Zhang Lulu, Hua Shaokui, Zhang Yu, Jiang Yan, Huang Qunlian, Chang Baoyuan, Li Dengke
Department of Urology, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China.
The Second Affiliated Hospital of Wannan Medical College, Wuhu, 241000, Anhui, People's Republic of China.
Eur J Clin Microbiol Infect Dis. 2025 May 2. doi: 10.1007/s10096-025-05155-z.
Microbiological culture and drug susceptibility testing of urine samples have lengthy turnaround times, increasing the risk of extended-spectrum β-lactamase (ESBL)-positive urinary tract infection (UTI) patients progressing to sepsis.
To develop an efficient machine learning model for the identification of ESBL positivity in UTI patients.
This retrospective study included 528 samples that had undergone drug susceptibility testing, based on inclusion and exclusion criteria. Variables were screened using Lasso regression, with 70% of the samples used to construct nine machine learning models (XGBClassifier, LogisticRegression, LGBMClassifier, AdaBoostClassifier, SVC, MLPClassifier, ComplementNB, GaussianNB, and GradientBoostingClassifier). Model selection was based on criteria including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, Kappa score, and Area Under the Curve (AUC). The best model type was identified through ten-fold cross-validation, which was then built using the remaining 30% of the data as a test set. Interpretations of predictive results were provided using the SHAP model, clarifying the impact of each feature on predictions and enhancing model transparency and interpretability.
The variables selected by the Lasso regression model are as follows: gender + urinary protein + urobilinogen + leukocytes + occult blood + age + pH + specific gravity + leukocyte count + erythrocyte count + epithelial cell count + cast count.The XGBoost model outperformed others in ten-fold cross-validation, with scores on the validation set as follows: AUC (95%CI): 0.924 (0.860-0.989); cutoff: 0.664(0.637-0.690); accuracy: 0.862(0.839-0.885); sensitivity: 0.9(0.879-0.920); specificity: 0.725(0.618-0.832); PPV: 0.923(0.896-0.950); NPV: 0.667(0.626-0.707); F1 score: 0.911(0.896-0.925); Kappa: 0.603(0.527-0.679). The final model achieved an AUC of 0.968 and accuracy of 0.943 on the test set.
This study developed a rapid and efficient machine learning model capable of identifying ESBL positivity based solely on routine urine test data.
尿液样本的微生物培养和药敏试验周转时间长，增加了产超广谱β-内酰胺酶（ESBL）的尿路感染（UTI）患者进展为败血症的风险。
开发一种用于识别UTI患者ESBL阳性的高效机器学习模型。
本回顾性研究根据纳入和排除标准纳入了528份已进行药敏试验的样本。使用Lasso回归筛选变量，70%的样本用于构建九个机器学习模型（XGBClassifier、LogisticRegression、LGBMClassifier、AdaBoostClassifier、SVC、MLPClassifier、ComplementNB、GaussianNB和GradientBoostingClassifier）。模型选择基于准确性、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）、F1分数、Kappa分数和曲线下面积（AUC）等标准。通过十折交叉验证确定最佳模型类型，然后使用其余30%的数据作为测试集构建模型。使用SHAP模型对预测结果进行解释，阐明每个特征对预测的影响，提高模型的透明度和可解释性。
Lasso回归模型选择的变量如下：性别+尿蛋白+尿胆原+白细胞+潜血+年龄+pH值+比重+白细胞计数+红细胞计数+上皮细胞计数+管型计数。XGBoost模型在十折交叉验证中表现优于其他模型，验证集得分如下：AUC（95%CI）：0.924（0.860-0.989）；截断值：0.664（0.637-0.690）；准确性：0.862（0.839-0.885）；敏感性：0.9（0.879-0.920）；特异性：0.725（0.618-0.832）；PPV：0.923（0.896-0.950）；NPV：0.667（0.626-0.707）；F1分数：0.911（0.896-0.925）；Kappa：0.603（0.527-0.679）。最终模型在测试集上的AUC为0.968，准确性为0.943。
本研究开发了一种快速有效的机器学习模型，能够仅根据常规尿检数据识别ESBL阳性。
