Department of Blood Transfusion, Tai'an Hospital of Traditional Chinese Medicine, Tai'an City, China.
Department of Clinical Laboratory, Tai'an Central Hospital, Tai'an City, China.
Urol Int. 2021;105(9-10):786-791. doi: 10.1159/000511483. Epub 2021 May 19.
Urinary tract infections (UTIs) have been proven to be the most encountered bacterial infection in humans. We hope to establish a prediction model for significant bacteriuria by comprehensively analyzing the relevant parameters of age, gender, and urine automatic analysis data.
A retrospective study was performed at Tai'an Central Hospital. All samples included in the study were tested for urine culture and urine automatic analysis. Data analysis was conducted with the SPSS.
The binary logistic regression module is used to establish the forecast formula, which gender, age, leukocyte count, bacterial count, leukocyte esterase, and nitrite were included. Receiver operating characteristic (ROC) curves showed that the area under ROC curve (AUC) of the prediction model was 0.878, bigger than the AUCs of the other 6 independent variables. The sensitivity and specificity of prediction model were 61.68 and 95.98%, respectively. The positive and the negative predictive values of the predictive model are 87.13 and 85.02%, respectively.
The prediction formula obtained in our study can achieve good prediction effect for significant bacteriuria, which can effectively avoid the treatment delay or antibiotic abuse caused by the subjective judgment of doctors.
尿路感染（UTIs）已被证实是人类最常见的细菌感染。我们希望通过综合分析年龄、性别和尿液自动分析数据的相关参数，建立一个对显著菌尿症的预测模型。
在泰安市中心医院进行了回顾性研究。研究中包含的所有样本均进行了尿液培养和尿液自动分析检测。使用 SPSS 进行数据分析。
使用二元逻辑回归模块建立了包含性别、年龄、白细胞计数、细菌计数、白细胞酯酶和亚硝酸盐的预测公式。受试者工作特征（ROC）曲线显示，预测模型的 ROC 曲线下面积（AUC）为 0.878，大于其他 6 个独立变量的 AUC。预测模型的灵敏度和特异性分别为 61.68%和 95.98%。预测模型的阳性和阴性预测值分别为 87.13%和 85.02%。
我们研究中获得的预测公式可以对显著菌尿症实现良好的预测效果，可以有效避免医生主观判断导致的治疗延误或抗生素滥用。
