CROSS SECTIONAL AI SURVEILLANCE OF MULTIDRUG RESISTANCE IN URINARY PATHOGENS FROM DIABETIC OUTPATIENTS

Background: Diabetic patients are at an increased risk of urinary tract infections (UTIs), which are frequently caused by multidrug-resistant (MDR) pathogens. Rising antimicrobial resistance complicates treatment decisions, especially in outpatient settings where empirical therapy is common. Enhanced surveillance using artificial intelligence (AI) may offer a novel solution for real-time monitoring of resistance patterns. Objective: To determine the prevalence and resistance patterns of urinary pathogens in diabetic outpatients using AI-supported data analysis tools for enhanced microbial surveillance and informed public health response. Methods: A cross-sectional study was conducted from October 2024 to June 2025 at outpatient clinics and laboratories in Lahore, Pakistan. A total of 350 diabetic outpatients with symptomatic UTIs were enrolled. Midstream urine samples were collected for culture and susceptibility testing using CLSI guidelines. MDR was defined as resistance to one agent in three or more antimicrobial classes. Data were analyzed using AI-supported tools to identify resistance trends and clustering. Statistical analysis included descriptive statistics, chi-square, t-tests, and logistic regression using SPSS v26.0. Results: Escherichia coli was the most common pathogen (55.7%), followed by Klebsiella pneumoniae (17.4%) and Enterococcus spp. (11.1%). High resistance rates were observed for ciprofloxacin (64.8%) and ceftriaxone (59.4%). MDR pathogens were identified in 42.3% of isolates, with significant associations to poor glycemic control and prior antibiotic use. AI tools facilitated clustering of resistance patterns and highlighted emerging resistance trends. Conclusion: The high burden of MDR uropathogens in diabetic outpatients necessitates routine culture-based diagnostics and antimicrobial stewardship. AI-supported surveillance proved effective in enhancing resistance detection and can guide more precise empirical therapy in resource-limited settings.