SynthesisBJS open2023
Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.
Synthesis in BJS open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Mortality prediction of heart transplantation using machine learning models: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2025Pooled it
- Predicting long-term allograft outcomes in kidney transplant recipients using a machine learning approach: a 5-year retrospective cohort study.Renal failure · 2026Article
- Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?World journal of nephrology · 2026Article
- Integrating kidney imaging for risk prediction, therapeutic monitoring, and prognostication across the kidney disease spectrum: a review of emerging evidence.Clinical kidney journal · 2026Review
- Predictors of Long-Term Kidney Transplant Survival: A Scoping Review.Health science reports · 2026Review
- Application of machine learning in the research progress of post-kidney transplant rejection.World journal of transplantation · 2026Review
- Predictive Value of Hematologic Inflammatory Indices for Early Dialysis Requirement After Kidney Transplantation.Annals of transplantation · 2026Article
- Predicting kidney graft survival with a machine learning model based on for-cause biopsy transcriptomics.Scientific reports · 2026Article
- Review
- Landscape of current practices and future perspectives in living donor kidney donation in Europe: proceedings of a pan-European symposium by the European kidney transplant association section of the European Society for Organ Transplantation.Transplant international : official journal of the European Society for Organ Transplantation · 2026Article
- Dialysis vintage modifies the effect of adsorption-based therapies on protein-bound toxin clearance.Frontiers in cell and developmental biology · 2026Article
- Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation.Journal of clinical medicine · 2025Review
- Optimizing kidney allocation: challenges and solutions.Current opinion in organ transplantation · 2025Review
- Survival analysis using machine learning in transplantation: a practical introduction.BMC medical informatics and decision making · 2025Article
- Advancements in Artificial Intelligence for Kidney Transplantology: A Comprehensive Review of Current Applications and Predictive Models.Journal of clinical medicine · 2025Review
- Predicting graft survival in paediatric kidney transplant recipients using machine learning.Pediatric nephrology (Berlin, Germany) · 2025Article
- Predicting offer burden to optimize batch sizes in simultaneously expiring kidney offers.Frontiers in artificial intelligence · 2025Article
- Phase-specific kidney graft failure prediction with machine learning model.Frontiers in artificial intelligence · 2025Article
- Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor-Recipient Matching.Diagnostics (Basel, Switzerland) · 2024Article
- Exploring the variable importance in random forests under correlations: a general concept applied to donor organ quality in post-transplant survival.BMC medical research methodology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe variations in outcome and frequent occurrence of kidney allograft failure continue to pose important clinical and research challenges despite recent advances in kidney transplantation. The aim of this systematic review was to examine the current application of machine learning models in kidney transplantation and perform a meta-analysis of these models in the prediction of graft survival.
methodsThis review was registered with the PROSPERO database (CRD42021247469) and all peer-reviewed original articles that reported machine learning model-based prediction of graft survival were included. Quality assessment was performed by the criteria defined by Qiao and risk-of-bias assessment was performed using the PROBAST tool. The diagnostic performance of the meta-analysis was assessed by a meta-analysis of the area under the receiver operating characteristic curve and a hierarchical summary receiver operating characteristic plot.
resultsA total of 31 studies met the inclusion criteria for the review and 27 studies were included in the meta-analysis. Twenty-nine different machine learning models were used to predict graft survival in the included studies. Nine studies compared the predictive performance of machine learning models with traditional regression methods. Five studies had a high risk of bias and three studies had an unclear risk of bias. The area under the hierarchical summary receiver operating characteristic curve was 0.82 and the summary sensitivity and specificity of machine learning-based models were 0.81 (95 per cent c.i. 0.76 to 0.86) and 0.81 (95 per cent c.i. 0.74 to 0.86) respectively for the overall model. The diagnostic odds ratio for the overall model was 18.24 (95 per cent c.i. 11.00 to 30.16) and 29.27 (95 per cent c.i. 13.22 to 44.46) based on the sensitivity analyses.
conclusionPrediction models using machine learning methods may improve the prediction of outcomes after kidney transplantation by the integration of the vast amounts of non-linear data.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.