ArticleScientific reports2020
Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Delayed initiation or reduced initial dose of calcineurin-inhibitors for kidney transplant recipients at high risk of delayed graft function.The Cochrane database of systematic reviews · 2025Pooled it
- Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.BJS open · 2023Pooled it
- Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?World journal of nephrology · 2026Article
- Development and validation of a multidimensional machine learning model for predicting delayed graft function in deceased donor kidney transplantation.International urology and nephrology · 2026Article
- Exfoliated kidney cells from urine for non-invasive kidney transplant monitoring: A potential opportunity?Clinical and experimental nephrology · 2026Review
- Construction of a deep learning-based predictive model for delayed graft function in kidney transplantation.Current urology · 2026Article
- Prediction of delayed graft function after deceased donor kidney transplantation: development and temporal validation in a single-center cohort.BMC nephrology · 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
- Cost Impact of a Novel Pre-transplant Risk Assessment Tool for Early Acute Rejection in Kidney Transplant Patients.Journal of health economics and outcomes research · 2026Article
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach.JMIR formative research · 2025Article
- Predicting graft survival in paediatric kidney transplant recipients using machine learning.Pediatric nephrology (Berlin, Germany) · 2025Article
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor-Recipient Matching.Diagnostics (Basel, Switzerland) · 2024Article
- First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation.Journal of nephrology · 2024Article
- Artificial Intelligence Reporting Guidelines' Adherence in Nephrology for Improved Research and Clinical Outcomes.Biomedicines · 2024Review
- Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation.Journal of nephrology · 2023Review
- Explainable artificial intelligence to predict the risk of side-specific extraprostatic extension in pre-prostatectomy patients.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2022Article
- Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network.Journal of healthcare engineering · 2022Article
- Postoperative day 1 serum cystatin C level predicts postoperative delayed graft function after kidney transplantation.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) has shown its potential to improve patient care over the last decade. In organ transplantation, delayed graft function (DGF) remains a major concern in deceased donor kidney transplantation (DDKT). To this end, we harnessed ML to build personalized prognostic models to predict DGF. Registry data were obtained on adult DDKT recipients for model development (n = 55,044) and validation (n = 6176). Incidence rates of DGF were 25.1% and 26.3% for the development and validation sets, respectively. Twenty-six predictors were identified via recursive feature elimination with random forest. Five widely-used ML algorithms-logistic regression (LR), elastic net, random forest, artificial neural network (ANN), and extreme gradient boosting (XGB) were trained and compared with a baseline LR model fitted with previously identified risk factors. The new ML models, particularly ANN with the area under the receiver operating characteristic curve (ROC-AUC) of 0.732 and XGB with ROC-AUC of 0.735, exhibited superior performance to the baseline model (ROC-AUC = 0.705). This study demonstrates the use of ML as a viable strategy to enable personalized risk quantification for medical applications. If successfully implemented, our models may aid in both risk quantification for DGF prevention clinical trials and personalized clinical decision making.
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