Predictive factors for multidrug-resistant gram-negative bacteria among hospitalised patients with complicated urinary tract infections - Aina Gomila; Evelyn Shaw; Jordi Carratalà; Leonard Leibovici; Cristian Tebé; Irith Wiegand; Laura Vallejo-Torres; Joan M. Vigo; Stephen Morris; Margaret Stoddart; Sally Grier; Christiane Vank; et al. (Antimicrobial Resistance and Infection Control, 2018)
Abstract Background Patients with complicated urinary tract infections (cUTIs) frequently receive broad-spectrum antibiotics. We aimed to determine the prevalence and predictive factors of multidrug-resistant gram-negative bacteria in patients with cUTI. Methods This is a multicenter, retrospective cohort study in south and eastern Europe, Turkey and Israel including consecutive patients with cUTIs hospitalised between January 2013 and December 2014. Multidrug-resistance was defined as non-susceptibility to at least one agent in three or more antimicrobial categories. A mixed-effects logistic regression model was used to determine predictive factors of multidrug-resistant gram-negative bacteria cUTI. Results From 948 patients and 1074 microbiological isolates, Escherichia coli was the most frequent microorganism (559/1074), showing a 14.5% multidrug-resistance rate. Klebsiella pneumoniae was second (168/1074) and exhibited the highest multidrug-resistance rate (54.2%), followed by Pseudomonas aeruginosa (97/1074) with a 38.1% multidrug-resistance rate. Predictors of multidrug-resistant gram-negative bacteria were male gender (odds ratio [OR], 1.66; 95% confidence interval [CI], 1.20–2.29), acquisition of cUTI in a medical care facility (OR, 2.59; 95%CI, 1.80–3.71), presence of indwelling urinary catheter (OR, 1.44; 95%CI, 0.99–2.10), having had urinary tract infection within the previous year (OR, 1.89; 95%CI, 1.28–2.79) and antibiotic treatment within the previous 30 days (OR, 1.68; 95%CI, 1.13–2.50). Conclusions The current high rate of multidrug-resistant gram-negative bacteria infections among hospitalised patients with cUTIs in the studied area is alarming. Our predictive model could be useful to avoid inappropriate antibiotic treatment and implement antibiotic stewardship policies that enhance the use of carbapenem-sparing regimens in patients at low risk of multidrug-resistance.