Background: Multidrug-resistant (MDR) organisms pose a significant challenge in the effective treatment of urinary tract infections (UTIs).
Method: This study investigated the prevalence of MDR organisms and clinical predictors of UTIs in 824 high vaginal swab (HVS) specimens collected from female patients aged 0–79 years with suspected UTIs over a four-year period. Data on age and clinical signs were gathered using structured questionnaires, and specimens underwent analysis through culture-based techniques and molecular methods, including PCR, to identify bacterial and fungal pathogens.
Results: Most specimens were from young adults (ages 20–39, 75%), with fewer from older adults and elderly patients (3.3% combined). Inflammatory symptoms (51.3%) were the most common presentation, followed by vaginal discharge (21.2%) and obstetric-related issues (11.5%). MDR organisms were identified in 21.8% of cases, while non-MDR organisms accounted for 79.2%. Pathogen isolation occurred in 83.4% of specimens, with Candida albicans (27.1%) and Staphylococcus aureus (26.7%) as the most prevalent isolates. Logistic regression analysis revealed a statistically significant reduction in MDR likelihood for patients with cysts and tumors (odds ratio = 0.92, p = 0.046). Enterococcus faecalis exhibited the highest MDR rate (40%), and Escherichia coli was significantly associated with MDR status (B = 3.220, p < 0.001). Chi-square tests found no significant associations between MDR status and patient age (χ² = 2.825, p = 0.985) (χ² = 1.964, p = 0.962). Evaluation of the predictive model revealed moderate explanatory power (Cox & Snell R² = 0.151, Nagelkerke R² = 0.233), acceptable discriminatory ability (AUC = 0.753, p < 0.001), and good overall fit (Hosmer-Lemeshow test, χ² = 2.506, p = 0.961). However, the model displayed low sensitivity for MDR classification (2.8%) and convergence issues.
Conclusion: These findings highlight the need for enhanced antimicrobial resistance (AMR) surveillance and updated clinical guidelines to improve UTI management and combat the growing AMR challenge. Further research should refine predictive models to better inform clinical decision-making.