The scenario of delayed graft function in Brazil

Sandes‐Freitas, Tainá Veras de

doi:10.1590/2175-8239-jbn-2018-0221

Cited by 3 publications

(1 citation statement)

References 7 publications

(10 reference statements)

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“…All statistical analysis demonstrated that each additional hour matters, even in KT with CIT < 21h. The large Donor maintenance and its impact on delayed graft function territorial extension, the allocation model predominantly based on HLA compatibility, and the absence of specific allocation policies for "marginal" donors contribute to the long CIT in our country [25]. However, it is noteworthy that almost half of KT with CIT <21h presented DGF, suggesting the contribution of other factors beyond the CIT.…”

Section: Discussionmentioning

confidence: 80%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 80%