ArticleJournal of medical Internet research2021
Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.BJS open · 2023Pooled it
- Pooled it
- Artificial intelligence in preclinical nonhuman primate xenotransplantation: bridging the gap from data complexity to clinical precision.Clinical transplantation and research · 2026Review
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Construction of a deep learning-based predictive model for delayed graft function in kidney transplantation.Current urology · 2026Article
- Dynamic Risk Prediction of Graft Failure after Deceased Donor Kidney Transplant.Clinical journal of the American Society of Nephrology : CJASN · 2026Article
- Predicting 2-Year Early Allograft Failure After Kidney Transplant: A Perioperative Risk Model from the MOVER Cohort.Healthcare (Basel, Switzerland) · 2026Article
- Multimodal deep learning integration for predicting renal function outcomes in living donor kidney transplantation: a retrospective cohort study.International journal of surgery (London, England) · 2026Article
- Assessing Deceased-Donor Kidneys Through Posttransplant Survival Prediction Algorithms.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2026Article
- Advancements in Artificial Intelligence for Kidney Transplantology: A Comprehensive Review of Current Applications and Predictive Models.Journal of clinical medicine · 2025Review
- Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models.BMC medical informatics and decision making · 2025Article
- Predicting graft survival in paediatric kidney transplant recipients using machine learning.Pediatric nephrology (Berlin, Germany) · 2025Article
- Phase-specific kidney graft failure prediction with machine learning model.Frontiers in artificial intelligence · 2025Article
- Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models.Scientific reports · 2024Article
- First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation.Journal of nephrology · 2024Article
- A risk prediction model for efficient intubation in the emergency department: A 4-year single-center retrospective analysis.Journal of the American College of Emergency Physicians open · 2024Article
- A machine learning approach towards assessing consistency and reproducibility: an application to graft survival across three kidney transplantation eras.Frontiers in digital health · 2024Article
- NephroCAGE-German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study.JMIR research protocols · 2023Article
- Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4.BMC nephrology · 2023Article
- Indoleamine 2,3-Dioxygenase 1 (IDO1) in Kidney Transplantation: A Guardian against Rejection.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation.
objectiveThe aim of this study was to predict the risk of kidney graft failure across three temporal cohorts (within 1 year, within 5 years, and after 5 years following a transplant) based on donor and recipient characteristics. We analyzed a large data set comprising over 50,000 kidney transplants covering an approximate 20-year period.
methodsWe applied machine learning-based classification algorithms to develop prediction models for the risk of graft failure for three different temporal cohorts. Deep learning-based autoencoders were applied for data dimensionality reduction, which improved the prediction performance. The influence of features on graft survival for each cohort was studied by investigating a new nonoverlapping patient stratification approach.
resultsOur models predicted graft survival with area under the curve scores of 82% within 1 year, 69% within 5 years, and 81% within 17 years. The feature importance analysis elucidated the varying influence of clinical features on graft survival across the three different temporal cohorts.
conclusionsIn this study, we applied machine learning to develop risk prediction models for graft failure that demonstrated a high level of prediction performance. Acknowledging that these models performed better than those reported in the literature for existing risk prediction tools, future studies will focus on how best to incorporate these prediction models into clinical care algorithms to optimize the long-term health of kidney recipients.
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