ArticleNPJ digital medicine2025
Enhancing post-kidney transplant prognostication: an interpretable machine learning approach for longitudinal outcome prediction.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?World journal of nephrology · 2026Article
- Randomized trial of electronic health record implemented AI risk prediction in kidney transplant care.NPJ digital medicine · 2026Article
- Evaluating deep learning sepsis prediction models in ICUs under distribution shift: a multi-centre retrospective cohort study.NPJ digital medicine · 2026Article
- Artificial Intelligence and Predictive Modelling for Precision Dosing of Immunosuppressants in Kidney Transplantation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Early prediction of plastic bronchitis in pediatric patients withFrontiers in cellular and infection microbiology · 2026Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Kidney transplantation offers life-extending treatment for patients with end-stage renal disease, yet long-term risks of graft loss and death persist. Traditional prediction models using only baseline data often fail to capture patients' evolving health status post-transplant. In this study, we propose a two-stage machine learning (ML) framework for dynamic, next-year risk prediction of graft loss and death, updated annually with newly available clinical and laboratory data. Using a multi-center cohort from the Swiss Transplant Cohort Study (STCS), we trained and evaluated five ML models across 13 years of follow-up, demonstrating that incorporating longitudinal data significantly improved predictive performance compared to baseline-only models. LightGBM achieved the strongest performance, with AUROC values up to 0.896 for graft loss and 0.797 for death. Our findings suggest that dynamic, interpretable ML models can enhance personalized risk stratification, offering a practical and scalable tool for guiding follow-up strategies and early interventions in kidney transplant recipients.
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Registered trials
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