 months, and 39–72 months. Clinically relevant retrospective data from deceased donor kidney transplant recipients were used for training and internal validation, with performance further confirmed on a blinded external validation cohort. Predictive performance was assessed using ROC AUC, F1 score, and G-mean. Results: The ML models demonstrated varying performance across time intervals. Short-term predictions in the 0–3 month and 3–9 month intervals yielded moderate accuracy (ROC AUC = 0.73 ± 0.07 and 0.72 ± 0.04, respectively). The highest predictive accuracy observed in mid-term or the 9– 15-month window (ROC AUC = 0.92 ± 0.02; F1 score = 0.85 ± 0.03), followed by the 15–39-month period (ROC AUC = 0.84 ± 0.04; F1 score = 0.76 ± 0.04). Long-term prediction from 39–72 months was more challenging (ROC AUC = 0.70 ± 0.07; F1 score = 0.65 ± 0.06). Conclusion: Phase-specific ML models offer robust predictive performance for kidney graft failure, particularly in mid-term periods, supporting their integration into dynamic post-transplant surveillance strategies. These models can aid clinicians in identifying high-risk patients and tailoring follow-up protocols to optimize long-term transplant outcomes.
Keywords: Kidney Transplantation, Graft failure, machine learning, Deceased donor, Survival Prediction
Received: 09 Aug 2025; Accepted: 10 Sep 2025.
Copyright: © 2025 Salybekov, Wolfien, Yerkos, Buribayev, Hidaka and Kobayashi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Amankeldi A. Salybekov, Shonan Kamakura General Hospital, Kamakura, Japan
Markus Wolfien, Technische Universitat Dresden, Dresden, Germany
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