Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/121281
Title: Predictive factors for multidrug-resistant gram-negative bacteria among hospitalised patients with complicated urinary tract infections 11 Medical and Health Sciences 1108 Medical Microbiology
Authors: Gomila, Aina
Shaw, Evelyn
Carratalà, Jordi
Leibovici, Leonard
Tebé, Cristian
Wiegand, Irith
Vallejo Torres, Laura 
Vigo, Joan M.
Morris, Stephen
Stoddart, Margaret
Grier, Sally
Vank, Christiane
Cuperus, Nienke
Van Den Heuvel, Leonard
Eliakim-Raz, Noa
Vuong, Cuong
MacGowan, Alasdair
Addy, Ibironke
Pujol, Miquel
UNESCO Clasification: 32 Ciencias médicas
320505 Enfermedades infecciosas
Keywords: Complicated urinary tract infection
Gram-negative bacteria
Multidrug-resistance
Predictive model of multidrug-resistance gram-negative bacteria
Issue Date: 2018
Journal: Antimicrobial Resistance and Infection Control 
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.
URI: http://hdl.handle.net/10553/121281
ISSN: 2047-2994
DOI: 10.1186/s13756-018-0401-6
Source: Antimicrobial Resistance and Infection Control [ISSN 2047-2994], v. 7, 111, (2018)
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