Abstract
Background
Many factors cause kidney transplant graft failure. To identify at-risk patients and tailor treatment, failure risks must be accurately predicted. We are trying to predict the temporal progression of graft function (as treated with estimated glomerular filtration rate) using only pre- and post-transplantation data to develop a model that could be used clinically to help patient management.
Methods
We develop a method that integrates a dynamic model of glomerular filtration rate with machine learning and explicitly accounts for parameter uncertainty. The algorithm is trained on a cohort of 892 kidney transplant recipients and validated in an independent cohort of 847 recipients. The model uses routinely collected pre-transplant variables together with early post-operative graft function as inputs.
Results
Here we show that the model predicts the temporal evolution of graft function and identifies a threshold in estimated glomerular filtration rate that enables classification of high-risk patients. Clinical events following kidney transplantation are predicted using only a limited number of inputs, achieving a graft outcome prediction accuracy of 0.88 ± 0.04 (F1-score: 0.81 ± 0.06) for the first postoperative year. An accuracy of 0.85 ± 0.05 (F1-score: 0.79 ± 0.07) is also achieved for clinical events in the second postoperative year, thereby strengthening the reliability of the prediction methodology over time.
Conclusions
This study demonstrates that combining a dynamic filtration model with machine learning provides a robust basis for individualized prediction of graft outcomes after kidney transplantation and has the potential to be developed into a clinical decision-support tool for early identification of high-risk patients.
Plain Language Summary
After a kidney transplant, some kidneys work well for many years, while others slowly fail to work properly. We wanted to estimate each patient’s personal risk of their transplant failing using only a few routine measurements. We analysed medical records from people who had a kidney transplant and combined early blood tests of kidney function (estimated filtration rate, eGFR) with a computer model. The model learns each patient’s typical kidney function and finds a personal cut-off level below which the transplant is likely to fail. It predicted which kidneys would keep working over the next one to two years with about 85–90% accuracy. In future, this approach could help doctors spot high-risk patients early and tailor follow-up and treatment.
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Data availability
The raw, de-identified patient data with codes are available at Zenodo44. The raw, de-identified patient data generated and analyzed in this study are not publicly available due to ethical and legal restrictions related to patient confidentiality and institutional review board (IRB) regulations. Controlled access to the data may be granted for academic, non-commercial research purposes upon reasonable request and subject to approval by the corresponding author and the relevant institutional ethics committee. Requests should be directed to the corresponding author at [haralampos.hatzikirou@ku.ac.ae] and will typically be reviewed within 2–4 weeks. Access is granted under a data use agreement that prohibits re-identification of participants and redistribution of the data and restricts use to the approved research purpose.
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Acknowledgements
S. Kadah and N.S. Heinz from the Hannover Medical School (MHH) assisted with data management. The authors would like to thank S. Khailaie and M. Meyer Hermann for the fruitful discussions. H.H. have received funding from the Volkswagenstiftung and the “Life?” program (96732). H.H. has received funding from the Bundes Ministerium für Bildung und Forschung under the grant agreement No. 031L0237C (MiEDGE project/ERACOSYSMED). Finally, H.H. acknowledges the funding of the FSU grant 2021-2023 grant from Khalifa University. WG has received funding by the Bundesministerium für Bildung und Forschung (BMBF) under the frame of ERACoSysMed-2, the ERA-Net for Systems Medicine in clinical research and medical practice (project ROCKET, JTC2_29).
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Conceptualization, G.W. and H.H.; methodology, S.S., A.R. and H.H.; software, S.S. I.S. A.R. and H.H.; validation, S.S., H.H and G.W.; formal analysis, S.S., I.S., A.R., and H.H.; investigation, S.S., I.S., A.R., H.H., and G.W.; resources, I.S. and G.W.; data curation, S.S., A.R., I.S. and G.W.; writing—original draft preparation, S.S. I.S., A.R., H.H. and G.W.; writing—review and editing, S.S., I.H., A.R. H.H. and G.W.; visualization, S.S., I.H., A.R. H.H. and G.W.; supervision, H.H. and G.W.; project administration, H.H., and G.W.
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Savvopoulos, S.V., Scheffner, I., Reppas, A. et al. Identification of a personalized eGFR threshold improves the prediction of kidney failure risk after transplantation. Commun Med (2026). https://doi.org/10.1038/s43856-026-01538-1
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DOI: https://doi.org/10.1038/s43856-026-01538-1
