Antimicrobial resistance pattern of bacteria isolated from patients with urinary tract infection in Tripoli city, Libya

Abstract

Urinary tract infections (UTIs) remain one of the most common bacterial infections worldwide diagnosed in outpatients as well 
as hospitalized patients. In last decades, the widespread uses of antibiotics has resulted in the appearance of pathogens with increasing resistance to antimicrobial agents among urinary tract pathogens all over the world.The aim of this study was to investigate the prevalence and antibiotic resistance patterns of bacterial pathogens isolated from patients with urinary tract infections (UTIs) at the red-crescent private clinic, Tripoli, Libya.This retrospective study was conducted at a private clinic in Tripoli city during February to May, 2016. Urine was collected from suspected cases of UTI patients. Identification of bacterial isolates was performed by conventional methods. Antimicrobial susceptibility testing of culture positive bacterial isolateswas done by disk diffusion method according to Clinical Laboratory Standard Institute guidelines (CLSI).Atotal number of 224 urine samples were collected of which 62 (27.7%) showed significant bacterial growth. The commonest bacterium isolated from the culture positive urine sample was Staphylococci (64.5%) 
followed by Escherichia coli (29.0%) and Klebsiella pneumonia (6.5%). K. pneumonia and E. coli were highly resistant (38.11%, 34.13%, respectively) than staphylococci spp (26.29%), and exhibit resistance to wide range of tested antimicrobials. Ciprofloxacin was the most (12.3% resistance) powerful antibiotic agent followed by Gentamycin (17.6% resistance) and Erythromycin (19.0% resistance) which affected 87.7%, 82.4% and 81.0% of tested UTI causative pathogens, respectively; on the other hand, Coamoxclav and Amoxicillin were the least (63.0% & 50.3% resistance, respectively) affected only 37.0% and 49.7% of tested isolates, respectively. The obtained findings 
emphasized the need for ongoing investigations to show the pattern of antibiotic resistance which can help clinicians in antibiotic prescription in their clinics.