Leibovici L, Alpert G, Laor A, Kalter-Leibovici O, Danon Y L
Israel Defence Forces Medical Corps, Petah Tikva.
Arch Intern Med. 1989 Sep;149(9):2048-50.
To develop a clinical model for diagnosis of bacterial urinary tract infection, we conducted a prospective study on 266 dysuric young women, 147 of whom had urinary tract infections. Five variables were found to be significant and independent correlates to bacterial urinary tract infection on logistic regression analysis: sexual activity, absence of vaginal discharge, short duration of complaints, leukocyturia, and hematuria. An algorithm combining the logistic model and a Gram-stained urine specimen, which was used in only a third of our patients, afforded a sensitivity of 86% and a specificity of 84%. The algorithm was validated in a second set of 166 dysuric women, 77 of whom had urinary tract infections. The algorithm led to a diagnosis of bacterial urinary tract infection with a sensitivity of 91% and specificity of 79%; the only laboratory test needed except for urinalysis was a Gram's stain of urine, obtained for 30% of the patients.
为建立一种诊断细菌性尿路感染的临床模型，我们对266名排尿困难的年轻女性进行了一项前瞻性研究，其中147人患有尿路感染。在逻辑回归分析中发现有五个变量与细菌性尿路感染显著且独立相关：性活动、无阴道分泌物、症状持续时间短、白细胞尿和血尿。一种结合逻辑模型和革兰氏染色尿标本的算法（仅在三分之一的患者中使用），其敏感性为86%，特异性为84%。该算法在另一组166名排尿困难的女性中得到验证，其中77人患有尿路感染。该算法诊断细菌性尿路感染的敏感性为91%，特异性为79%；除尿液分析外唯一需要的实验室检查是对30%的患者进行尿液革兰氏染色。
