Urinary Tract Infection at MZH: Prevalence, Etiologic Organisms, Antimicrobial Susceptibility Patterns, and Predictors of Antibiotic Resistance: A Cross- Sectional Analytical Study
Abstract
Background: Urinary tract infections (UTIs) are among the most common bacterial infections globally. Rising antimicrobial resistance (AMR) limits the effectiveness of empirical therapy. Local susceptibility data are essential for guiding treatment, particularly in regions with high antibiotic utilization. Methods: A cross-sectional analytical study was conducted at Madinat Zayed Hospital (MZH) from January to December 2024 among adult inpatients with culture-confirmed UTIs. Demographic, clinical, microbiological, and treatment variables were collected.Antimicrobial susceptibility testing followed CLSI 2024 standards using Kirby–Bauer disk diffusion and MIC methods [4]. Resistance was defined at the isolate level as non-susceptibility (resistant or intermediate) to the antibiotic group assigned for that isolate. Binary logistic regression identified independent predictors of resistance. Results: Ninety-eight patients were included (mean age 56.54 ± 22.39 years; 50% male). Escherichia coli was the predominant organism (49.0%), followed by Klebsiella pneumoniae (20.4%) and Pseudomonas aeruginosa (12.2%). Overall, 26 isolates (26.5%) were classified as
resistant, 69 (70.4%) as sensitive, and 3 (3.1%) as intermediate. Aminoglycosides demonstrated the highest sensitivity rate (92.0%), while cephalosporins showed the highest resistance rate (40.5%). In multivariable logistic regression, Gram-negative organism type (adjusted odds ratio [AOR] 3.41, 95% CI 1.22–9.56, p = 0.019) and cephalosporin exposure on admission (AOR 2.87, 95% CI 1.03–7.98, p = 0.044) were independently associated with resistance. Model fit was acceptable (Hosmer–Lemeshow p = 0.42); Nagelkerke R² = 0.19 indicated modest explanatory power. Conclusion: UTIs at MZH were predominantly caused by Gram-negative organisms, with notable resistance to cephalosporins and penicillins. Aminoglycosides remained highly effective in vitro. Early cephalosporin exposure and Gram-negative species were independent predictors of resistance. These findings support culture- guided therapy and continued antimicrobial stewardship. Given the modest sample size and limited number of resistance events, the regression results should be interpreted as hypothesis-generating.
Keywords: urinary tract infection, antimicrobial resistance, E. coli, susceptibility pattern, logistic regression, Gram-negative bacteria
Keywords:
urinary tract infection, antimicrobial resistance, E. coli, susceptibility pattern, logistic regression, Gram-negative bacteriaDOI
https://doi.org/10.22270/jddt.v16i9.7983References
1. Foxman B. The epidemiology of urinary tract infection. Nat Rev Urol. 2010;7(12):653-660. https://doi.org/10.1038/nrurol.2010.190 PMid:21139641
2. Flores-Mireles AL, Walker JN, Caparon M, Hultgren SJ. Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol. 2015;13(5):269-284. https://doi.org/10.1038/nrmicro3432 PMid:25853778
3. Zeng Z, Zhan J, Zhang K, Chen H, Cheng S. Global, regional, and national burden of urinary tract infections from 1990 to 2019: an analysis of the global burden of disease study 2019. World J Urol. 2022;40(3):755-763. https://doi.org/10.1007/s00345-021-03913-0 PMid:35066637
4. Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing. 34th ed. CLSI supplement M100. Wayne, PA: Clinical and Laboratory Standards Institute; 2024.
5. Stamm WE, Norrby SR. Urinary tract infections: disease panorama and challenges. J Infect Dis. 2001;183 Suppl 1:S1-S4. https://doi.org/10.1086/318850 PMid:11171002
6. Tandogdu Z, Wagenlehner FM. Global epidemiology of urinary tract infections. Curr Opin Infect Dis. 2016;29(1):73-79. https://doi.org/10.1097/QCO.0000000000000228 PMid:26694621
7. Al-Orphaly M, Hadi HA, Eltayeb FK, et al. Epidemiology of multidrug-resistant Pseudomonas aeruginosa in the Middle East and North Africa Region. mSphere. 2021;6(3):e00202-21. https://doi.org/10.1128/mSphere.00202-21 PMid:34011686 PMCid:PMC8265635
8. Ministry of Health and Prevention, UAE. National Antimicrobial Resistance Surveillance Report 2024. Abu Dhabi: MOHAP; 2024.
9. Magiorakos AP, Srinivasan A, Carey RB, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18(3):268-281. https://doi.org/10.1111/j.1469-0691.2011.03570.x PMid:21793988
10. Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. 3rd ed. Hoboken, NJ: Wiley; 2013. https://doi.org/10.1002/9781118548387
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Copyright (c) 2026 Ashraf Alakkad , Zill Huma Hussain , Anwar Adwan , Sowjanya Bhupathiraju , Mohamed Sharaf
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