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

Author

Listed:
  • 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
  • Tainá Veras de Sandes-Freitas

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

Suggested Citation

  • 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 Fe, 2020. "The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-13, February.
    • Handle: RePEc:plo:pone00:0228597
    DOI: 10.1371/journal.pone.0228597
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