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Background: Urinary tract infections (UTIs) are among the most common bacterial infections, and antibiotic resistance complicates empiric treatment. This study aimed to describe recent resistance patterns among uropathogens in a tertiary-care teaching hospital to optimize empiric UTI management. Methods: This retrospective observational study included 280 patients diagnosed with UTIs at the Dr. Patnam Mahender Reddy Institute of Medical Sciences, Hyderabad, over a six-month period from June 2023 to November 2023. Urine culture and antibiotic susceptibility data were collected from electronic medical records. Patient demographics, including age, sex, and comorbid diabetes, were recorded. Causative uropathogens and their resistance rates to commonly prescribed UTI antibiotics were analyzed. Empiric antibiotic treatment patterns and outcomes were talked about. These included clinical cure, recurrence, susceptibility match, and microbiologic eradication. Results: The mean age of patients was 43.5 years, with 196 (70%) being female and 70 (25%) having diabetes. Escherichia coli caused 210 (75%) of UTIs, Klebsiella pneumoniae 42 (15%), Proteus mirabilis 14 (5%), Enterococcus faecalis 8 (3%), and Staphylococcus saprophyticus 6 (2%). E. coli resistance rates were 48% for ampicillin, 25% for ciprofloxacin, 18% for trimethoprim/sulfamethoxazole (TMP/SMX), and 5% for nitrofurantoin. K. pneumoniae resistance rates were 89% for ampicillin, 67% for ciprofloxacin, 44% for TMP/SMX, and 22% for nitrofurantoin. The most frequently prescribed antibiotic was nitrofurantoin (45%), then ciprofloxacin (35%). Clinical cure was achieved in 75% of cases. Recurrent UTIs within four weeks occurred in 25% of cases. Treatment matched urine culture susceptibility in 82% of patients. Conclusion: The rising fluoroquinolone resistance highlights the need for current local data to guide empiric UTI treatment. Nitrofurantoin had low resistance rates and was an effective first-line therapy. Ongoing monitoring of resistance patterns in UTIs is essential to optimize antibiotic selection.
Marepalli et al. (2024) studied this question.
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