ArticleBMC medical research methodology2021
Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.BJS open · 2023Pooled it
- Predicting first-year kidney graft loss at the time of donor offer using machine learning and competing-risk analysis in a Latin American cohort.Clinical kidney journal · 2026Article
- Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.Journal of the American Society of Nephrology : JASN · 2026Review
- Dynamic Risk Prediction of Graft Failure after Deceased Donor Kidney Transplant.Clinical journal of the American Society of Nephrology : CJASN · 2026Article
- Assessing Deceased-Donor Kidneys Through Posttransplant Survival Prediction Algorithms.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2026Article
- Immunosuppressive regimens and long-term kidney transplant outcomes: A dual survival modeling framework.PloS one · 2026Article
- A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization.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 kidney graft function and failure among kidney transplant recipients.BMC medical research methodology · 2024Article
- Letter to the Editor: Kidney Transplantation and Obesity are There Any Differences in Outcomes?World journal of surgery · 2023Article
- Individual dynamic prediction and prognostic analysis for long-term allograft survival after kidney transplantation.BMC nephrology · 2022Article
- Transplant and Recipient Factors in Prediction of Kidney Transplant Outcomes: A UK-Wide Paired Analysis.Journal of clinical medicine · 2022Article
- Deceased Donor Characteristics and Kidney Transplant Outcomes.Transplant international : official journal of the European Society for Organ Transplantation · 2022Review
- Article
- Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database.Medicines (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKidney graft failure risk prediction models assist evidence-based medical decision-making in clinical practice. Our objective was to develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia.
methodsData included donor and recipient characteristics (n = 98) of 7,365 deceased donor transplants from January 1st, 2007 to December 31st, 2017 conducted in Australia. Seven variable selection methods were used to identify the most important independent variables included in the model. Predictive models were developed using: survival tree, random survival forest, survival support vector machine and Cox proportional regression. The models were trained using 70% of the data and validated using the rest of the data (30%). The model with best discriminatory power, assessed using concordance index (C-index) was chosen as the best model.
resultsTwo models, developed using cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure. The best fitting Cox model used seven independent variables and showed moderate level of prediction accuracy (calibration).
conclusionThis index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools.
Indexed as
Identifiers
What Socratic 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 Socratic graph.