Leibovici L, Greenshtain S, Cohen O, Wysenbeek A J
Department of Medicine B, Beilinson Medical Center, Petah Tiqva, Israel.
Arch Intern Med. 1992 Dec;152(12):2481-6.
Guidelines to show whether a patient hospitalized because of a urinary tract infection (UTI) has a severe infection, and whether he or she is at high risk for harboring a multiresistant pathogen, are scant. The aims of the present study were to find (1) clinical and laboratory variables known within 24 hours of admission that, combined in a logistic model, will point to a high or low probability of bacteremia and (2) variables that can be used to define patients at high risk for the subsequent isolation of a multiresistant uropathogen.
In a set of patients consecutively admitted to a department of medicine because of UTI, we compared bacteremic vs nonbacteremic patients, and patients with a multiresistant uropathogen vs others, on logistic regression analysis. The logistic models derived were validated in a second set of patients with UTI.
Among 247 patients with UTI (median age, 75 years), 80 of them with bacteremia, five factors were significantly and independently associated with bacteremia: serum creatinine level, leukocyte count, temperature, diabetes mellitus, and low serum albumin level. A logistic model incorporating those factors was used to divide the patients into three groups with increasing prevalence of bacteremia (6%, 39%, and 69%) and of death (3%, 6%, and 20%). Three factors were predictive of the subsequent isolation of a resistant uropathogen: use of antibiotics before admission, advanced age, and male gender. The combination of those factors was used to divide patients into two groups, with resistance to cefuroxime of 9% vs 28%, to gentamicin of 7% vs 20%, and to sulfamethoxazole-trimethoprim of 30% vs 50%. In a second set of 144 patients with UTI, the percentages of bacteremia in the three groups were 5%, 16%, and 55%, and those of death, 2%, 6%, and 17%. When divided by the second model, the resistance to cefuroxime in the two groups was 16% vs 30%; to gentamicin, 16% vs 28%; and to sulfamethoxazole-trimethoprim, 28% vs 59%.
If prospectively validated in other settings, the models can be used to define groups of patients with UTI at low and high risk for bacteremia, and to help in the choice of empiric antibiotic treatment.
关于因尿路感染（UTI）住院的患者是否患有严重感染以及是否有携带多重耐药病原体高风险的指南十分匮乏。本研究的目的是找出：（1）入院24小时内已知的临床和实验室变量，将这些变量纳入逻辑模型后，可提示菌血症发生概率的高低；（2）可用于定义后续分离出多重耐药尿路病原体高风险患者的变量。
在一组因UTI连续入住内科的患者中，我们通过逻辑回归分析比较了菌血症患者与非菌血症患者，以及携带多重耐药尿路病原体患者与其他患者。所推导的逻辑模型在第二组UTI患者中进行了验证。
在247例UTI患者（中位年龄75岁）中，80例有菌血症，五个因素与菌血症显著且独立相关：血清肌酐水平、白细胞计数、体温、糖尿病和低血清白蛋白水平。纳入这些因素的逻辑模型用于将患者分为三组，菌血症患病率（6%、39%和69%）和死亡率（3%、6%和20%）逐渐升高。三个因素可预测后续分离出耐药尿路病原体：入院前使用抗生素、高龄和男性。这些因素的组合用于将患者分为两组，对头孢呋辛的耐药率分别为9%和28%，对庆大霉素的耐药率分别为7%和20%，对复方新诺明的耐药率分别为30%和50%。在第二组144例UTI患者中，三组的菌血症百分比分别为5%、16%和55%，死亡率分别为2%、6%和17%。根据第二个模型分组时，两组对头孢呋辛的耐药率分别为16%和30%；对庆大霉素的耐药率分别为16%和28%；对复方新诺明的耐药率分别为28%和59%。
如果在其他环境中得到前瞻性验证，这些模型可用于定义UTI患者中菌血症低风险和高风险组，并有助于经验性抗生素治疗的选择。
