Multi-drug Resistance Profile of Bacteria Isolates Associated with Urinary Tract Infections amongst Hospitalized Female Patients in Benin City, Nigeria
EHIAGHE, J.I., USIFOH, A., ERHUNMWUNSE, R.U, MADUKA, N.
South Asian Journal of Research in Microbiology · pp. 1–8 · Published 25 Apr 2025
10.9734/sajrm/2025/v19i5432Abstract
Background: Urinary tract infections (UTIs) pose a significant public health burden, particularly among hospitalized female patients. Multidrug resistance (MDR) among UTI pathogens complicates treatment, necessitating continuous surveillance. Objectives: This study aimed to isolate and phenotypically identify MDR bacterial isolates associated with UTIs in the female patients at Edo Specialist Hospital, Benin City, Edo State, Nigeria. Materials and Methods: Urine samples were obtained from 50 hospitalized female patients aged 18 years and above and were analyzed at the Medical Microbiology Laboratory, Benson Idahosa University. Bacterial isolation involved culturing on MacConkey and Cystine Lactose Electrolyte Deficient (CLED) agar, followed by Gram staining and biochemical tests. Antibiotic susceptibility was determined using the Kirby-Bauer disc diffusion method. Results: The prevalence of bacterial infection in the urine of participants was 60%. Escherichia coli (50%), Staphylococcus aureus (43.3%), and Pseudomonas aeruginosa (6.7%) were isolated from samples. High resistance was observed against Ciprofloxacin, Cefuroxime, and Oxacillin, with sensitivity rates of 46.7%, 38.5%, and 13.3%, respectively. Pseudomonas aeruginosa exhibited notable resistance to Oxacillin and Amoxicillin-clavulanic acid. Imipenem was the most effective antibiotic, with sensitivity rates of 73.3% (Escherichia coli), 92.3% (Staphylococcus aureus), and 100% (Pseudomonas aeruginosa). Conclusions: The findings highlight the urgent need for robust antibiotic stewardship programs, continuous surveillance, and the development of novel antibiotics to combat MDR UTI pathogens. Tailored treatment strategies based on local resistance patterns are essential for effective management and improved patient outcomes.
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