Phase-specific kidney graft failure prediction with machine learning model
Salybekov A.A. Wolfien M. Yerkos A. Buribayev Z. Hidaka S. Kobayashi S.
2025Frontiers Media SA
Frontiers in Artificial Intelligence
2025#8
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 to 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. Copyright
deceased donor , graft failure , kidney transplantation , machine learning , survival prediction
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Kidney Disease and Transplant Center, Shonan Kamakura General Hospital, Kamakura, Japan
Regenerative Medicine Division, Cell and Gene Therapy Department, Qazaq Institute of Innovative Medicine, Astana, Kazakhstan
Faculty of Medicine Carl Gustav Carus, Institute for Medical Informatics and Biometry, TUD Dresden University of Technology, Dresden, Germany
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden, Germany
Department of Computer Science, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Kidney Disease and Transplant Center
Regenerative Medicine Division
Faculty of Medicine Carl Gustav Carus
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)
Department of Computer Science
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