Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University, 710061, Xi'an, Shaanxi, China.
BMC Nephrol. 2024 Apr 19;25(1):138. doi: 10.1186/s12882-024-03557-3.
Delayed graft function (DGF) is an important complication after kidney transplantation surgery. The present study aimed to develop and validate a nomogram for preoperative prediction of DGF on the basis of clinical and histological risk factors.
The prediction model was constructed in a development cohort comprising 492 kidney transplant recipients from May 2018 to December 2019. Data regarding donor and recipient characteristics, pre-transplantation biopsy results, and machine perfusion parameters were collected, and univariate analysis was performed. The least absolute shrinkage and selection operator regression model was used for variable selection. The prediction model was developed by multivariate logistic regression analysis and presented as a nomogram. An external validation cohort comprising 105 transplantation cases from January 2020 to April 2020 was included in the analysis.
266 donors were included in the development cohort, 458 kidneys (93.1%) were preserved by hypothermic machine perfusion (HMP), 96 (19.51%) of 492 recipients developed DGF. Twenty-eight variables measured before transplantation surgery were included in the LASSO regression model. The nomogram consisted of 12 variables from donor characteristics, pre-transplantation biopsy results and machine perfusion parameters. Internal and external validation showed good discrimination and calibration of the nomogram, with Area Under Curve (AUC) 0.83 (95%CI, 0.78-0.88) and 0.87 (95%CI, 0.80-0.94). Decision curve analysis demonstrated that the nomogram was clinically useful.
A DGF predicting nomogram was developed that incorporated donor characteristics, pre-transplantation biopsy results, and machine perfusion parameters. This nomogram can be conveniently used for preoperative individualized prediction of DGF in kidney transplant recipients.
延迟移植物功能（DGF）是肾移植手术后的一个重要并发症。本研究旨在基于临床和组织学危险因素建立并验证一种预测 DGF 的列线图。
该预测模型在一个包含 492 例肾移植受者的开发队列中构建，这些受者来自 2018 年 5 月至 2019 年 12 月。收集了供者和受者特征、移植前活检结果和机器灌注参数的数据，并进行了单因素分析。使用最小绝对收缩和选择算子回归模型进行变量选择。通过多变量逻辑回归分析建立预测模型，并以列线图的形式呈现。分析中还包括一个来自 2020 年 1 月至 4 月的 105 例移植病例的外部验证队列。
开发队列中纳入了 266 例供者，492 例受者中 458 例（93.1%）采用低温机器灌注（HMP）保存，96 例（19.51%）发生 DGF。移植前手术测量的 28 个变量被纳入 LASSO 回归模型。该列线图由供者特征、移植前活检结果和机器灌注参数中的 12 个变量组成。内部和外部验证表明该列线图具有良好的区分度和校准度，AUC 分别为 0.83（95%CI，0.78-0.88）和 0.87（95%CI，0.80-0.94）。决策曲线分析表明该列线图具有临床实用性。
本研究建立了一种预测 DGF 的列线图，纳入了供者特征、移植前活检结果和机器灌注参数。该列线图可方便地用于预测肾移植受者的 DGF。
Zotero 插件
