Artificial Intelligence Laboratory, Departament of Computer Sciences, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; Faculty of Medical Sciences of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; IMUNOLAB - Laboratory of Histocompatibility, Belo Horizonte, Minas Gerais, Brazil.
IMUNOLAB - Laboratory of Histocompatibility, Belo Horizonte, Minas Gerais, Brazil; Faculty of Hospital Santa Casa, Belo Horizonte, Minas Gerais, Brazil.
Transpl Immunol. 2024 Aug;85:102057. doi: 10.1016/j.trim.2024.102057. Epub 2024 May 24.
Despite significant progress over the last decades in the survival of kidney allografts, several risk factors remain contributing to worsening kidney function or even loss of transplants. We aimed to evaluate a new machine learning method to identify these variables which may predict the early graft loss in kidney transplant patients and to assess their usefulness for improving clinical decisions.
A retrospective cohort study was carried out with 627 kidney transplant patients followed at least three months. All these data were pre-processed, and their selected features were used to develop an automatically working a machine learning algorithm; this algorithm was then applied for training and parameterization of the model; and finally, the tested model was then used for the analysis of patients' features that were the most impactful for the prediction of clinical outcomes. Our models were evaluated using the Area Under the Curve (AUC), and the SHapley Additive exPlanations (SHAP) algorithm was used to interpret its predictions.
The final selected model achieved a precision of 0.81, a sensitivity of 0.61, a specificity of 0.89, and an AUC value of 0.84. In our model, serum creatinine levels of kidney transplant patients, evaluated at the hospital discharge, proved to be the most important factor in the decision-making for the allograft loss. Patients with a weight equivalent to a BMI closer to the normal range prior to a kidney transplant are less likely to experience graft loss compared to patients with a BMI below the normal range. The age of patients at transplantation and Polyomavirus (BKPyV) infection had significant impact on clinical outcomes in our model.
Our algorithm suggests that the main characteristics that impacted early allograft loss were serum creatinine levels at the hospital discharge, as well as the pre-transplant values such as body weight, age of patients, and their BKPyV infection. We propose that machine learning tools can be developed to effectively assist medical decision-making in kidney transplantation.
尽管在过去几十年中，肾移植的存活率有了显著提高，但仍有一些风险因素导致肾功能恶化甚至移植失败。我们旨在评估一种新的机器学习方法，以识别这些可能预测肾移植患者早期移植物丢失的变量，并评估其对改善临床决策的有用性。
对至少随访 3 个月的 627 例肾移植患者进行回顾性队列研究。对所有这些数据进行预处理，并使用所选特征开发自动机器学习算法；然后将该算法应用于模型的训练和参数化；最后，使用测试模型分析对预测临床结局最有影响的患者特征。使用曲线下面积（AUC）评估我们的模型，使用 SHapley Additive exPlanations（SHAP）算法解释其预测。
最终选择的模型达到了 0.81 的精度、0.61 的敏感性、0.89 的特异性和 0.84 的 AUC 值。在我们的模型中，出院时评估的肾移植患者血清肌酐水平被证明是决定移植物丢失的最重要因素。与 BMI 低于正常值范围的患者相比，BMI 更接近正常范围的患者在移植前体重更有可能经历移植物丢失。患者的年龄和多瘤病毒（BKPyV）感染在我们的模型中对临床结局有显著影响。
我们的算法表明，影响早期移植物丢失的主要特征是出院时的血清肌酐水平，以及移植前的体重、患者年龄和 BKPyV 感染等指标。我们提出可以开发机器学习工具来有效协助肾移植中的医学决策。
