Department of Clinical Medicine, Faculty of Medicine, Federal University of Ceará, Fortaleza, Ceará, Brazil.
Walter Cantídio University Hospital, Fortaleza, Ceará, Brazil.
PLoS One. 2020 Feb 6;15(2):e0228597. doi: 10.1371/journal.pone.0228597. eCollection 2020.
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.
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.
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.
Some DMR variables were associated with DGF, suggesting a potential impact of variables reflecting poor clinical and hemodynamic status on the incidence of DGF.
本研究评估了在一个发病率较高的国家中延迟移植物功能（DGF）的风险因素，详细描述了与供者维护相关（DMR）的变量，并使用了机器学习（ML）方法，超越了传统的基于回归的模型。
回顾性分析了来自巴西两个中心的 443 例脑死亡已故供者肾移植（KT），使用预测模型评估了以下 DMR：动脉血气 pH 值、血清钠、血糖、尿量、平均动脉压、血管加压素的使用和心脏复苏逆转。
大多数患者（95.7%）接受了标准标准供者的肾脏。DGF 的发生率为 53%。在多变量逻辑回归分析中，DMR 变量对 DGF 的发生没有影响。在后分析中，仅包括冷缺血时间<21h 的 KT（n = 220），恢复手术前 24 小时的尿量（OR = 0.639，95%CI 0.444-0.919）和血清钠（OR = 1.030，95%CI 1.052-1.379）是 DGF 的危险因素。使用弹性网正则化回归模型和 ML 分析（决策树、神经网络和支持向量机），尿量和其他 DMR 变量成为 DGF 的预测因素：平均动脉压、≥1 或高剂量血管加压素和血糖。
一些 DMR 变量与 DGF 相关，这表明反映较差的临床和血液动力学状态的变量可能对 DGF 的发生率有影响。
