Pan Jiashan, Liao Guiyi
Department of Urology, The First Affiliated Hospital of Anhui Medical University and Institute of Urology and Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei, Anhui, 230022, People's Republic of China.
Int J Gen Med. 2021 Nov 30;14:9103-9115. doi: 10.2147/IJGM.S331854. eCollection 2021.
Delayed graft function (DGF) is a major complication of kidney transplantation (KT), especially in patients receiving donor of decease (DD) KT. Therefore, the kidney donor pool is rare worldwide, it is critical to evaluate the risk coefficient of DGF using preoperative data of donors and recipients and provide a reference for clinical decision-making and resource allocation.
A total of 238 DD recipients were performed in our center. Finally, 211 patients were included. The clinical database was divided into 34 clinical blood indicators (CBIs) and 6 demographics indexes (DIs). CBIs and DIs were screened for variables with P<0.05 and demonstrated the best cut-off value using multivariable logistics regression. The selected CBIs were passed through the least absolute shrinkage and selection operator (LASSO) to obtain the predictive factors and synthesized into a Riskscore, forming a nomogram with the selected DIs. We used receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA) to verify the discrimination and clinical effects of this nomogram. Finally, 10-fold cross-validation was conducted internally to show the effect of the model.
The 34 CBIs of the database finally screened out 12 predictors, which were synthesized into Riskscore. The 6 DIs selected 3 variables. Riskscore and 3 DIswere constructed into a nomogram, and the ROC of the nomogram has an AUC value of 0.725. Calibration and DCA showed excellent verification effects on the nomogram. The 10-fold crossover internal validation also demonstrated the model's excellent discrepancy.
The nomogram has an excellent ability to predict DGF and provides an essential reference for decision-making and resource allocation in a clinical setting.
移植肾功能延迟恢复（DGF）是肾移植（KT）的主要并发症，尤其是在接受死亡供体（DD）肾移植的患者中。因此，在全球范围内肾脏供体库稀缺的情况下，利用供体和受体的术前数据评估DGF的风险系数，并为临床决策和资源分配提供参考至关重要。
本中心共进行了238例DD受体肾移植手术。最终纳入211例患者。临床数据库分为34项临床血液指标（CBI）和6项人口统计学指标（DI）。对CBI和DI进行筛选，选取P<0.05的变量，并通过多变量逻辑回归确定最佳截断值。将选定的CBI通过最小绝对收缩和选择算子（LASSO）获得预测因子，并合成风险评分（Riskscore），与选定的DI一起形成列线图。我们使用受试者工作特征曲线（ROC）、校准和决策曲线分析（DCA）来验证该列线图的辨别能力和临床效果。最后进行内部10倍交叉验证以显示模型的效果。
数据库中的34项CBI最终筛选出12个预测因子，并合成Riskscore。6项DI中选取了3个变量。将Riskscore和3项DI构建成列线图，该列线图的ROC曲线下面积（AUC）值为0.725。校准和DCA显示该列线图具有良好的验证效果。10倍交叉内部验证也证明了该模型具有出色的区分能力。
该列线图对DGF具有出色的预测能力，为临床决策和资源分配提供了重要参考。
