ArticlePloS one2021
A machine learning prediction model for waiting time to kidney transplant.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Outcomes for Frail Dialysis Patients Not Referred for Transplantation: A Cohort Study.Journal of the American Geriatrics Society · 2026Article
- Learning From Queues: Operational Analysis and Performance Evaluation of Dialysis Duration in a Paired Kidney Exchange With anLearning health systems · 2026Article
- Predictive Models for Kidney Offer Acceptance: Challenges and Strategies.Journal of transplantation · 2026Article
- Prediction model for probability of deceased donor kidney transplantation among transplant candidates in waiting list.BMC nephrology · 2025Article
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Predicting pediatric kidney transplant wait times: The unexplored landscape and pre-allocation transplant policies.Pediatric nephrology (Berlin, Germany) · 2024Article
- Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital.Translational pediatrics · 2023Article
- Continuation of immunosuppression vs. immunosuppression weaning in potential repeat kidney transplant candidates: a care management perspective.Frontiers in nephrology · 2023Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting waiting time for a deceased donor kidney transplant can help patients and clinicians to discuss management and contribute to a more efficient use of resources. This study aimed at developing a predictor model to estimate time on a kidney transplant waiting list using a machine learning approach.
methodsA retrospective cohort study including data of patients registered, between January 1, 2000 and December 31, 2017, in the waiting list of São Paulo State Organ Allocation System (SP-OAS) /Brazil. Data were randomly divided into two groups: 75% for training and 25% for testing. A Cox regression model was fitted with deceased donor transplant as the outcome. Sensitivity analyses were performed using different Cox models. Cox hazard ratios were used to develop the risk-prediction equations.
resultsOf 54,055 records retrieved, 48,153 registries were included in the final analysis. During the study period, approximately 1/3 of the patients were transplanted with a deceased donor. The major characteristics associated with changes in the likelihood of transplantation were age, subregion, cPRA, and frequency of HLA-DR, -B and -A. The model developed was able to predict waiting time with good agreement in internal validation (c-index = 0.70).
conclusionThe kidney transplant waiting time calculator developed shows good predictive performance and provides information that may be valuable in assisting candidates and their providers. Moreover, it can significantly improve the use of economic resources and the management of patient care before transplant.
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