Stefan Erb1, Reno Frei2, Sarah Tschudin Sutter1, Adrian Egli3, Marc Dangel1, Gernot Bonkat4, Andreas F Widmer1. 1. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, University of Basel, Switzerland. 2. Division of Clinical Microbiology, University Hospital Basel, University of Basel, Switzerland. 3. Division of Clinical Microbiology, University Hospital Basel, University of Basel, Switzerland / Applied Microbiology Research, Department of Biomedicine, University of Basel, Switzerland. 4. alta uro AG, Merian Iselin Klinik, Centre of Biomechanics and Calorimetry (COB), University of Basel, Switzerland.
Abstract
BACKGROUND: Antimicrobial resistance data from surveillance networks are frequently do not accurately predict resistance patterns of urinary tract infections at the bedside. OJECTIVE: To determine simple patient- and institution-related risk factors affecting antimicrobial resistance patterns of Escherichia coli urine isolates. METHODS: From January 2012 to May 2015 all consecutive urine samples with significant growth of E. coli (≥103 CFU/ml) obtained from a tertiary care hospital were analysed for antimicrobial susceptibility and related to basic clinical data such a patient age, ward, sample type (catheter vs non-catheter urine). RESULTS: Antimicrobial susceptibility testing was available for 5246 E. coli urine isolates from 4870 patients. E. coli was most commonly resistant to amoxicillin (43.1%), cotrimoxazole (24.5%) and ciprofloxacin (17.4%). Resistance rates were low for meropenem (0.0%), fosfomycin (0.9%) and nitrofurantoin (1.5%). Significantly higher rates of resistance to ciprofloxacin (32.8 vs 15.8%) and cotrimoxazole (30.6 vs 23.9%) were found in urological patients compared with patients on other wards (p <0.01). In multivariable analysis, predictors for E. coli resistance against ciprofloxacin and cotrimoxazole were: treatment in the urological unit (odds ratio [OR] 2.04, 95% confidence interval [CI] 1.63-2.54; p <0.001 and OR 1.33, 95% CI 1.07-1.64; p = 0.010, respectively), male sex (OR 1.93, 95% CI 1.630-2.29; p <0.001 and OR 1.22, 95% CI 1.22-1.04; p = 0.015), and only to a lesser extent urine samples obtained from indwelling catheters (OR 1.30, 95% CI 1.05-1.61; p = 0.014 and OR 1.26, 95% CI 1.04-1.53; p = 0.020). Age ≥65 years was associated with higher resistance to ciprofloxacin (OR 1.42, 95% CI 1.21-1.67; p <0.001), but lower resistance to cotrimoxazole (OR 0.76, 95% CI 0.67-0.86; p <0.001). CONCLUSIONS: Simple bedside patient data such as age, sex and treating hospital unit help to predict antimicrobial resistance and can improve the empirical treatment of urinary tract infections.
BACKGROUND: Antimicrobial resistance data from surveillance networks are frequently do not accurately predict resistance patterns of urinary tract infections at the bedside. OJECTIVE: To determine simple patient- and institution-related risk factors affecting antimicrobial resistance patterns of Escherichia coli urine isolates. METHODS: From January 2012 to May 2015 all consecutive urine samples with significant growth of E. coli (≥103 CFU/ml) obtained from a tertiary care hospital were analysed for antimicrobial susceptibility and related to basic clinical data such a patient age, ward, sample type (catheter vs non-catheter urine). RESULTS: Antimicrobial susceptibility testing was available for 5246 E. coli urine isolates from 4870 patients. E. coli was most commonly resistant to amoxicillin (43.1%), cotrimoxazole (24.5%) and ciprofloxacin (17.4%). Resistance rates were low for meropenem (0.0%), fosfomycin (0.9%) and nitrofurantoin (1.5%). Significantly higher rates of resistance to ciprofloxacin (32.8 vs 15.8%) and cotrimoxazole (30.6 vs 23.9%) were found in urological patients compared with patients on other wards (p <0.01). In multivariable analysis, predictors for E. coli resistance against ciprofloxacin and cotrimoxazole were: treatment in the urological unit (odds ratio [OR] 2.04, 95% confidence interval [CI] 1.63-2.54; p <0.001 and OR 1.33, 95% CI 1.07-1.64; p = 0.010, respectively), male sex (OR 1.93, 95% CI 1.630-2.29; p <0.001 and OR 1.22, 95% CI 1.22-1.04; p = 0.015), and only to a lesser extent urine samples obtained from indwelling catheters (OR 1.30, 95% CI 1.05-1.61; p = 0.014 and OR 1.26, 95% CI 1.04-1.53; p = 0.020). Age ≥65 years was associated with higher resistance to ciprofloxacin (OR 1.42, 95% CI 1.21-1.67; p <0.001), but lower resistance to cotrimoxazole (OR 0.76, 95% CI 0.67-0.86; p <0.001). CONCLUSIONS: Simple bedside patient data such as age, sex and treating hospital unit help to predict antimicrobial resistance and can improve the empirical treatment of urinary tract infections.
Authors: Kathrin Bausch; Soheila Aghlmandi; Sarah Ursula Sutter; Linda Maria Stamm; Hannah Ewald; Christian Appenzeller-Herzog; Jan Adam Roth; Andreas F Widmer; Hans-Helge Seifert Journal: Syst Rev Date: 2020-04-23
Authors: Sander G Kuiper; Maarten Ploeger; Erik B Wilms; Marleen M van Dijk; Emiel Leegwater; Robert A G Huis In't Veld; Cees van Nieuwkoop Journal: Antibiotics (Basel) Date: 2022-01-11