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, China (mainland).
Med Sci Monit. 2022 Jan 1;28:e933559. doi: 10.12659/MSM.933559.
BACKGROUND In an environment of limited kidney donation resources, patient recovery and survival after kidney transplantation (KT) are highly important. We used pre-operative data of kidney recipients to build a statistical model for predicting survivability after kidney transplantation. MATERIAL AND METHODS A dataset was constructed from a pool of patients who received a first KT in our hospital. For allogeneic transplantation, all donated kidneys were collected from deceased donors. Logistic regression analysis was used to change continuous variables into dichotomous ones through the creation of appropriate cut-off values. A regression model based on the least absolute shrinkage and selection operator (LASSO) algorithm was used for dimensionality reduction, feature selection, and survivability prediction. We used receiver operating characteristic (ROC) analysis, calibration, and decision curve analysis (DCA) to evaluate the performance and clinical impact of the proposed model. Finally, a 10-fold cross-validation scheme was implemented to verify the model robustness. RESULTS We identified 22 potential variables from which 30 features were selected as survivability predictors. The model established based on the LASSO regression algorithm had shown discrimination with an area under curve (AUC) value of 0.690 (95% confidence interval: 0.557-0.823) and good calibration result. DCA demonstrated clinical applicability of the prognostic model when the intervention progressed to the possibility threshold of 2%. An average AUC value of 0.691 was obtained on the validation data. CONCLUSIONS Our results suggest that the proposed model can predict the mortality risk for patients after kidney transplants and could help kidney specialists choose kidney recipients with better prognosis.
在肾脏捐献资源有限的情况下，肾移植（KT）后患者的恢复和生存至关重要。我们使用肾移植受者的术前数据构建了一个统计模型，用于预测肾移植后的生存率。
从我院接受首次 KT 的患者中构建了一个数据集。对于同种异体移植，所有捐献的肾脏均来自已故供体。通过创建适当的截断值，将逻辑回归分析用于将连续变量转换为二分类变量。基于最小绝对值收缩和选择算子（LASSO）算法的回归模型用于降维、特征选择和生存率预测。我们使用接收者操作特征（ROC）分析、校准和决策曲线分析（DCA）来评估所提出模型的性能和临床影响。最后，实施了 10 折交叉验证方案来验证模型的稳健性。
我们从 22 个潜在变量中确定了 30 个特征作为生存率预测因子。基于 LASSO 回归算法建立的模型具有区分能力，曲线下面积（AUC）值为 0.690（95%置信区间：0.557-0.823），校准效果良好。DCA 当干预进展到可能性阈值为 2%时，证明了预后模型的临床适用性。在验证数据上，平均 AUC 值为 0.691。
我们的研究结果表明，所提出的模型可以预测肾移植后患者的死亡风险，有助于肾科专家选择预后更好的肾移植受者。
