Stesura Seveso Archivio Italiano di Urologia e Andrologia 2025; 97(3):14129 1 ORIGINAL PAPER INTRODUCTION Urinary tract infections (UTIs) are among the most common bacterial infections globally, with over 150 million cases reported annually (1, 2). They impose a significant clinical and economic burden on healthcare systems, ranging from uncomplicated cystitis to severe conditions such as pyelonephritis and urosepsis (1). The severity of clinical presentation often reflects the extent of disease progression (3). Gram-negative bacteria, particularly Escherichia coli (E. coli), remain the predominant uropathogens world- wide. However, recent epidemiological trends indicate a growing prevalence of non-E. coli organisms, including Klebsiella pneumoniae (K. pneumoniae), Pseudomonas aerug- inosa (P. aeruginosa), and Staphylococcus aureus (S. aureus), which complicates empirical treatment decisions and high- lights the need for continuous surveillance (4, 5). The emergence of antimicrobial resistance (AMR), especial- ly multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains, has further complicated the management of UTIs (6). MDR is defined as resistance to at least three antimicrobial classes, while XDR refers to resistance to all but two or fewer antimicrobial classes (7). These resist- ance patterns are particularly concerning in resource-lim- ited settings, where inadequate antimicrobial stewardship and limited diagnostic infrastructure contribute to the rapid dissemination of resistant organisms (7). Key molec- ular mechanisms underlying resistance include the pro- duction of extended-spectrum β-lactamases (ESBLs), car- bapenemases (e.g., KPC and NDM), and the overexpres- sion of efflux pumps. These mechanisms are associated with treatment failure, prolonged hospital stays, increased healthcare costs, and higher mortality rates (8). Background: Urinary tract infections (UTIs) are a major global health concern, particularly in resource-limited regions where antimicrobial resistance (AMR) is increasingly prevalent. This study aimed to describe the demographic characteristics, pathogen distribu- tion, and antimicrobial resistance patterns among UTI patients, and to identify clinical predictors of multidrug-resist- ant (MDR) and extensively drug-resistant (XDR) infections. Methods: A retrospective analysis was conducted on 216 clini- cally confirmed UTI cases processed at the Infectious Bacteriology and Biochemistry Laboratory affiliated with IBB University between January 2023 and September 2024. Data collected included patient demographics, clinical symp- toms, comorbidities, bacterial isolates, and antimicrobial sus- ceptibility profiles. MDR and XDR were classified according to internationally recognized definitions. Univariate and multi- variate logistic regression analyses were performed to identify independent predictors of MDR/XDR infections. Results: The majority of patients were adults aged 15-65 years (83.3%, n = 180), with a slight male predominance (53.2%, n = 115). Escherichia coli was the most frequently isolated pathogen (29.6%, n = 64), followed by Staphylococcus aureus (19.0%, n = 41) and Pseudomonas aeruginosa (6.0%, n = 13). A substantial proportion of isolates exhibited MDR or XDR phenotypes (80.1%, n = 173). Among E. coli isolates, resist- ance rates to ciprofloxacin and ceftriaxone exceeded 60%. Notably, all Klebsiella pneumoniae isolates were MDR (100%), and 92.3% of P. aeruginosa isolates were MDR. Nitrofurantoin and carbapenems demonstrated relatively high- er susceptibility rates. Multivariate analysis identified prior hospitalization (adjusted odds ratio [aOR] = 3.15; 95% CI: 1.50-6.60; p = 0.002) and E. coli infection (aOR = 2.41; 95% CI: 1.02–5.70; p = 0.04) as significant predictors of MDR/XDR infections. Conclusions: The high prevalence of MDR and XDR uropathogens, particularly E. coli, underscores the urgent need for sustained antimicrobial resistance surveillance and stew- ardship programs in resource-limited settings. Identifying key clinical predictors can inform empirical treatment strategies, improve patient outcomes, and help contain the spread of resistant organisms. Epidemiology and antimicrobial resistance of uropathogens in a tertiary care setting in Yemen: A retrospective study Faisal Ahmed 1, Ennayyat Alhamdani 1, Saleh Al-Wageeh 2, Qasem Alyhari 2, Saif Ghabisha 2, Ahmed Ateik 3, Khalil Al-Naggar 1, Ibrahim Alnadhari 4, 5, Abdulghani Al-Hagri 6 1 Department of Urology, School of Medicine, Ibb University, Ibb, Yemen; 2 Department of General Surgery, School of Medicine, Ibb University, Ibb, Yemen; 3 Department of General Surgery, School of Medicine, 21 September University, Sana'a, Yemen; 4 Al Wakra Hospital, Hamad Medical Corporation, Al Wakra, Qatar; 5 Department of Surgery, College of Medicine, Qatar University, Doha, Qatar; 6 Student Research Committee, Faculty of Medicine, Sana'a University, Sana'a, Yemen. DOI: 10.4081/aiua.2025.14129 Summary KEY WORDS: Urinary tract infections; Uropathogenic Escherichia coli; Antimicrobial resistance; Multidrug resistance (MDR); exten- sively drug-resistant (XDR); Yemen; Resource-limited setting. Submitted 10 July 2025; Accepted 27 July 2025 Archivio Italiano di Urologia e Andrologia 2025; 97(3):14129 F. Ahmed, E. Alhamdani, S. Al-Wageeh, et al. 2 In Yemen, the inappropriate use of antibiotics is a major driver of AMR. A prior study in Aden reported that antibi- otics were prescribed in 84.2% of outpatient cases, a rate far exceeding World Health Organization (WHO) recom- mendations (9). This issue is compounded by the wide- spread availability of counterfeit and substandard medi- cines. It is estimated that up to 80% of pharmaceuticals entering Yemen are distributed through unregulated channels, with approximately 40% being of poor quality or counterfeit (10). According to WHO analyses, 43% of counterfeit antibi- otics contain no active ingredient, 24% fail to meet qual- ity standards, 21% contain subtherapeutic concentra- tions, and 7% contain incorrect substances (11). The combination of irrational prescribing practices and the proliferation of ineffective antimicrobials creates a con- ducive environment for the emergence and spread of resistant uropathogens. Patients exposed to subtherapeu- tic or inactive treatments are at increased risk of treatment failure, facilitating the persistence and transmission of resistant strains in the community (12). Despite national efforts to monitor AMR, there remains a significant gap in data regarding the distribution and resistance profiles of uropathogens in Yemen, particular- ly in tertiary care settings. This lack of local evidence lim- its the development of context-specific treatment guide- lines and infection control strategies. Understanding the demographic and clinical factors associated with MDR and XDR UTIs – such as prior hospitalization, comor- bidities, and healthcare exposure – is essential for improving patient outcomes and informing antimicrobial stewardship programs (12). This study aimed to describe the demographic characteristics, pathogen distribution, and antimicrobial resistance patterns among UTI cases at a tertiary referral laboratory in Ibb, Yemen. Additionally, it sought to identify clinical predictors of MDR and XDR infections to support evidence-based prescribing and enhance regional AMR surveillance. PATIENTS AND METHODS Study design and setting This retrospective observational study was conducted at the Infectious Bacteriology and Biochemistry (IBB) Laboratory, affiliated with IBB University, located in Ibb City, Yemen. The IBB Laboratory functions as a tertiary referral center, receiving clinical specimens from diverse patient populations across multiple healthcare facilities. The study period spanned from January 1, 2023, to September 12, 2024. Study population The study included all patients with clinically confirmed UTIs whose urine samples were processed at the IBB Laboratory during the specified timeframe. A UTI was defined as the presence of typical clinical symptoms – such as dysuria, urinary frequency, and urgency – accom- panied by a positive urine culture yielding ≥ 105 colony- forming units per milliliter (CFU/mL) of a single uropathogen. This definition aligns with internationally recognized diagnostic criteria for UTI (e.g., EMA and FDA guidelines) (13). Patients with contaminated sam- ples (mixed flora), or duplicate isolates from the same infection episode were excluded. Consecutive sampling was employed to minimize selection bias, resulting in a final sample of 216 eligible cases. Sample collection and microbiological analysis Midstream urine specimens were collected using stan- dardized aseptic techniques to reduce contamination risk. Initial screening was performed using urine dipstick tests to detect leukocyte esterase and nitrites, which are sug- gestive of infection. However, dipstick results were not used as diagnostic criteria. Samples were inoculated onto CLED agar, blood agar, and MacConkey agar and incu- bated aerobically at 35-37°C for 18-24 hours. Bacterial growth was quantified, and isolates with ≥ 105 CFU/mL were considered clinically significant. Bacterial identification was performed using a combina- tion of conventional biochemical tests and automated sys- tems, including VITEK 2 (bioMérieux, Durham, NC, USA) (14). Where available, matrix-assisted laser desorption/ion- ization time-of-flight (MALDI-TOF) mass spectrometry was used for confirmatory identification. Antimicrobial susceptibility testing Antimicrobial susceptibility testing (AST) was conducted in accordance with the Clinical and Laboratory Standards Institute (CLSI) guidelines (31st Edition) (15). The Kirby- Bauer disk diffusion method and/or broth microdilution techniques were used to determine susceptibility to a panel of antibiotics commonly used in UTI treatment, including fluoroquinolones, β-lactams, aminoglycosides, nitrofurantoin, and carbapenems. Minimum inhibitory concentrations (MICs) were interpreted using CLSI clinical breakpoints. MDR was defined as resistance to at least one agent in three or more antimi- crobial classes, while XDR was defined as resistance to all but two or fewer antimicrobial classes, based on interna- tional consensus definitions (7). Quality control was maintained by including reference strains such as E. coli ATCC 25922 in each testing batch to ensure the validity and reproducibility of results. Data collection and management Relevant demographic, clinical, and microbiological data were extracted from the laboratory information system and patient medical records using a standardized data collection form. Double data entry and cross-validation were performed to ensure accuracy and minimize tran- scription errors. All data were anonymized prior to analy- sis. Patient confidentiality was maintained throughout the study in accordance with institutional ethical standards and the principles of the Declaration of Helsinki. Statistical analysis Data were analyzed using IBM SPSS Statistics version 23 (IBM Corp., Armonk, NY, USA). Descriptive statistics were used to summarize demographic, clinical, and microbio- logical variables. Categorical variables were expressed as frequencies and percentages, while continuous variables were reported as means ± standard deviations (SD) or medians with interquartile ranges (IQR), depending on Archivio Italiano di Urologia e Andrologia 2025; 97(3):14129 3 Uropathogens in Yemen data distribution. Group comparisons were performed using the chi-square (χ²) test or Fisher’s exact test for cat- egorical variables, and the Student’s t-test or Mann- Whitney U test for continuous variables, following nor- mality assessment using the Shapiro-Wilk test. Variables with a p-value < 0.20 in univariate analysis were includ- ed in a multivariable logistic regression model to identify independent predictors of MDR/XDR UTIs. Adjusted odds ratios (aOR) with 95% confidence intervals (CI) were cal- culated. Multicollinearity was assessed using the variance inflation factor (VIF), with values > 5 indicating significant collinearity. Model calibration was evaluated using the Hosmer-Lemeshow goodness-of-fit test, with a p-value > 0.05 indicating adequate fit. Discriminatory ability was assessed using the area under the receiver operating char- acteristic curve (AUC), with values between 0.7 and 0.8 considered acceptable. A two-tailed p-value < 0.05 was considered statistically significant for all analyses. Ethical considerations The study protocol was reviewed and approved by the Institutional Ethics Committee of IBB University (Approval No. IBBUNI.AC.YEM.2024.79). Given the ret- rospective nature of the study and the use of anonymized data, the requirement for informed consent was waived. All procedures were conducted in accordance with the ethical standards of the institutional research committee and the Declaration of Helsinki. RESULTS Demographic and clinical characteristics A total of 216 patients with clinically confirmed UTIs were included in the study. The majority were adults aged 15-65 years, comprising 180 patients (83.3%). Pediatric patients aged 1-14 years accounted for 24 patients (11.1%), while geriatric patients over 65 years represented 12 patients (5.6%) (Table 1). Males slightly outnumbered females, with 115 (53.2%) and 101 (46.8%) patients, respectively. Most patients presented with symptomatic UTIs (91.7%, n = 198), while 8.3% (n = 18) had asymptomatic bacteriuria. Comorbidities were present in 32.4% of patients. Diabetes mellitus was the most common (19.4%, n = 42), followed by other chronic conditions such as hypertension, chronic kidney disease, or immunosuppression (13.0%, n = 28). The remaining 67.6% (n = 146) had no documented comorbidities. A history of prior hospitalization within the last six months was reported in 26.9% (n = 58) of cases. The prevalence of MDR or XDR infections was high across all age groups: 75.0% (18/24) in pediatric patients, 81.1% (146/180) in adults, and 83.3% (10/12) in geri- atric patients. However, these differences were not statis- tically significant (p = 0.12). Similarly, no significant dif- ference in MDR/XDR prevalence was observed between males (79.1%, 91/115) and females (82.2%, 83/101) (p = 0.45). Symptomatic patients had an MDR/XDR preva- lence of 80.8% (160/198), compared to 77.8% (14/18) in those with asymptomatic bacteriuria (p = 0.62). Patients with diabetes mellitus exhibited a higher MDR/XDR prevalence (88.1%, 37/42) compared to those without comorbidities (78.1%, 114/146), though this dif- ference was not statistically significant (p = 0.08). In con- trast, prior hospitalization was significantly associated with MDR/XDR UTIs (89.7% vs. 77.2%, p = 0.02), high- lighting its importance as a risk factor (Table 1). Pathogen distribution and resistance profiles A total of 216 bacterial isolates were identified. E. coli was the most frequently isolated pathogen (29.6%, n = 64), followed by S. aureus (19.0%, n = 41), P. aeruginosa (6.0%, n = 13), and K. pneumoniae (5.6%, n = 12) (Table 2). Other Gram-negative organisms accounted for 24.1% (n = 52) of isolates, while other Gram-positive species represented 15.7% (n = 34). Overall, 56.0% (n = 121) of isolates were classified as MDR, and 25.9% (n = 56) as XDR. The highest MDR rates were observed in K. pneumoniae (100.0%, 12/12), fol- lowed by P. aeruginosa (92.3%, 12/13), E. coli (81.3%, 52/64), and S. aureus (73.2%, 30/41). XDR rates were also notable in K. pneumoniae (50.0%, 6/12), P. aerugi- Table 1. Demographic and clinical characteristics of patients with urinary tract infections and their association with multidrug-resistant/extensively drug-resistant status (n = 216). Characteristic Category N (%) MDR/XDR cases n (%) P-value Age group Pediatric (1–14 years) 24 (11.1) 18 (75.0) 0.12 * Adult (15–65 years) 180 (83.3) 146 (81.1) Geriatric (> 65 years) 12 (5.6) 10 (83.3) Sex Male 115 (53.2) 91 (79.1) 0.45 * Female 101 (46.8) 83 (82.2) Clinical presentation Symptomatic UTI 198 (91.7) 160 (80.8) 0.62 * Asymptomatic Bacteriuria 18 (8.3) 14 (77.8) Comorbidities Diabetes Mellitus 42 (19.4) 37 (88.1) 0.08 * Hypertension 15 (6.9) 12 (80.0) 0.83 * Chronic Kidney Disease 8 (3.7) 7 (87.5) 0.50 ** Immunocompromised 5 (2.3) 4 (80.0) 1.00 ** None (Reference) 146 (67.6) 114 (78.1) — Prior Hospitalization Yes 58 (26.9) 52 (89.7) 0.02 * No 158 (73.1) 122 (77.2) * Chi-square test; ** Fisher’s exact test (used for small cell counts). MDR/XDR percentages represent the proportion of resistant cases within each subgroup. Archivio Italiano di Urologia e Andrologia 2025; 97(3):14129 F. Ahmed, E. Alhamdani, S. Al-Wageeh, et al. 4 nosa (38.5%, 5/13), S. aureus (24.4%, 10/41), and E. coli (18.8%, 12/64) (Table 2). Antimicrobial resistance patterns Antibiotic susceptibility testing revealed high resistance rates to several commonly used agents (Table 3). Ciprofloxacin resistance was observed in 64.8% (140/216) of isolates, with particularly high rates among E. coli (78.1%, 50/64) and P. aeruginosa (84.6%, 11/13). Ceftriaxone resistance was detected in 75.9% (164/216) of isolates, especially among K. pneumoniae (91.7%, 11/12). Gentamicin resistance was present in 52.8% (114/216) of isolates, with the highest rate observed in P. aeruginosa (76.9%, 10/13). Nitrofurantoin resistance was relatively low, affecting 28.8% (57/198) of tested isolates. Enterococcus faecalis demonstrated the lowest nitrofurantoin resistance (12.0%, 3/25). Meropenem resistance was observed in 13.4% (29/216) of isolates, with a notably higher rate among K. pneumoniae (33.3%, 4/12) (Table 3). Resistance phenotype classification Based on resistance phenotypes, 17.6% (n = 38) of iso- lates were non-MDR, 56.0% (n = 121) were MDR, 25.9% (n = 56) were XDR, and 0.5% (n = 1) were pandrug- resistant (PDR) (Table 4). Among E. coli isolates, 18.8% (12/64) were non-MDR, 62.5% (40/64) were MDR, and 18.8% (12/64) were XDR. For S. aureus, 26.8% (11/41) were non-MDR, 48.8% (20/41) were MDR, and 24.4% (10/41) were XDR. P. aeruginosa isolates were predomi- nantly MDR (53.8%, 7/13) or XDR (38.5%, 5/13). No PDR isolates were identified among these species, with the exception of one Acinetobacter baumannii isolate (Table 4). Predictors of MDR/XDR urinary tract infections Multivariable logistic regression analysis identified two independent predictors of MDR/XDR UTIs (Table 5). Infection with E. coli was significantly associated with an increased risk of MDR/XDR status aOR = 2.41; 95% CI, 1.02-5.70; p = 0.04). Prior hospitalization within the last six months was also a strong predictor (aOR = 3.15; 95% CI, 1.50-6.60; p = 0.002). In contrast, age over 65 years (aOR = 1.82; 95% CI, 0.38- 8.72; p = 0.45) and female sex (aOR = 0.91; 95% CI, 0.44-1.89; p = 0.80) were not significantly associated Table 3. Antimicrobial resistance profiles of uropathogens (n = 216 isolates). Antibiotic (Class) Resistant Isolates Overall Pathogens with n (%) Resistance Rate High Resistance (>75%) Ciprofloxacin (Fluoroquinolone) 140 (64.8) 64.8% Escherichia coli Ceftriaxone (3rd-gen Cephalosporin) 164 (75.9) 75.9% Klebsiella pneumoniae Gentamicin (Aminoglycoside) 114 (52.8) 52.8% Pseudomonas aeruginosa Nitrofurantoin (Nitrofuran) 57/198 (28.8) 28.8% Enterococcus faecalis (low resistance) Meropenem (Carbapenem) 29 (13.4) 13.4% Klebsiella pneumoniae 3rd-gen = Third-generation cephalosporin. High resistance defined as > 75% resistance rate among isolates tested. Nitrofurantoin was not tested against Pseudomonas spp. (n = 13) and non-UTI pathogens (n = 5). Table 2. Frequency and resistance patterns of uropathogens isolated from patients with urinary tract infections (n = 216 Isolates). Pathogen Frequency n (%) MDR Cases n (%) XDR Cases n (%) Escherichia coli 64 (29.6) 52 (81.3) 12 (18.8) Staphylococcus aureus 41 (19.0) 30 (73.2) 10 (24.4) Pseudomonas aeruginosa 13 (6.0) 12 (92.3) 5 (38.5) Klebsiella pneumoniae 12 (5.6) 12 (100.0) 6 (50.0) Other Gram-negative spp. * 52 (24.1) 38 (73.1) 14 (26.9) Other Gram-positive spp. ** 34 (15.7) 22 (64.7) 9 (26.5) MDR = resistance to ≥ 3 antimicrobial classes; XDR = resistance to all but ≤ 2 classes. * Includes Proteus spp. (n = 9), Enterobacter spp. (n = 8), Citrobacter spp. (n = 6), and others (n = 29). ** Includes Enterococcus spp. (n = 17), Streptococcus spp. (n = 5), and other species (n = 12). Overall MDR rate = 56.0% (121/216); XDR rate = 25.9% (56/216). Table 4. Resistance phenotypes of major uropathogens isolated from patients with UTIs (n = 216 Isolates). Pathogen Non-MDR n (%) MDR n (%) XDR n (%) PDR n (%) Escherichia coli 12 (18.8) 40 (62.5) 12 (18.8) 0 (0.0) Staphylococcus aureus 11 (26.8) 20 (48.8) 10 (24.4) 0 (0.0) Pseudomonas aeruginosa 1 (7.7) 7 (53.8) 5 (38.5) 0 (0.0) Klebsiella pneumoniae 0 (0.0) 6 (50.0) 6 (50.0) 0 (0.0) Total 38 (17.6) 121 (56.0) 56 (25.9) 1 (0.5) MDR = resistance to ≥3 antimicrobial classes; XDR = resistance to all but ≤ 2 classes; PDR = resistance to all tested agents. One Acinetobacter baumannii isolate was classified as pandrug-resistant (PDR). Archivio Italiano di Urologia e Andrologia 2025; 97(3):14129 5 Uropathogens in Yemen with MDR/XDR status. Diabetes mellitus showed a non- significant elevation in odds (aOR = 1.95; 95% CI, 0.82- 4.65; p = 0.13), which should be interpreted cautiously. Model diagnostics indicated adequate fit (Hosmer- Lemeshow test, p = 0.62) and moderate discriminative ability (area under the curve = 0.72; 95% CI, 0.65-0.79) (Table 5). DISCUSSION Pathogen distribution and resistance trends This study reveals a high prevalence of MDR and XDR uropathogens among patients with UTIs, consistent with emerging trends in both regional and global contexts (2, 16). E. coli was the most frequently isolated pathogen, accounting for 29.6% of cases, which aligns with data from across Africa and other low- and middle-income coun- tries (LMICs) (17-19). The predominance of E. coli as a uropathogen is well-documented, and its high rates of MDR (81.3%) and XDR (18.8%) phenotypes in this study mirror findings from sub-Saharan Africa and other resource-constrained settings (10, 18-20). This trend is largely driven by the global spread of extended-spectrum beta-lactamase (ESBL)-producing strains, which signifi- cantly limit therapeutic options and complicate clinical management (21, 22). Similarly, the elevated resistance observed in K. pneumo- niae (100% MDR, 50% XDR) and P. aeruginosa (92.3% MDR, 38.5% XDR) reflects a broader pattern of increas- ing antimicrobial resistance among Gram-negative pathogens in UTIs (10, 23-25). The universal resistance of K. pneumoniae to ampicillin is consistent with its intrinsic resistance mechanisms, including chromosomal beta-lactamase production and the acquisition of plas- mid-mediated ESBLs and carbapenemases (23). These enzymatic defenses severely restrict antibiotic choices and underscore the need for enhanced surveillance and stew- ardship. Resistance to commonly prescribed antibiotics such as ciprofloxacin (64.8%) and ceftriaxone (75.9%) was notably high, consistent with reports from Saudi Arabia, Iran, and other parts of Africa, where fluoroquinolone and third-generation cephalosporin resistance often exceed 60% (2, 3, 17, 26, 27). In contrast, nitrofurantoin and carbapenems retained relatively higher susceptibility rates (71.2% and 86.6%, respectively), supporting their continued use as empirical treatment options in selected cases (28, 29). However, the emergence of carbapenem resistance – particularly among K. pneumoniae isolates (33%) – is concerning and highlights the urgent need for judicious use of last-resort antibiotics. Risk factors for MDR/XDR infections Multivariate analysis identified prior hospitalization (aOR = 3.15; 95% CI: 1.50-6.60; p = 0.002) and E. coli infec- tion (aOR = 2.41; 95% CI: 1.02-5.70; p = 0.04) as inde- pendent predictors of MDR/XDR UTIs. These findings are consistent with existing literature that links healthcare exposure to increased risk of resistant infections due to selective antibiotic pressure and nosocomial transmission (30, 31). The association between E. coli and MDR/XDR status may reflect the widespread dissemination of ESBL- producing strains in both community and hospital set- tings. This pathogen’s genetic adaptability and frequent exposure to antibiotics make it a key driver of resistance in UTIs (25). These findings emphasize the importance of targeted diagnostic approaches and tailored empirical therapy, particularly in high-risk populations. Demographic and clinical characteristics Although age and gender were not identified as a statisti- cally significant predictor of MDR/XDR urinary tract infec- tions in this study, the majority of patients were adults aged 15-65 years (83.3%), with a slight male predomi- nance (53.2%). This contrasts with many regional studies that report a higher UTI prevalence among females (26, 27, 32). The observed male predominance may be attrib- utable to differences in healthcare-seeking behavior, refer- ral patterns, or underlying comorbidities in this setting. Notably, Khanal et al. reported higher MDR rates among males, possibly due to increased antibiotic exposure in this group (33). These sex-specific trends warrant further investigation and should inform future risk stratification strategies. Moreover, our findings align with regional epi- demiological data from the Middle East and North Africa. Amiri et al. reported that UTI incidence peaked in younger adults, particularly females aged 20-24 and males aged 35-39, followed by a gradual decline with age (2). While our study did not observe a significant difference in MDR/XDR prevalence across age groups, the concentra- tion of cases in the 15-65 age range is consistent with these regional patterns (2). This suggests that while age remains a key factor in UTI epidemiology, the emergence of resistance may be influenced more by healthcare expo- sure and antibiotic use than by age alone. Table 5. Multivariable logistic regression analysis of predictors for multidrug-resistant and extensively drug-resistant urinary tract infections. Predictor Reference Unadjusted OR p-value Adjusted OR p-value VIF category (95% CI) (95% CI) Age > 65 years ≤ 65 years 1.42 (0.51–3.95) 0.50 1.82 (0.38–8.72) 0.45 1.12 Female sex Male 0.84 (0.45–1.56) 0.58 0.91 (0.44–1.89) 0.80 1.04 Escherichia coli Other pathogens 2.15 (1.12–4.13) 0.02 2.41 (1.02–5.70) 0.04 1.32 Prior hospitalization No hospitalization 3.40 (1.78–6.50) < 0.001 3.15 (1.50–6.60) 0.002 1.18 Diabetes mellitus No diabetes 2.01 (0.93–4.35) 0.08 1.95 (0.82–4.65) 0.13 1.21 Model Diagnostics: • Hosmer-Lemeshow test: p = 0.62 (indicating good model fit). • Area under the curve (AUC): 0.72 (95% CI: 0.65–0.79), indicating moderate discriminatory ability.. • Variance Inflation Factor (VIF): All values < 5, suggesting no significant multicollinearity. Archivio Italiano di Urologia e Andrologia 2025; 97(3):14129 F. Ahmed, E. Alhamdani, S. Al-Wageeh, et al. 6 Although several factors previously reported to be associ- ated with MDR urinary tract infections – such as prior antibiotic use, duration of catheterization, urological pro- cedures and the presence of comorbidities – were not fully captured in our analysis due to the retrospective nature of the study, their role in the development of resistance remains well established (34-38). These patient-specific factors, along with broader determinants such as healthcare exposure and environmental influ- ences, significantly contribute to the emergence and per- sistence of MDR and XDR uropathogens. In our study, a notably high proportion of patients with diabetes mellitus (88.1%) were affected by resistant infections, a finding that aligns with existing literature linking diabetes to increased susceptibility to complicated and antimicro- bial-resistant UTIs (39). The underlying pathophysiology includes immune dysfunction due to chronic hyper- glycemia, urinary stasis, and frequent healthcare contact, all of which facilitate bacterial colonization and the selec- tion of resistant strains (40). These findings underscore the importance of targeted infection control measures and individualized antimicrobial strategies in high-risk popu- lations, particularly those with chronic comorbidities such as diabetes mellitus. Clinical and public health implications The findings of this study have important implications for clinical practice and public health policy. The high preva- lence of MDR and XDR uropathogens – particularly E. coli – highlights the urgent need for continuous antimi- crobial resistance surveillance and the implementation of stewardship programs in resource-limited settings. Empirical treatment guidelines should be updated regu- larly to reflect local resistance patterns and minimize the risk of treatment failure. Moreover, the emergence of carbapenem resistance in Klebsiella spp. and other Gram-negative pathogens sig- nals a critical threat to available treatment options. This trend necessitates the development of novel therapeutic strategies and the reinforcement of infection prevention and control measures in both hospital and community settings. Study limitations Several limitations should be considered when interpret- ing the results of this study. First, its retrospective design limits the ability to control for confounding variables, and some clinical data may be incomplete or inconsistently documented. Second, variations in UTI definitions across studies may affect comparability. Although our use of clinical symptoms combined with culture results aligns with current guidelines, misclassification – particularly in cases of asymptomatic bacteriuria – cannot be ruled out (41, 42). Third, while antimicrobial susceptibility testing followed CLSI guidelines, we did not systematically investigate specific resistance mechanisms such as ESBL or carbapenemase production. This limits the depth of our resistance analysis and may obscure important epi- demiological trends. Fourth, the study was conducted at a single tertiary care center, which may limit the general- izability of the findings to primary care or community set- tings. Finally, the study did not assess patient outcomes or treatment efficacy, which are essential for linking resistance patterns to clinical impact and guiding thera- peutic decisions. CONCLUSIONS In summary, this study highlights a high burden of MDR and XDR uropathogens in a resource-limited setting, with E. coli playing a central role in resistance dissemination. Prior hospitalization and E. coli infection were identified as key predictors of resistant infections. These findings underscore the need for ongoing resistance monitoring, antimicrobial stewardship, and context-specific treatment guidelines to improve patient outcomes and curb the spread of antimicrobial resistance. REFERENCES 1. Mancuso G, Midiri A, Gerace E, et al. Urinary Tract Infections: The Current Scenario and Future Prospects. Pathogens. 2023; 12:623. 2. Amiri F, Safiri S, Aletaha R, et al. Epidemiology of urinary tract infections in the Middle East and North Africa, 1990-2021. Trop Med Health. 2025; 53:16. 3. Fakhri-Demeshghieh A, Shokri A, Bokaie S. Antibiotic Resistance of Uropathogenic Escherichia coli (UPEC) among Iranian Pediatrics: A Systematic Review and Meta-Analysis. Iran J Public Health. 2024; 53:508-23. 4. Kapesa C, Mumbula EM, Kwenda HC. Prevalence of gram-nega- tive bacterial causes of urinary tract infection and their antimicrobial susceptibility profile at the university teaching hospitals in Lusaka, Zambia. Scientific African. 2025; 27:e02558. DECLARATIONS Ethical approval and consent for participate: The study pro- tocol was reviewed and approved by the Institutional Review Board (IRB) of Ibb University (Approval Code: IBBUNI.AC.YEM.2024.79, dated February 3, 2024). Due to the retrospective nature of the study and the use of anonymized data, the requirement for informed consent was waived. The study was conducted in accordance with the principles of the Declaration of Helsinki. Availability of data and material: The datasets analyzed during the current study are available in the Mendeley Data repository and can be accessed via the following DOI: 10.17632/26hn6wmb8x.1. Competing interests: The authors declare no conflicts of interest. Funding: This research received no specific grant from any fund- ing agency in the public, commercial, or not-for-profit sectors. Authors' contributions: All authors contributed substantially to the conception, design, data acquisition, analysis, interpretation, and manuscript preparation. Each author reviewed and approved the final version for submission and agreed to be accountable for all aspects of the work. Acknowledgments: None. Archivio Italiano di Urologia e Andrologia 2025; 97(3):14129 7 Uropathogens in Yemen 5. Mouanga-Ndzime Y, Bisseye C, Longo-Pendy NM, et al. Trends in Escherichia coli and Klebsiella pneumoniae Urinary Tract Infections and Antibiotic Resistance over a 5-Year Period in Southeastern Gabon. Antibiotics (Basel). 2024; 14:14. 6. Prestinaci F, Pezzotti P, Pantosti A. Antimicrobial resistance: a global multifaceted phenomenon. Pathog Glob Health. 2015; 109:309-18. 7. Magiorakos AP, Srinivasan A, Carey RB, et al. Multidrug-resist- ant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012; 18:268-81. 8. Muteeb G, Kazi RNA, Aatif M, et al. Antimicrobial resistance: Linking molecular mechanisms to public health impact. SLAS Discov. 2025; 33:100232. 9. Alshakka M, Said K, Babakri M, et al. A Study on Antibiotics Prescribing Pattern at Outpatient Department in Four Hospitals in Aden-Yemen. Journal of Pharmacy Practice and Community Medicine. 2016; 2:88-93. 10. Badulla WFS, Alshakka M, Mohamed Ibrahim MI. Antimicrobial Resistance Profiles for Different Isolates in Aden, Yemen: A Cross- Sectional Study in a Resource-Poor Setting. Biomed Res Int. 2020; 2020:1810290. 11. Delepierre A, Gayot A, Carpentier A. Update on counterfeit antibiotics worldwide; public health risks. Med Mal Infect. 2012; 42:247-55. 12. Goldmann DA, Weinstein RA, Wenzel RP, et al. Strategies to Prevent and Control the Emergence and Spread of Antimicrobial- Resistant Microorganisms in Hospitals. A challenge to hospital lead- ership. Jama. 1996; 275:234-40. 13. Sudsakorn S, Bahadduri P, Fretland J, Lu C. 2020 FDA Drug- drug Interaction Guidance: A Comparison Analysis and Action Plan by Pharmaceutical Industrial Scientists. Curr Drug Metab. 2020; 21:403-26. 14. Cheesbrough M. District Laboratory Practice in Tropical Countries. 2 ed. Cambridge: Cambridge University Press; 2006. 15. Humphries R, Bobenchik AM, Hindler JA, Schuetz AN. Overview of Changes to the Clinical and Laboratory Standards Institute Performance Standards for Antimicrobial Susceptibility Testing, M100, 31st Edition. J Clin Microbiol. 2021; 59:e0021321. 16. Coque TM, Cantón R, Pérez-Cobas AE, et al. Antimicrobial Resistance in the Global Health Network: Known Unknowns and Challenges for Efficient Responses in the 21st Century. Microorganisms. 2023; 11:1050. 17. Aramalo SY, Akullo M, Oromcan BW. A cross-sectional prospec- tive study on antimicrobial resistance profiles of common bacterial pathogens causing urinary tract infections among patients among patients at Mengo Hospital,Kampala district. Student's Journal of Health Research Africa. 2025; 6:15. 18. Diriba A, Gizaw S, Alemu F, et al. Prevalence, antimicrobial sen- sitivity patterns and associated factors of urinary tract infection among patients attending Nekemte Comprehensive Specialized Hospital, Western Ethiopia, 2024: a cross-sectional study. BMC Infect Dis. 2025; 25:474. 19. Que AT, Tran AD, Trang THN, et al. Epidemiology and antimi- crobial resistance patterns of urinary tract infection: insights and strategies from a 5-year serial cross-sectional study in Vietnam. Ther Adv Infect Dis. 2025; 12:20499361251315346. 20. Ngai PV, Dat TH, Nhi LY, et al. Distribution and antifungal sus- ceptibility of Candida species causing vulvovaginal candidiasis and uri- nary tract infection in Medlatec healthcare system, Ha Noi city, Vietnam in 2023. Ther Adv Infect Dis. 2025; 12:20499361241311465. 21. Farag PF, Albulushi HO, Eskembaji MH, et al. Prevalence and antibiotic resistance profile of UTI-causing uropathogenic bacteria in diabetics and non-diabetics at the Maternity and Children Hospital in Jeddah, Saudi Arabia. Front Microbiol. 2024; 15:1507505. 22. Zhanel GG, Pozdirca M, Golden AR, et al. Sulopenem: An Intravenous and Oral Penem for the Treatment of Urinary Tract Infections Due to Multidrug-Resistant Bacteria. Drugs. 2022; 82:533-57. 23. Li J, Shi Y, Song X, et al. Mechanisms of Antimicrobial Resistance in Klebsiella: Advances in Detection Methods and Clinical Implications. Infect Drug Resist. 2025; 18:1339-54. 24. Pitout JD, Laupland KB. Extended-spectrum beta-lactamase- producing Enterobacteriaceae: an emerging public-health concern. Lancet Infect Dis. 2008; 8:159-66. 25. L B, Priya B, A E, P Shenoy R. Isolation and molecular charac- terization of multi-drug resistant uropathogenic Escherichia coli from urine samples: Insights into urinary tract infection management. The Microbe. 2024; 5:100185. 26. Mohanna MA, Raja'a YA. Frequency and treatment of urinary tract infection in children subjected to urine culture, in Sana'a, Yemen. J Ayub Med Coll Abbottabad. 2005; 17:20-2. 27. Nasher MA, Nasher TM, Gunaid AA. Etiologies of the urinary tract infections in a Yemeni City. Saudi Med J. 2001; 22:599-602. 28. Nakandi RM, Kakeeto P, Kihumuro RB, et al. Antibiotic suscep- tibility patterns of bacterial uropathogens at a private tertiary hospi- tal in Uganda: a retrospective study. BMC Infect Dis. 2025; 25:605. 29. Naidoo A, Kajee A, Mvelase NR, Swe-Han KS. Antimicrobial susceptibility of bacterial uropathogens in a South African regional hospital. Afr J Lab Med. 2023; 12:1920. 30. Barré SL, Weeda ER, Matuskowitz AJ, Hall GA, Weant KA. Risk Factors for Antibiotic Resistant Urinary Pathogens in Patients Discharged From the Emergency Department. Hosp Pharm. 2022; 57:462-8. 31. Rossignol L, Maugat S, Blake A, Vaux S, Heym B, Le Strat Y, et al. Risk factors for resistance in urinary tract infections in women in general practice: A cross-sectional survey. J Infect. 2015; 71:302-11. https://doi.org/10.1016/j.jinf.2015.05.012. 32. Ku JH, Bruxvoort KJ, Salas SB, et al. Multidrug Resistance of Escherichia coli From Outpatient Uncomplicated Urinary Tract Infections in a Large United States Integrated Healthcare Organization. Open Forum Infect Dis. 2023; 10:ofad287. 33. Khanal N, Cortie CH, Story C, et al. Multidrug resistance in uri- nary E. coli higher in males compared to females. BMC Urol. 2024; 24:255. 34. Wright SW, Wrenn KD, Haynes M, Haas DW. Prevalence and risk factors for multidrug resistant uropathogens in ED patients. Am J Emerg Med. 2000; 18:143-6. 35. Mohamed AH, Sheikh Omar NM, Osman MM, et al. Antimicrobial Resistance and Predisposing Factors Associated with Catheter-Associated UTI Caused by Uropathogens Exhibiting Multidrug-Resistant Patterns: A 3-Year Retrospective Study at a Tertiary Hospital in Mogadishu, Somalia. Trop Med Infect Dis. 2022; 7(3). 36. Alrasheedy M, Abousada HJ, Abdulhaq MM, et al. Prevalence of urinary tract infection in children in the kingdom of Saudi Arabia. Arch Ital Urol Androl. 2021; 93:206-10. Archivio Italiano di Urologia e Andrologia 2025; 97(3):14129 F. Ahmed, E. Alhamdani, S. Al-Wageeh, et al. 8 37. El-Agamy EI, Elhelaly MA, Abouelgreed TA, et al. Randomized comparison of effect of standard antibiotic prophylaxis versus enhanced prophylactic measures on rate of urinary tract infection after flexible ureteroscopy. Arch Ital Urol Androl. 2023; 95:11084. 38. Cai T, Tamanini I, Kulchavenya E, et al. The role of nutraceuti- cals and phytotherapy in the management of urinary tract infections: What we need to know? Arch Ital Urol Androl. 2017; 89:1-6. 39. Nitzan O, Elias M, Chazan B, Saliba W. Urinary tract infections in patients with type 2 diabetes mellitus: review of prevalence, diagno- sis, and management. Diabetes Metab Syndr Obes. 2015; 8:129-36. 40. Chen SL, Jackson SL, Boyko EJ. Diabetes mellitus and urinary tract infection: epidemiology, pathogenesis and proposed studies in animal models. J Urol. 2009; 182(6 Suppl):S51-6. 41. Bilsen MP, Jongeneel RMH, Schneeberger C, et al. Definitions of Urinary Tract Infection in Current Research: A Systematic Review. Open Forum Infect Dis. 2023; 10:ofad332. 42. Nelson Z, Aslan AT, Beahm NP, et al. Guidelines for the Prevention, Diagnosis, and Management of Urinary Tract Infections in Pediatrics and Adults: A WikiGuidelines Group Consensus Statement. JAMA Netw Open. 2024; 7:e2444495. Correspondence Faisal Ahmed (Corresponding Author) fmaaa2006@yahoo.com Ennayyat Alhamdani enayatalhamdani@gmail.com Khalil Al-Naggar alnajjarkh1234@gmail.com Department of Urology, School of Medicine, Ibb University, Ibb, Yemen Saleh Al-Wageeh alwajihsa78@gmail.com Qasem Alyhari qalyhary@hotmail.com Saif Ghabisha saifalighabisha@yahoo.com Department of General Surgery, School of Medicine, Ibb University, Ibb, Yemen Ahmed Ateik drahmedatik@gmail.com Department of General Surgery, School of Medicine, 21 September University, Sana'a, Yemen Ibrahim Alnadhari ibrahimah1978@yahoo.com Al Wakra Hospital, Hamad Medical Corporation, Al Wakra, Qatar & Department of Surgery, College of Medicine, Qatar University, Doha, Qatar Abdulghani Al-Hagri alhagriabdulghani@gmail.com Student Research Committee, Faculty of Medicine, Sana'a University, Sana'a, Yemen