Antibiotic Resistance Profiling and Identification of Risk Factors Associated With Prevalence of Urinary Tract Infections: A Cross-Sectional Study

Abstract

Urinary tract infections (UTIs) are among the most common and serious global health concerns. The alarming rise of antibiotic resistance levels in uropathogens has further complicated the situation. This study aimed to determine the epidemiology, antibiotic resistance pattern, and associated risk factors of clinical isolates from patients suspected to have UTIs. A cross-sectional study was conducted at a tertiary-care hospital in North Kashmir, India, from June to December 2024. The samples were collected from the clinically suspected UTI patients and processed through standard procedures. Out of 513 urine samples, 190 (37%) showed significant growth on chrome agar, Escherchia coli followed by Entrococcus sp. were the most prevalent pathogens isolated. The isolates showed diverse resistance profiles, with 53% of pathogens showing multidrug resistance (resistance to three or more classes of antibiotics). The logistic regression and random forest models were applied to the dataset to determine the association between significant bacterial growth and the associated risk factors. The models were evaluated by AUC-ROC and F1-score. According to machine learning analysis, risk factors independently associated with the prevalence of UTIs were recurrent UTI, lower abdomen pain or hematuria, and urinary urgency. The findings in our study highlight that the unregulated use of antibiotics is encouraging the emergence of resistant strains, which need urgent attention due to their significant impact on community health.