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

Silvana Daher Costa, Luis Gustavo Modelli de Andrade, Francisco Victor Carvalho Barroso, Cláudia Maria Costa de Oliveira, Elizabeth De Francesco Daher, Paula Frassinetti Castelo Branco Camurça Fernandes, Ronaldo de Matos Esmeraldo and Tainá Veras de Sandes-Freitas

PLOS ONE, 2020, vol. 15, issue 2, 1-13

Abstract: 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

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0228597

DOI: 10.1371/journal.pone.0228597

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