Prediction of Uropathogens by Flow Cytometry and Dip-stick Test Results of Urine Through Multivariable Logistic Regression Analysis
Prediction of Uropathogens by Flow Cytometry and Dip-stick Test Results of Urine Through Multivariable Logistic Regression Analysis
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DOI：
10.1371/journal.pone.0227257
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发表时间：
2020-01-07
期刊：
影响因子：
3.7
通讯作者：
Yamanishi, Hachiro
中科院分区：
文献类型：
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作者：
Nakamura, Akihiro;Kohno, Aya;Yamanishi, Hachiro
目的多重耐药肠杆菌科细菌在尿路感染（UTI）中的广泛分布，其原因之一是广谱抗菌药物如氟喹诺酮类抗菌药物的过度使用。为了提高抗菌药物的管理，本研究旨在计算一个概率预测公式，以预测在真实的时间从试纸测试和流式细胞术的微生物菌株引起UTI。MethodologyWe检查了372个门诊现场尿液标本观察脓尿和菌尿试纸测试和流式细胞术。我们做了多项逻辑分析-以年龄和性别为解释变量，以每种菌株为响应变量，对11个测量项目进行回归分析，并对BACT散点图进行分析，计算概率预测公式。结果区分杆菌群和球菌群或多菌群的最佳预测公式是一个有5个解释变量的模型，该模型包括角区域内散点图点的百分比0-25度（P< 0.001）、性别（P< 0.001）、亚硝酸盐（P = 0.002）和酮（P = 0.133）。对于预测的临界值Y = 0.395，灵敏度为0.867，特异性为0.775（交叉验证组：灵敏度= 0.840，特异性= 0.760）。奇异变形杆菌和其他杆菌的最佳预测公式是0-20度角区域内散点图点百分比（P< 0.001）和亚硝酸盐（P = 0.090）模型。对于预测的截断值Y = 0.064，灵敏度为0.889，特异性为0.788（交叉验证组：灵敏度= 1.000，特异性= 0.766）。结论同时使用计算的概率预测公式与尿液分析结果有利于实时预测的有机体引起尿路感染，从而提供了有益的信息，经验性治疗。
PurposeMultidrug-resistant Enterobacteriaceae in urinary tract infection (UTI) has spread worldwide; one cause is overuse of broad-spectrum antimicrobial agents such as fluoroquinolone antibacterials. To improve antimicrobial agent administration, this study aimed to calculate a probability prediction formula to predict the organism strain causing UTI in real time from dip-stick testing and flow cytometry.MethodologyWe examined 372 outpatient spot urine samples with observed pyuria and bacteriuria using dip-stick testing and flow cytometry. We performed multiple logistic-regression analysis on the basis of 11 measurement items and BACT scattergram analysis with age and sex as explanatory variables and each strain as the response variable and calculated a probability prediction formula.ResultsThe best prediction formula for discrimination of the bacilli group and cocci or polymicrobial group was a model with 5 explanatory variables that included percentage of scattergram dots in an angular area of 0-25 degrees (P< 0.001), sex (P< 0.001), nitrite (P = 0.002), and ketones (P = 0.133). For a predicted cut-off value of Y = 0.395, sensitivity was 0.867 and specificity was 0.775 (cross-validation group: sensitivity = 0.840, specificity = 0.760). The best prediction formula for P. mirabilis and other bacilli was a model with percentage of scattergram dots in an angular area of 0-20 degrees (P< 0.001) and nitrite (P = 0.090). For a predicted cut-off value of Y = 0.064, sensitivity was 0.889 and specificity was 0.788 (cross-validation group: sensitivity = 1.000, specificity = 0.766).ConclusionSimultaneous use of the calculated probability prediction formula with urinalysis results facilitates real-time prediction of organisms causing UTI, thus providing helpful information for empiric therapy.