Tapak Leili, Hamidi Omid, Amini Payam, Poorolajal Jalal
Modeling of Noncommunicable Diseases Research Center, Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Department of Science, Hamedan University of Technology, Hamedan, Iran.
Healthc Inform Res. 2017 Oct;23(4):277-284. doi: 10.4258/hir.2017.23.4.277. Epub 2017 Oct 31.
Kidney transplantation is the best renal replacement therapy for patients with end-stage renal disease. Several studies have attempted to identify predisposing factors of graft rejection; however, the results have been inconsistent. We aimed to identify prognostic factors associated with kidney transplant rejection using the artificial neural network (ANN) approach and to compare the results with those obtained by logistic regression (LR).
The study used information regarding 378 patients who had undergone kidney transplantation from a retrospective study conducted in Hamadan, Western Iran, from 1994 to 2011. ANN was used to identify potential important risk factors for chronic nonreversible graft rejection.
Recipients' age, creatinine level, cold ischemic time, and hemoglobin level at discharge were identified as the most important prognostic factors by ANN. The ANN model showed higher total accuracy (0.75 vs. 0.55 for LR), and the area under the ROC curve (0.88 vs. 0.75 for LR) was better than that obtained with LR.
The results of this study indicate that the ANN model outperformed LR in the prediction of kidney transplantation failure. Therefore, this approach is a promising classifier for predicting graft failure to improve patients' survival and quality of life, and it should be further investigated for the prediction of other clinical outcomes.
肾移植是终末期肾病患者最佳的肾脏替代治疗方法。多项研究试图确定移植排斥反应的诱发因素；然而，结果并不一致。我们旨在使用人工神经网络（ANN）方法确定与肾移植排斥反应相关的预后因素，并将结果与逻辑回归（LR）所得结果进行比较。
本研究使用了1994年至2011年在伊朗西部哈马丹进行的一项回顾性研究中378例接受肾移植患者的信息。人工神经网络用于确定慢性不可逆移植排斥反应的潜在重要危险因素。
人工神经网络确定受者年龄、肌酐水平、冷缺血时间和出院时血红蛋白水平为最重要的预后因素。人工神经网络模型显示出更高的总准确率（逻辑回归为0.75对0.55），且ROC曲线下面积（逻辑回归为0.88对0.75）优于逻辑回归。
本研究结果表明，在预测肾移植失败方面，人工神经网络模型优于逻辑回归。因此，这种方法是预测移植失败以提高患者生存率和生活质量的一种有前景的分类方法，应进一步研究其对其他临床结局的预测作用。
