ArticlePloS one2020
The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Risk factors for delayed graft function in patients with kidney transplantation: a systematic review and meta-analysis.BMJ open · 2025Pooled it
- Outcomes of Kidney Transplants From Standard Criteria Donors With Diabetes: A Propensity Score-Matched Study.Kidney medicine · 2026Article
- 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
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Artificial Intelligence Reporting Guidelines' Adherence in Nephrology for Improved Research and Clinical Outcomes.Biomedicines · 2024Review
- Article
- Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation.Journal of nephrology · 2023Review
- Revisiting transplant immunology through the lens of single-cell technologies.Seminars in immunopathology · 2023Review
- The promise of machine learning applications in solid organ transplantation.NPJ digital medicine · 2022Review
- Artificial Intelligence-A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant.Journal of clinical medicine · 2021Article
- How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?Seminars in immunopathology · 2021Review
- Artificial intelligence and kidney transplantation.World journal of transplantation · 2021Review
- The Impact of Hypothermic Pulsatile Machine Perfusion Versus Static Cold Storage: A Donor-Matched Paired Analysis in a Scenario of High Incidence of Delayed Kidney Graft Function.Annals of transplantation · 2020Article
- Proposal of a new equation for estimating resting energy expenditure of acute kidney injury patients on dialysis: a machine learning approach.Nutrition & metabolism · 2020Article
- The association between the Kidney Donor Profile Index and one-year outcomes in Brazilian kidney transplant recipients of standard criteria donors.Jornal brasileiro de nefrologiaObservational
- Effectiveness of steroid-free immunosuppressive regimens after kidney transplantation: the pioneering experience of a brazilian center.Jornal brasileiro de nefrologiaArticle
- Comparative analysis of kidney transplant costs related to recovery of renal function after the procedure.Jornal brasileiro de nefrologiaArticle
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study evaluated the risk factors for delayed graft function (DGF) in a country where its incidence is high, detailing donor maintenance-related (DMR) variables and using machine learning (ML) methods beyond the traditional regression-based models.
methodsA total of 443 brain dead deceased donor kidney transplants (KT) from two Brazilian centers were retrospectively analyzed and the following DMR were evaluated using predictive modeling: arterial blood gas pH, serum sodium, blood glucose, urine output, mean arterial pressure, vasopressors use, and reversed cardiac arrest.
resultsMost patients (95.7%) received kidneys from standard criteria donors. The incidence of DGF was 53%. In multivariable logistic regression analysis, DMR variables did not impact on DGF occurrence. In post-hoc analysis including only KT with cold ischemia time<21h (n = 220), urine output in 24h prior to recovery surgery (OR = 0.639, 95%CI 0.444-0.919) and serum sodium (OR = 1.030, 95%CI 1.052-1.379) were risk factors for DGF. Using elastic net regularized regression model and ML analysis (decision tree, neural network and support vector machine), urine output and other DMR variables emerged as DGF predictors: mean arterial pressure, ≥ 1 or high dose vasopressors and blood glucose.
conclusionsSome DMR variables were associated with DGF, suggesting a potential impact of variables reflecting poor clinical and hemodynamic status on the incidence of DGF.
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