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The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis


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Made available in DSpace on 2020-12-12T01:14:19Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-02-01

Background: This 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. Methods: A 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. Results: Most 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. Conclusions: Some DMR variables were associated with DGF, suggesting a potential impact of variables reflecting poor clinical and hemodynamic status on the incidence of DGF.

Department of Clinical Medicine Faculty of Medicine Federal University of Ceará

Walter Cantídio University Hospital

Hospital Geral de Fortaleza

Department of Internal Medicine Universidade Estadual Paulista-UNESP

Department of Internal Medicine Universidade Estadual Paulista-UNESP

Tipo de Documento Artigo científico
Idioma Inglês
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