Background: 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.
Methods: 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.
Results: 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%.
Conclusions: 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.