Multivariate Analysis of Risk Factors and Antimicrobial Resistance Patterns in Catheter-Associated Urinary Tract Infections: A Prospective Observational Study

Background: Catheter-associated urinary tract infections (CAUTIs) pose a significant healthcare burden, particularly in resource-limited settings like Jammu and Kashmir, India. This study aimed to identify risk factors, assess antimicrobial resistance patterns, and develop a predictive model for CAUTI in surgical intensive care unit (SICU) patients. Materials and Methods: A prospective observational cohort study was conducted in the SICU of a tertiary care center from February 2023 to December 2024. Adult patients requiring urinary catheterization for >48 h were included. Data on demographics, clinical parameters, and microbiological profiles were collected. Multivariable logistic regression and time-to-event analysis were used to identify risk factors, while antimicrobial resistance patterns were characterized. A predictive model for CAUTI risk was developed and validated. Results: Among 280 patients, 76 (27.1%) developed CAUTI. Significant risk factors included female gender (adjusted odds ratios [OR]: 2.89; 95% confidence interval [CI]: 1.65–5.07; P = 0.001), age >70 years (adjusted OR: 1.92; 95% CI: 1.15–3.21; P = 0.020), APACHE II score >20 (adjusted OR: 2.43; 95% CI: 1.39–4.25; P = 0.002), and catheterization >10 days (adjusted OR: 8.45; 95% CI: 3.78–18.87; P < 0.001). Time-to-event analysis showed a cumulative CAUTI incidence of 0.62 after >10 days. Predominant pathogens were Escherichia coli (36.8%) and Klebsiella pneumoniae (23.7%), with high rates of multidrug resistance (MDR) (57.1%–75.0%) and extensive drug resistance (28.6%–50.0%). The predictive model exhibited strong performance (area under the curve: 0.842 training, 0.824 validation). Conclusion: Prolonged catheterization, female gender, and higher clinical severity are key CAUTI risk factors. The high MDR and extensively drug-resistant rates highlight the need for antimicrobial stewardship and infection control. The predictive model aids in identifying high-risk patients for targeted interventions.