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Abstract：
In order to provide decision-making support for the auxiliary diagnosis and individualized treatment of calculous pyonephrosis, the study aims to analyze the clinical features of the condition, investigate its risk factors, and develop a prediction model of the condition using machine learning techniques. A retrospective analysis was conducted on the clinical data of 268 patients with calculous renal pelvic effusion who underwent ultrasonography-guided percutaneous renal puncture and drainage in our hospital during January 2018 to December 2022. The patients were included into two groups, one for pyonephrosis and the other for hydronephrosis. At a random ratio of 7:3, the research cohort was split into training and testing data sets. Single factor analysis was utilized to examine the 43 characteristics of the hydronephrosis group and the pyonephrosis group using the T test, Spearman rank correlation test and chi-square test. Disparities in the characteristic distributions between the two groups in the training and test sets were noted. The features were filtered using the minimal absolute value shrinkage and selection operator on the training set of data. Auxiliary diagnostic prediction models were established using the following five machine learning (ML) algorithms: random forest (RF), xtreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosting decision trees (GBDT) and logistic regression (LR). The area under the curve (AUC) was used to compare the performance, and the best model was chosen. The decision curve was used to evaluate the clinical practicability of the models. The models with the greatest AUC in the training dataset were RF (1.000), followed by XGBoost (0.999), GBDT (0.977), and SVM (0.971). The lowest AUC was obtained by LR (0.938). With the greatest AUC in the test dataset going to GBDT (0.967), followed by LR (0.957), XGBoost (0.950), SVM (0.939) and RF (0.924). LR, GBDT and RF models had the highest accuracy were 0.873, followed by SVM, and the lowest was XGBoost. Out of the five models, the LR model had the best sensitivity and specificity is 0.923 and 0.887. The GBDT model had the highest AUC among the five models of calculous pyonephrosis developed using the ML, followed by the LR model. The LR model was considered be the best prediction model when combined with clinical operability. As it comes to diagnosing pyonephrosis, the LR model was more credible and had better prediction accuracy than common analysis approaches. Its nomogram can be used as an additional non-invasive diagnostic technique.
摘要：
为结石性脓肾的辅助诊断和个体化治疗提供决策支持,该研究旨在分析该疾病的临床特征，调查其危险因素，并利用机器学习技术建立条件预测模型。回顾性分析2018年1月至2022年12月在我院行超声引导下经皮肾穿刺引流术的268例结石性肾盂积液患者的临床资料。将患者分为两组,一个是脓肾，另一个是肾积水。以7：3的随机比例，将研究队列分为训练和测试数据集。采用单因素分析对肾积水组和肾积水组的43个特征进行T检验,Spearman秩相关检验和卡方检验。注意到训练集和测试集中两组之间特征分布的差异。在训练数据集上使用最小绝对值收缩和选择运算符来过滤特征。使用以下五种机器学习(ML)算法建立辅助诊断预测模型:随机森林(RF)、xtreme梯度提升(XGBoost)，支持向量机(SVM)，梯度提升决策树(GBDT)和逻辑回归(LR)。曲线下面积（AUC）用于比较性能，选择了最好的模型。利用决策曲线评价模型的临床实用性。训练数据集中AUC最大的模型是RF(1.000)，其次是XGBoost（0.999），GBDT（0.977），和SVM(0.971)。通过LR获得最低AUC(0.938)。测试数据集中最大的AUC为GBDT（0.967），其次是LR(0.957)，XGBoost(0.950),SVM(0.939)和RF(0.924)。LR,GBDT和RF模型的精度最高，为0.873，其次是SVM。最低的是XGBoost。在五个模型中，LR模型的敏感性和特异性最好，分别为0.923和0.887。在使用ML开发的五种结石性脓肾模型中，GBDT模型的AUC最高，其次是LR模型。LR模型被认为是与临床可操作性相结合的最佳预测模型。至于诊断脓肾，与常用分析方法相比,LR模型更可信,预测精度更高.它的列线图可以用作额外的非侵入性诊断技术。
