Predictive Modeling of Multidrug Resistance in Female Urinary Tract Infections: Implications for Clinical Management

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Okoro, Emeka Okechukwu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5619455/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jun, 2025 Read the published version in Discover Public Health → Version 1 posted 11 You are reading this latest preprint version Abstract 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. MDR UTI Clinical predictors Escherichia coli Antimicrobial resistance Figures Figure 1 1 Background Urinary tract infections (UTIs) are a prevalent and significant health issue, particularly among women, due to their anatomical and physiological predispositions. These infections can lead to a range of complications, including recurrent infections and the development of antimicrobial resistance, which complicates treatment options [ 1 , 2 ]. The increasing rates of multidrug-resistant (MDR) uropathogens pose a critical challenge to healthcare systems worldwide, necessitating a comprehensive understanding of the factors contributing to resistance patterns [ 3 , 4 ]. The core aim of this study is to evaluate the prevalence and characteristics of UTIs among female patients while specifically predicting variables associated with multidrug resistance. By employing a cross-sectional design, this research seeks to analyze HVS specimens from a diverse cohort of female patients, spanning various age groups, to identify demographic, clinical, and microbiological factors that may influence the likelihood of encountering MDR organisms [ 5 , 6 ]. Understanding these associations is crucial for developing targeted interventions and refining empirical treatment guidelines, particularly in light of the rising prevalence of resistant strains such as Escherichia coli and Enterococcus faecalis [ 7 , 8 ]. Previous studies have highlighted the importance of identifying risk factors associated with MDR infections, as these insights can inform clinical decision-making and antimicrobial stewardship efforts [ 9 , 10 ]. For instance, factors such as previous antibiotic exposure, underlying health conditions, and demographic variables have been shown to correlate with increased risk for MDR infections [ 11 , 12 ]. Additionally, the role of mobile genetic elements in the dissemination of resistance genes among uropathogens further complicates the landscape of UTI management [ 13 , 14 ]. This study aims to fill the existing knowledge gaps by systematically investigating the prevalence of MDR organisms in UTIs and the associated risk factors. By utilizing advanced microbiological techniques, including culture and molecular methods, the research will enhance the detection of a wide range of uropathogens, thereby providing a more comprehensive understanding of the microbial landscape associated with UTIs [ 15 , 16 ]. The findings from this study are expected to contribute significantly to the existing body of literature on UTIs, offering valuable insights that can guide clinical practice and public health strategies aimed at mitigating the impact of antimicrobial resistance [ 17 , 18 ]. This study is designed to address the urgent need for a deeper understanding of the factors contributing to multidrug resistance in UTIs among female patients. By identifying and predicting these variables, the research aims to inform clinical guidelines and improve patient outcomes in the face of an escalating public health threat posed by resistant pathogens [ 19 ]. 2 Methods 2.1 Study Design and Population This study employed a cross-sectional design to evaluate the prevalence and characteristics of urinary tract infections (UTIs) among female patients presenting with related symptoms. A total of 824 HVS specimens were collected from females aged 0 to 79 years who visited the clinic with symptoms suggestive of UTIs. The study was conducted over a specified period, ensuring that all specimens were collected under similar conditions to minimize variability. The age distribution of the participants was categorized into specific groups: children (0–12 years), teenagers (13–19 years), young adults (20–39 years), middle-aged adults (40–59 years), older adults (60–79 years), and the elderly (80 + years). The majority of specimens were obtained from young adults, which reflects the demographic most commonly affected by UTIs. 2.2 Sample Collection and Processing HVS specimens were collected using sterile swab sticks and containers to prevent contamination. Each specimen was processed within two hours of collection to ensure the viability of the microorganisms. Upon receipt in the laboratory, specimens were subjected to macroscopic examination followed by culture on appropriate media to isolate potential pathogens while microscopic examination was done on centrifuged sediment. The specimens were inoculated onto blood agar, MacConkey agar, and CLED agar plates, and incubated at 37°C for 24 hours. Suspected candida colonies were subcultured on SDA. The use of multiple culture media allowed for the detection of a wide range of bacterial and fungal pathogens, thereby enhancing the sensitivity of the diagnostic process [ 20 ]. 2.3 Isolation and Identification of Pathogens Following incubation, colonies were examined for morphology, and suspected pathogens were subjected to further identification using biochemical tests and molecular methods. For bacterial identification, standard biochemical tests such as catalase, coagulase, and oxidase tests were performed. Additionally, the API system (bioMérieux) was utilized for the identification of Enterobacteriaceae and non-fermenting Gram-negative bacteria. For fungal identification, particularly Candida species, germ tube tests and chromogenic agar were employed to differentiate between species. To confirm the identity of the isolated organisms and detect specific pathogens, polymerase chain reaction (PCR) assays were performed. Primers specific to Escherichia coli , Proteus mirabilis , Pseudomonas aeruginosa, Staphylococcus aureus, Group B Streptococcus, Enterococcus faecalis and Candida albicans organisms isolated were, were utilized. The PCR conditions were optimized for each target, including denaturation at 95°C for 30 seconds, annealing at 55–60°C for 30 seconds, and extension at 72°C for 1 minute, followed by a final extension at 72°C for 5 minutes. The PCR products were analyzed using gel electrophoresis, and the presence of bands corresponding to the expected sizes confirmed the identity of the pathogens [ 21 ]. 2.4 Antimicrobial Susceptibility Testing Antimicrobial susceptibility testing was conducted using the disk diffusion method according to Clinical and Laboratory Standards Institute (CLSI) guidelines. Isolates were tested against a panel of antibiotics relevant to UTI treatment, including chloramphenicol (CH), erythromycin (E1Y/E), gentamicin (CN), ampicillin (APX), amoxicillin (AMX), ceftriaxone (CT1), sulfamethoxazole (S), and cefalexin (C1X). The results were interpreted based on the zone of inhibition, and multidrug resistance (MDR) was defined as resistance to three or more antibiotic classes [ 22 ]. 2.5 Data Analysis Descriptive statistics were employed to summarize the demographic and clinical characteristics of the study population. Chi-square tests were used to assess the association between age categories, clinical details, and the multidrug resistance status of the isolated organisms. To address potential biases in the model, particularly those arising from confounding variables, multivariable logistic regression was employed. This method allowed for the adjustment of various covariates, thus controlling for their potential confounding effects on the outcome. Logistic regression analysis was performed to identify predictors of MDR among the isolated pathogens. The model included age, clinical symptoms, and specific organisms as independent variables, with MDR status as the dependent variable. The significance level was set at p < 0.05 for all statistical tests [ 23 ]. 2.6 Ethical Considerations The study was conducted in accordance with ethical guidelines, and approval was obtained from the Research and Ethics committee of Federal University Teaching Hospital, Owerri, Nigeria prior to the commencement of the study. Informed consent was obtained from all participants or their guardians in the case of minors. Confidentiality of patient information was maintained throughout the study [ 24 ]. 3 Results 3.1 Distribution and Analysis of HVS Specimens by Age, Clinical Presentation, Pathogen, and MDR Status A total of 824 HVS specimens from females presenting with symptoms suggestive of urinary tract infections (UTIs) were analyzed. The age distribution revealed that the majority of participants were young adults (20–39 years), comprising 618 specimens (75.0%), followed by middle-aged adults (40–59 years) with 145 specimens (17.6%). Other age categories, including children (0–12 years) and teenagers (13–19 years), accounted for only 34 specimens (4.1%) combined, while older adults (60–79 years) and the elderly (80 + years) contributed a marginal 27 specimens (3.3%). The most common clinical presentation was symptoms of inflammatory conditions or asymptomatic cases, reported in 423 specimens (51.3%). Other prominent presentations included vaginal discharge and related conditions (175 specimens; 21.2%) and obstetric or pregnancy-related issues (95 specimens; 11.5%). Less frequent presentations involved pain and discomfort (38 specimens; 4.6%), gynecological or reproductive health conditions (68 specimens; 8.3%), and cysts or tumors (7 specimens; 0.8%). Bacterial and fungal pathogens were isolated in 687 specimens (83.4%), while 137 specimens (16.6%) showed no microbial growth. The most frequently isolated pathogens were Candida albicans (223 isolates; 27.1%) and Staphylococcus aureus (220 isolates; 26.7%), accounting for over half of the positive cultures. Other significant isolates included Escherichia coli (116 isolates; 14.1%) and Group B Streptococcus (94 isolates; 11.4%). Less common pathogens included Enterococcus faecalis (15 isolates; 1.8%), Proteus mirabilis (5 isolates; 0.6%), and Pseudomonas aeruginosa (6 isolates; 0.7%). Co-infections of E. coli and Candida albicans were identified in 8 specimens (1.0%). Among the 824 specimens, 643 cases (79.2%) were classified as non-multidrug-resistant (NON-MDR), while 181 cases (21.8%) exhibited multidrug resistance (MDR). The analysis presents the distribution of UTI specimens from female patients across various categories, including age groups, clinical details, and organisms, stratified by multidrug resistance (MDR) status. A total of 824 female specimens were analyzed, with 643 (78.0%) classified as non-MDR and 181 (21.8%) as MDR. The majority of specimens were from young adults (20–39 years), representing 75.0% of the sample. Of these, 21.8% were MDR. A smaller proportion of specimens came from older age groups, with 2.8% from older adults (60–79 years) and 0.5% from elderly individuals (80 + years). Chi-square analysis for the association between age category and MDR status showed no significant difference (χ2 = 2.825,p = 0.985 \chi^2 = 2.825, p = 0.985χ2 = 2.825,p = 0.985), suggesting that age did not significantly influence MDR rates in this female cohort (table 3.1).. The most frequent clinical details reported were symptoms of inflammatory conditions (51.3%), followed by vaginal discharge and related conditions (21.2%). Other conditions included gynecological health issues (8.3%), obstetric conditions (11.5%), and cysts and tumors (0.8%). The proportion of MDR cases within each clinical detail category was consistent, ranging from 15.4–22.9%. Chi-square analysis revealed no significant association between clinical details and MDR status (χ2 = 1.964,p = 0.962 \chi^2 = 1.964, p = 0.962χ2 = 1.964,p = 0.962), indicating that clinical symptoms did not strongly correlate with multidrug resistance in this female patient population. The most commonly isolated organisms were Candida albicans (27.1%) and Staphylococcus aureus (26.7%). There were no MDR results for Candida albican as it was not a bacterium. Enterococcus faecalis exhibited the highest MDR rate (40.0%), while Pseudomonas aeruginosa had a lower MDR rate (16.7%). The Chi-square test for the association between organism type and MDR status showed a significant relationship (χ2 = 119.109, p < 0.001, X 2 = 119.109, p < 0.001χ2 = 119.109,p < 0.001), indicating that the type of organism is significantly associated with the likelihood of being multidrug resistant (table 3.1). Table 1 Combined Descriptive and Analytical Results For UTI Specimens Variable Category Frequency (%) MDR (%) Chi-Square (χ2) df p-value Age Categories Children: 0–12 years 18 (2.2) 16.7 2.825 10 0.985 Teenagers: 13–19 years 16 (1.9) 31.2 Young Adults: 20–39 years 618 (75.0) 21.8 Middle-aged Adults: 40–59 years 145 (17.6) 21.4 Older Adults: 60–79 years 23 (2.8) 26.1 Elderly: 80 + years 4 (0.5) 0.0 Clinical Details Inflammatory Conditions 423 (51.3) 22.9 1.964 7 0.962 Vaginal Discharge and Related Conditions 175 (21.2) 22.9 Gynecological and Reproductive Health 68 (8.3) 20.6 Obstetric Conditions and Pregnancy Issues 95 (11.5) 20.0 Cysts and Tumours 7 (0.8) 28.6 Pain and Discomfort 38 (4.6) 15.8 Other Clinical Conditions and Symptoms 13 (1.6) 15.4 Other Medical Conditions and Procedures 5 (0.6) 20.0 Organism No Growth 137 (16.6) 119.109 16 < 0.001 Escherichia coli 116 (14.1) 39.1 Proteus mirabilis 5 (0.6) 20.0 Pseudomonas aeruginosa 6 (0.7) 16.7 Staphylococcus aureus 220 (26.7) 35.9 Group B Streptococcus 94 (11.4) 39.4 Enterococcus faecalis 15 (1.8) 40.0 Candida albicans 223 (27.1) E. coli and C. albicans 8 (1.0) Total 824 180 (21.8) 3.2 Logistic Regression Analysis of Multidrug Resistance Predictors The logistic regression analysis (Table 2 ) indicates that the age categories (Children: 0–12 years, Teenagers: 13–19, Young Adults: 20–39, Middle-aged Adults: 40–59, Older Adults: 60–79, and Elderly: 80 + years) do not show statistically significant contributions to the outcome variable, as evidenced by their high p-values (0.999) and large standard errors. This suggests that age has no meaningful association with the dependent variable in this dataset. Regarding clinical symptoms, individuals with vaginal discharge and related conditions have an odds ratio (EXP(B)) of 1.071, indicating nearly the same likelihood of the outcome as those without this condition, but this finding is not statistically significant (p = 0.955). For gynecological and reproductive health issues, the odds ratio is 1.118, but this is also not significant (p = 0.927), suggesting no meaningful relationship with the outcome. Obstetric and pregnancy-related issues have an odds ratio of 0.958, with no significant impact (p = 0.972). Cysts and tumors, however, show a statistically significant result with an odds ratio of 0.92 (p = 0.046), indicating a slight reduction in the likelihood of the outcome. Pain and discomfort have an odds ratio of 1.075, but this is not significant (p = 0.961), suggesting no substantial influence. Other clinical conditions show an odds ratio of 0.617, which is also not statistically significant (p = 0.710), indicating no meaningful association. Candida albican being associated with UTI was excluded from the regression model due to its classification as a fungus rather than a bacterium, which precludes a direct association with MDR in the context of this analysis. Among the organisms, Escherichia coli (E. coli) is the only significant predictor, with an odds ratio of 1.38 (p < 0.001), indicating a strong positive association with the likelihood of the outcome. This suggests that the presence of E. coli increases the likelihood of MDR occurring. In contrast, other organisms such as Proteus mirabilis , Pseudomonas aeruginosa , Staphylococcus aureus , and Group B Streptococcus did not show statistically significant associations, with p-values greater than 0.05. Similarly, Enterococcus faecalis did not exhibit a significant effect, with an odds ratio of 0.595 (p = 0.493), indicating no meaningful association with the outcome. The overall model suggests that, apart from E. coli and cysts or tumors, most variables do not significantly affect the outcome. Additionally, the constant term (intercept) indicates negligible odds of the outcome in the absence of predictors, with an EXP(B) value approaching zero. Some variables, particularly age categories, have large standard errors, indicating possible data quality issues or multicollinearity within the model. Table 2 Logistic Regression of Age, Symptoms, and Organisms Associated with MDR Variables in the Equation B Standard Error Wald df Sig. EXP (B) 95% C.I.for EXP(B) Lower Upper Step 1 a Children: 0–12 years 3.098 5 0.685 Teenagers (13–19) 19.999 19003.751 0.000 1 0.999 484485328.357 .000 . Young Adults: (20–39) 20.255 19003.751 0.000 1 0.999 626023752.619 .000 . Middle-aged Adults: 40–59 years 19.730 19003.751 0.000 1 0.999 370501381.725 .000 . Older Adults: 60–79 years 19.408 19003.751 0.000 1 0.999 268501617.198 .000 . Elderly: 80 + years and above 19.427 19003.751 0.000 1 0.999 273597294.605 .000 . Symptoms of Inflammatory Conditions or no symptoms at all 2.153 7 0.951 Vaginal Discharge and Related Conditions 0.068 1.208 0.003 1 0.955 1.071 .100 11.434 Gynecological and Reproductive Health 0.111 1.221 0.008 1 0.927 1.118 .102 12.233 Obstetric Conditions and Pregnancy-Related Issues -0.043 1.246 0.001 1 0.972 .958 .083 11.011 Cysts and Tumours 0.83 1.238 0.005 1 0.046 .920 .081 10.410 Pain and Discomfort 0.072 1.497 0.002 1 0.961 1.075 .057 20.196 Other Clinical Conditions and Symptoms -0.483 1.302 0.138 1 0.710 .617 .048 7.910 Other Medical Conditions and Procedures -0.577 1.451 0.158 1 0.091 .562 .033 9.649 NO GROWTH 86.363 8 0.99 Escherichia coli 3.220 .839 14.750 1 0.000 .040 1.38 2.07 Proteus mirabilis -0.894 .754 1.405 1 0.236 .409 .093 1.794 Pseudomonas aeruginosa -1.512 1.332 1.288 1 0.256 .220 .016 3.002 Staphylococcus aureus -1.585 1.355 1.368 1 0.242 .205 .014 2.920 Group B Streptococcus -0.667 .738 .818 1 0.366 .513 .121 2.178 Enterococcus faecalis 0.519 .756 .471 1 0.0493 .595 .135 2.619 Constant -19.599 19003.751 .000 1 0.999 .000 a. Variable(s) entered on step 1: AgeCat, CLINICALDETAILS, ORGANISM. 3.3 Model Performance and Fit Analysis The Omnibus Tests of Model Coefficients (Table 3 ) show that the model is statistically significant, with a Chi-square value of 135.355 (df = 20, p < 0.001). This indicates that the predictors included in the model significantly improve the prediction of the outcome compared to a null model with no predictors. The Model Summary (Table 4 ) reports a -2 Log Likelihood value of 732.280, which is a measure of model fit. The Cox & Snell R Square value is 0.151, and the Nagelkerke R Square value is 0.233, suggesting that the predictors explain 15.1–23.3% of the variance in the outcome. However, the estimation terminated at the maximum number of iterations, suggesting potential convergence issues that may affect the reliability of the results. The Classification (Table 5 ) indicates the performance of the model in predicting the outcome categories (NON-MDR vs. MDR). The model correctly classifies 99.2% of NON-MDR cases and 2.8% of MDR cases, with an overall classification accuracy of 78.0%. However, the low sensitivity (ability to correctly identify MDR cases) indicates that the model performs poorly in predicting this category, likely due to imbalanced data or insufficient predictors. The Area Under the Curve (AUC) (Table 6 ) is 0.753 (p < 0.001) with a 95% confidence interval ranging from 0.716 to 0.789. This indicates acceptable discriminatory ability of the model, meaning it is reasonably good at distinguishing between MDR and NON-MDR outcomes. However, the presence of ties between positive and negative groups suggests that the model's discrimination may not be optimal. The Hosmer and Lemeshow Test (Table 7 ) reports a Chi-square value of 2.506 (df = 8, p = 0.961), indicating that the model fits the data well. A non-significant p-value suggests that there is no significant difference between observed and predicted outcomes, supporting the adequacy of the model. Overall, while the model is statistically significant and shows reasonable discrimination (AUC), its predictive power is moderate (low R-squared values), and it struggles with correctly classifying MDR cases. The termination at maximum iterations suggests a need for further investigation into convergence issues, possibly by refining the model or addressing data limitations. Table 3 Omnibus Tests of Model Coefficients Chi-square df Sig. Step 1 Step 135.355 20 .000 Block 135.355 20 .000 Model 135.355 20 .000 Table 4 Model Summary Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 732.280 a .151 .233 a. Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found. Table 5 Classification Table a Observed Predicted MDR Percentage Correct NON-MDR MDR Step 1 MDR_ NON-MDR 638 5 99.2 MDR 176 5 2.8 Overall Percentage 78.0 a. The cut value is .500 Table 6 Area Under the Curve Test Result Variable(s): Predicted probability Area Std. Error a Asymptotic Sig. b Asymptotic 95% Confidence Interval Lower Bound Upper Bound .753 .019 .000 .716 .789 The test result variable(s): Predicted probability has at least one tie between the positive actual state group and the negative actual state group. Statistics may be biased. a. Under the nonparametric assumption b. Null hypothesis: true area = 0.5 Table 7 Hosmer and Lemeshow Test Step Chi-square df Sig. 1 2.506 8 .961 4 Discussion The study of urinary tract infections (UTIs) among female patients presents a critical examination of the prevalence, characteristics, and antimicrobial resistance patterns of uropathogens. The cross-sectional design utilized in this investigation is particularly effective for capturing a snapshot of the current state of UTIs, allowing for a comprehensive understanding of the demographic and clinical factors associated with these infections. The findings indicate a significant prevalence of multidrug-resistant (MDR) organisms, particularly Enterococcus faecalis , which poses a considerable challenge to treatment and underscores the need for ongoing surveillance and updated clinical guidelines [ 1 – 3 ]. The demographic analysis revealed that the majority of specimens were collected from young adults aged 20–39 years, a finding that aligns with previous studies indicating that this demographic is particularly susceptible to UTIs [ 4 , 5 ]. The low representation of older adults and the elderly may reflect healthcare-seeking behaviors or the clinical presentation of UTIs in these populations. For instance, research has shown that older adults often present with atypical symptoms, which may lead to underdiagnosis [ 6 , 7 ]. This highlights the importance of tailored screening and diagnostic approaches for different age groups to ensure timely and appropriate treatment. The methodology employed for sample collection and processing was rigorous, utilizing sterile containers and prompt processing to minimize contamination. The use of multiple culture media for pathogen isolation is a well-established practice that enhances the detection of a broad spectrum of uropathogens, thereby increasing the sensitivity of the diagnostic process [ 8 , 9 ]. The isolation of both bacterial and fungal pathogens underscores the polymicrobial nature of UTIs, with Candida albicans and Staphylococcus aureus being the most frequently identified organisms. This finding is consistent with previous studies that have reported similar trends in pathogen distribution [ 10 , 11 ]. The study's findings regarding antimicrobial susceptibility testing are particularly concerning. The high prevalence of MDR organisms highlights the urgent need for effective antimicrobial stewardship and the development of new treatment strategies [ 12 , 13 ]. The disk diffusion method employed for susceptibility testing is a standard practice that provides valuable insights into the resistance patterns of uropathogens. The identification of MDR strains necessitates a reevaluation of empirical treatment guidelines, especially in light of the increasing resistance to commonly used antibiotics such as amoxicillin and ciprofloxacin [ 14 , 15 ]. Statistical analysis revealed no significant association between age categories, clinical details, and MDR status, suggesting that factors other than age or specific clinical presentations may play a more critical role in the development of multidrug resistance [ 16 , 17 ]. This finding is particularly relevant in the context of the increasing complexity of UTIs and the need for personalized treatment approaches that consider individual patient factors, including previous antibiotic exposure and underlying health conditions [ 18 , 19 ]. The lack of significant predictors among other organisms suggests that the dynamics of resistance may be more complex than previously understood, warranting further investigation into the genetic and environmental factors contributing to resistance [ 25 ]. The results of the logistic regression analysis indicate that age categories do not significantly contribute to the likelihood of multidrug resistance (MDR) in urinary tract infections (UTIs). The high p-values (0.999) and large standard errors associated with age suggest that age is not a meaningful predictor in this dataset. This finding aligns with previous research that has reported inconsistent associations between age and UTI outcomes, particularly in older populations where atypical presentations may complicate diagnosis and treatment [ 25 ]. The lack of significant findings across various age groups may reflect the complex interplay of factors influencing UTI susceptibility and resistance patterns, which are not solely dependent on age but may also involve behavioral, environmental, and genetic factors [ 1 ]. In terms of clinical symptoms, the analysis reveals that common presentations such as vaginal discharge and gynecological issues do not significantly correlate with MDR status, as indicated by their odds ratios (1.071 and 1.118, respectively) and high p-values (0.955 and 0.927). This suggests that these symptoms may not be reliable indicators of MDR in the context of UTIs. Interestingly, cysts and tumors showed a statistically significant association with a slight reduction in the likelihood of MDR (odds ratio of 0.92, p = 0.046), indicating that patients with these conditions may have a different risk profile for developing MDR organisms [ 26 ]. This finding warrants further investigation into the underlying mechanisms that may contribute to this association. Among the organisms analyzed, Escherichia coli emerged as a significant predictor of MDR status, with an odds ratio of 1.38 (p < 0.001), indicating a association with the likelihood of being multidrug resistant. This finding is particularly noteworthy given that E. coli is the most common uropathogen in UTIs. The results suggest that the presence of E. coli is associated with a lower likelihood of encountering MDR strains, which may reflect its role as a primary pathogen in uncomplicated UTIs [ 1 ]. Conversely, other organisms such as Proteus mirabilis, Pseudomonas aeruginosa , Staphylococcus aureus , and Group B Streptococcus did not show significant associations with MDR status, indicating that the dynamics of resistance may vary significantly among different uropathogens [ 27 ]. Overall, the logistic regression model indicates that, apart from E. coli and cysts or tumors, most variables do not significantly influence the outcome of MDR in UTIs. The presence of large standard errors for some predictors, particularly age categories, suggests potential data quality issues or multicollinearity within the model, which may affect the reliability of the results [ 25 ]. This highlights the need for further research to explore the multifactorial nature of UTIs and the development of resistance, as well as the importance of refining clinical guidelines to better address the complexities of UTI management in diverse patient populations.). The implications of this study extend beyond the immediate findings, as they contribute to a broader understanding of the challenges posed by UTIs in the context of rising antimicrobial resistance. The need for innovative treatment strategies, including the exploration of non-antibiotic approaches for disease prevention and control, is increasingly recognized [ 28 , 29 ]. Additionally, the integration of patient-centered care models that consider individual preferences and experiences may enhance treatment adherence and outcomes [ 30 , 31 ]. Moreover, the findings of this study resonate with global trends in antimicrobial resistance, as highlighted in various international studies. For instance, research conducted in different regions has demonstrated similar patterns of resistance among uropathogens, emphasizing the need for a coordinated global response to combat this public health threat [ 32 , 33 ]. The establishment of robust surveillance systems and the promotion of responsible antibiotic use are critical components of this response [ 34 , 35 ]. Conclusion This study sheds light on the prevalence of multidrug-resistant (MDR) organisms in urinary tract infections (UTIs) among female patients, revealing significant insights into the clinical predictors associated with these infections. The findings indicate that a substantial proportion of the isolated pathogens were MDR, particularly Enterococcus faecalis, which poses a considerable challenge for effective treatment. The demographic analysis highlighted that young adults (ages 20–39) were predominantly affected, aligning with existing literature that underscores this group's vulnerability to UTIs. The study's methodology, which included rigorous sample collection and processing, allowed for a comprehensive assessment of the microbial landscape associated with UTIs. The identification of both bacterial and fungal pathogens emphasizes the polymicrobial nature of these infections, necessitating a multifaceted approach to diagnosis and treatment. Notably, the logistic regression analysis identified Escherichia coli as a significant predictor of MDR status, reinforcing its role as a primary uropathogen in UTIs. Despite the moderate explanatory power of the predictive model, the low sensitivity for classifying MDR cases indicates a need for further refinement of predictive models to enhance their clinical applicability. The lack of significant associations between age, clinical details, and MDR status suggests that other factors, such as previous antibiotic exposure and underlying health conditions, may play a more critical role in the development of resistance. Abbreviations MDR Multidrug-Resistant UTI Urinary Tract Infection HVS High Vaginal Swab PCR Polymerase Chain Reaction CLSI Clinical and Laboratory Standards Institute AUC Area Under the Curve AMR Antimicrobial Resistance CLED Cystine-Lactose-Electrolyte-Deficient Agar API Analytical Profile Index Declarations Ethics approval and consent to participate: Ethical clearance for this study was obtained from the Federal University Teaching Hospital, Owerri, Imo State (FMC/OW/HREC/VOL.1/34-01661). Consent for publication: Not applicable. Competing interests : The authors declare no competing interests. Funding: No specific funding was received for this study. Author Contribution F.C. I. performed the statistical analysis, wrote, corrected and reviewed the paper, C.I.O. and E.O. conducted the laboratory investigation, M.M. Ozoude and Marwizi, F.M. contributed to the writing and review of the manuscript. Acknowledgement We acknowledge the support of the Federal University Teaching Hospital, Owerri, Imo State. Data Availability The data utilized in this study are confidential in accordance with the ethical approval policy but can be made available by the corresponding authors upon reasonable request. References Flores-Mireles AL, Walker JN, Caparon MG, Hultgren SJ. Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol. 2015;13(5):269–84. https://doi.org/10.1038/nrmicro3432 . Heidar NA, Degheili JA, Yacoubian A, Khauli RB. Management of urinary tract infection in women: a practical approach for everyday practice. Urol Annals. 2019;11(4):339. https://doi.org/10.4103/ua.ua_104_19 . Tezcan Ş, Uslu N, Soy EHA, Haberal M. Untitled Experimental Clin Transplantation. 2017;15(Suppl 1). https://doi.org/10.6002/ect.mesot2016.p118 . Hassan MM, Malik M, Saleem R, Saleem A, Zohaib K, Malik AY, Javaid M. (2022). Efficacy of single dose of fosfomycin versus a five-day course of ciprofloxacin in patients with uncomplicated urinary tract infection. Cureus. https://doi.org/10.7759/cureus.24843 Gondos AS, Al-Moyed KA, Al-Robasi ABA, Al-Shamahy HA, Alyousefi NA. Urinary tract infection among renal transplant recipients in yemen. PLoS ONE. 2015;10(12):e0144266. https://doi.org/10.1371/journal.pone.0144266 . Guzmán M, Salazar E, Cordero V, Castro A, Villanueva A, Rodulfo H, Donato MD. Multidrug resistance and risk factors associated with community-acquired urinary tract infections caused by escherichia coli in venezuela. Biomédica. 2019;39:96–107. https://doi.org/10.7705/biomedica.v39i2.4030 . Cao D, Shen Y, Huang Y, Chen B, Chen Z, Ai J, Wei Q. Levofloxacin versus ciprofloxacin in the treatment of urinary tract infections: evidence-based analysis. Front Pharmacol. 2021;12. https://doi.org/10.3389/fphar.2021.658095 . Anesi JA, Lautenbach E, Nachamkin I, Garrigan C, Bilker WB, Wheeler MK, Han JH. Clinical and molecular characterization of community-onset urinary tract infections due to extended-spectrum cephalosporin-resistant enterobacteriaceae. Infect Control &Amp Hosp Epidemiol. 2016;37(12):1433–9. https://doi.org/10.1017/ice.2016.225 . Bitew A, Molalign T, Chanie M. Species distribution and antibiotic susceptibility profile of bacterial uropathogens among patients complaining urinary tract infections. BMC Infect Dis. 2017;17(1). https://doi.org/10.1186/s12879-017-2743-8 . Haider I, Ullah S, Bibi K, Naeem HS, Khan HA, Khan WM. Multi-drug resistant escherichia coli and their sensitivity to oral fosfomycin in urinary tract infections: a single-center experience. J Med Sci. 2023;31(02):97–101. https://doi.org/10.52764/jms.23.31.2.1 . Daga MK, Mawari G, Wasi S, Kumar N, Sharma U, Hussain MA. (2021). Current pattern and clinico-bacteriological profile of healthcare associated infections (hai) in an icu setting: an observational study. https://doi.org/10.21203/rs.3.rs-874099/v1 Tyne DV, Gilmore MS. Friend turned foe: evolution of enterococcal virulence and antibiotic resistance. Annu Rev Microbiol. 2014;68(1):337–56. https://doi.org/10.1146/annurev-micro-091213-113003 . Ibrahim Z, Behiry A, Attia O, El-sayed H. Evaluation of in vitro effect of fosfomycin on resistant gram-negative pathogens in urinary tract infection. Microbes Infect Dis. 2022;0(0):0–0. https://doi.org/10.21608/mid.2022.127574.1259 . Yasin F, Assad S, Talpur AS, Zahid M, Malik SA. (2017). Combination therapy for multidrug-resistant klebsiella pneumoniae urinary tract infection. Cureus. https://doi.org/10.7759/cureus.1503 Gopichand, P., Agarwal, G., Natarajan, M., Mandal, J., Deepanjali, S., Parameswaran,S., … Dorairajan, L. N. (2019). in vitro effect of fosfomycin on multi-drug resistant gram-negative bacteria causing urinary tract infections. Infection and Drug Resistance, Volume 12, 2005–2013. https://doi.org/10.2147/idr.s207569. Shen L, H, W., Jiang Y. A case report: intermittent catheterization combined with rehabilitation in the treatment of carbapenem-resistant klebsiella pneumoniae catheter-associated urinary tract infection. Front Cell Infect Microbiol. 2022;12. https://doi.org/10.3389/fcimb.2022.1027576 . Alkhouri JS, Santiago F, Guzman-Cole C, Garsevanyan S, Sindi S, Barlow M. Molecular surveillance and assessment of ceftolozane/tazobactam resistance with common β-lactam antibiotics and β-lactamase genes. J Clin &Amp Biomedical Res. 2021;1–8. https://doi.org/10.47363/jcbr/2021(3)136 . Kasew D, Desalegn B, Aynalem M, Tila S, Diriba D, Afework B, Baynes HW. Antimicrobial resistance trend of bacterial uropathogens at the university of gondar comprehensive specialized hospital, northwest ethiopia: a 10 years retrospective study. PLoS ONE. 2022;17(4):e0266878. https://doi.org/10.1371/journal.pone.0266878 . Khadgi S, Timilsina U, Shrestha B. Plasmid profiling of multidrug resistant escherichia coli strains isolated from urinary tract infection patients. Int J Appl Sci Biotechnol. 2013;1(1):1–4. https://doi.org/10.3126/ijasbt.v1i1.7918 . Bach F. (2010). Self-concordant analysis for logistic regression. Electronic Journal of Statistics, 4(none). https://doi.org/10.1214/09-ejs521 Rani D, Krishan K, Kanchan T. A methodological comparison of discriminant function analysis and binary logistic regression for estimating sex in forensic research and case-work. Med Sci Law. 2022;63(3):227–36. https://doi.org/10.1177/00258024221136687 . Tenga A, Ronglan L, Bahr R. Measuring the effectiveness of offensive match-play in professional soccer. Eur J Sport Sci. 2010;10(4):269–77. https://doi.org/10.1080/17461390903515170 . Twisk J, Vente W, Apeldoorn A, Boer M. Should we use logistic mixed model analysis for the effect estimation in a longitudinal rct with a dichotomous outcome variable? Epidemiol Biostatistics Public Health. 2022;14(3). https://doi.org/10.2427/12613 . Denœux T. (2018). Logistic regression revisited: belief function analysis., 57–64. https://doi.org/10.1007/978-3-319-99383-6_8 Caterino JM, Ting SA, Sisbarro SG, Espinola JA, Camargo CA. Age, nursing home residence, and presentation of urinary tract infection in u.s. emergency departments, 2001–2008. Acad Emerg Med. 2012;19(10):1173–80. https://doi.org/10.1111/j.1553-2712.2012.01452.x . Rose A, Thimme A, Halfar C, Nehen HG, Rübben H. Severity of urinary incontinence of nursing home residents correlates with malnutrition, dementia and loss of mobility. Urol Int. 2013;91(2):165–9. https://doi.org/10.1159/000348344 . Newby B, Ramesh KK. Urinary tract infection in a preterm neonate caused by lactococcus lactis. Can J Hosp Pharm. 2014;67(6). https://doi.org/10.4212/cjhp.v67i6.1409 . Bai S, He H, Han C, Yang M, Bi X, Fan W. What makes a theme park experience less enjoyable? evidence from online customer reviews of disneyland china. Front Psychol. 2023;14. https://doi.org/10.3389/fpsyg.2023.1120483 . Ningsih A. Exploring the challenges of digital textbooks in reading comprehension. Klausa (Kajian Linguistik Pembelajaran Bahasa Dan Sastra). 2023;7(2):29–36. https://doi.org/10.33479/klausa.v7i2.844 . Gasparini L, Tsuji S, Bergmann C. Ten easy steps to conducting transparent, reproducible meta-analyses for infant researchers. Infancy. 2022;27(4):736–64. https://doi.org/10.1111/infa.12470 . Fogel J, Ustoyev S. Social media advertisements with deposit contracts and fitness club/gym membership: are consumers persuaded? J Consumer Mark. 2020;38(1):27–38. https://doi.org/10.1108/jcm-02-2020-3621 . Tong Q, Hammer K, Johnson E, Zegarra M, Goto M, Lo T. (2018). A systematic review and meta-analysis on the use of prophylactic topical antibiotics for the prevention of uncomplicated wound infections. Infection and Drug Resistance, 11, 417–25. https://doi.org/10.2147/idr.s151293 Hill M, Laughter M, Harmange C, Dellavalle R, Rundle C, Dunnick C. The contact dermatitis quality of life index (cdql): survey development and content validity assessment. Jmir Dermatology. 2021;4(2):e30620. https://doi.org/10.2196/30620 . Reinhold M, Bürkner P, Holling H. Effects of expressive writing on depressive symptoms-a meta-analysis. Clin Psychol Sci Pract. 2018;25(1):e12224. https://doi.org/10.1111/cpsp.12224 . Vandenbroucke J, Pearce N. From ideas to studies: how to get ideas and sharpen them into research questions. Clin Epidemiol. 2018;10:253–64. https://doi.org/10.2147/clep.s142940 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5619455","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":391369601,"identity":"f6164dde-0732-4294-a130-5e5f903ee5d6","order_by":0,"name":"Francis Chukwuebuka Ihenetu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACNgkIzQ8mExhsgCRj4wHCWhIYJBsgdBpISwNeLQwoWhgYDoNJvFr4pJuPPfj447CEefvZgx8e/Dlvt7b9MNCWGptonA6TOZZuOCPhsITMmbxkicS228nbziQCtRxLy23A6ZccM2mehMN1Egw5ZgyJDbeTzQ4AtTA2HMajJf+b9B+gLRL8b8wYEv6cSzY7/5CQlhw2aQaQFqB1DAlsB+zMbhC0Jc1MsictHajljTHQL8kJZjeAtiTg8Yv8jORnEj9srIEOyzH8+OOPnb3Z+fSHDz7U2ODUggESwSoTiFUOAvakKB4Fo2AUjIKRAQCELl3zaguRIQAAAABJRU5ErkJggg==","orcid":"","institution":"Imo State University","correspondingAuthor":true,"prefix":"","firstName":"Francis","middleName":"Chukwuebuka","lastName":"Ihenetu","suffix":""},{"id":391369602,"identity":"70754deb-c368-4b9e-ae42-06f214374e28","order_by":1,"name":"Chinyere I. Okoro","email":"","orcid":"","institution":"Federal University Teaching Hospital Owerri","correspondingAuthor":false,"prefix":"","firstName":"Chinyere","middleName":"I.","lastName":"Okoro","suffix":""},{"id":391369603,"identity":"fae44a78-cbdf-4dcc-b2a7-8498141a3912","order_by":2,"name":"Emeka Okechukwu","email":"","orcid":"","institution":"Rhema University","correspondingAuthor":false,"prefix":"","firstName":"Emeka","middleName":"","lastName":"Okechukwu","suffix":""},{"id":391369604,"identity":"62ab0400-a60e-4d99-a42d-38d8679fdbb7","order_by":3,"name":"Makuochukwu Maryann Ozoude","email":"","orcid":"","institution":"Zaporizhzhia State Medical and Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Makuochukwu","middleName":"Maryann","lastName":"Ozoude","suffix":""},{"id":391369605,"identity":"70922aa8-ef66-478a-b49c-457306845bd2","order_by":4,"name":"Farirai Melania Marwizi","email":"","orcid":"","institution":"Internal Medicine, St. John’s Episcopal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Farirai","middleName":"Melania","lastName":"Marwizi","suffix":""}],"badges":[],"createdAt":"2024-12-10 21:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5619455/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5619455/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12982-025-00762-9","type":"published","date":"2025-06-25T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72286711,"identity":"c31ba45f-3b36-46e8-a8f5-caf8d1a32b1a","added_by":"auto","created_at":"2024-12-24 17:06:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC of the Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5619455/v1/3822f4fbf16fa1a33fd99eb5.png"},{"id":85686053,"identity":"96937bbd-b101-4416-bccb-7bb06381b8f6","added_by":"auto","created_at":"2025-06-30 16:01:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1192731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5619455/v1/87fd2a46-3676-4e7b-92c8-3130ff20c132.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Modeling of Multidrug Resistance in Female Urinary Tract Infections: Implications for Clinical Management","fulltext":[{"header":"1 Background","content":"\u003cp\u003eUrinary tract infections (UTIs) are a prevalent and significant health issue, particularly among women, due to their anatomical and physiological predispositions. These infections can lead to a range of complications, including recurrent infections and the development of antimicrobial resistance, which complicates treatment options [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The increasing rates of multidrug-resistant (MDR) uropathogens pose a critical challenge to healthcare systems worldwide, necessitating a comprehensive understanding of the factors contributing to resistance patterns [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe core aim of this study is to evaluate the prevalence and characteristics of UTIs among female patients while specifically predicting variables associated with multidrug resistance. By employing a cross-sectional design, this research seeks to analyze HVS specimens from a diverse cohort of female patients, spanning various age groups, to identify demographic, clinical, and microbiological factors that may influence the likelihood of encountering MDR organisms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Understanding these associations is crucial for developing targeted interventions and refining empirical treatment guidelines, particularly in light of the rising prevalence of resistant strains such as \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have highlighted the importance of identifying risk factors associated with MDR infections, as these insights can inform clinical decision-making and antimicrobial stewardship efforts [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For instance, factors such as previous antibiotic exposure, underlying health conditions, and demographic variables have been shown to correlate with increased risk for MDR infections [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, the role of mobile genetic elements in the dissemination of resistance genes among uropathogens further complicates the landscape of UTI management [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to fill the existing knowledge gaps by systematically investigating the prevalence of MDR organisms in UTIs and the associated risk factors. By utilizing advanced microbiological techniques, including culture and molecular methods, the research will enhance the detection of a wide range of uropathogens, thereby providing a more comprehensive understanding of the microbial landscape associated with UTIs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The findings from this study are expected to contribute significantly to the existing body of literature on UTIs, offering valuable insights that can guide clinical practice and public health strategies aimed at mitigating the impact of antimicrobial resistance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study is designed to address the urgent need for a deeper understanding of the factors contributing to multidrug resistance in UTIs among female patients. By identifying and predicting these variables, the research aims to inform clinical guidelines and improve patient outcomes in the face of an escalating public health threat posed by resistant pathogens [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional design to evaluate the prevalence and characteristics of urinary tract infections (UTIs) among female patients presenting with related symptoms. A total of 824 HVS specimens were collected from females aged 0 to 79 years who visited the clinic with symptoms suggestive of UTIs. The study was conducted over a specified period, ensuring that all specimens were collected under similar conditions to minimize variability. The age distribution of the participants was categorized into specific groups: children (0\u0026ndash;12 years), teenagers (13\u0026ndash;19 years), young adults (20\u0026ndash;39 years), middle-aged adults (40\u0026ndash;59 years), older adults (60\u0026ndash;79 years), and the elderly (80\u0026thinsp;+\u0026thinsp;years). The majority of specimens were obtained from young adults, which reflects the demographic most commonly affected by UTIs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample Collection and Processing\u003c/h2\u003e \u003cp\u003eHVS specimens were collected using sterile swab sticks and containers to prevent contamination. Each specimen was processed within two hours of collection to ensure the viability of the microorganisms. Upon receipt in the laboratory, specimens were subjected to macroscopic examination followed by culture on appropriate media to isolate potential pathogens while microscopic examination was done on centrifuged sediment. The specimens were inoculated onto blood agar, MacConkey agar, and CLED agar plates, and incubated at 37\u0026deg;C for 24 hours. Suspected candida colonies were subcultured on SDA. The use of multiple culture media allowed for the detection of a wide range of bacterial and fungal pathogens, thereby enhancing the sensitivity of the diagnostic process [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Isolation and Identification of Pathogens\u003c/h2\u003e \u003cp\u003eFollowing incubation, colonies were examined for morphology, and suspected pathogens were subjected to further identification using biochemical tests and molecular methods. For bacterial identification, standard biochemical tests such as catalase, coagulase, and oxidase tests were performed. Additionally, the API system (bioM\u0026eacute;rieux) was utilized for the identification of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e and non-fermenting Gram-negative bacteria. For fungal identification, particularly Candida species, germ tube tests and chromogenic agar were employed to differentiate between species. To confirm the identity of the isolated organisms and detect specific pathogens, polymerase chain reaction (PCR) assays were performed. Primers specific to \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eProteus mirabilis\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa, Staphylococcus aureus, Group B Streptococcus, Enterococcus faecalis\u003c/em\u003e and \u003cem\u003eCandida albicans\u003c/em\u003e organisms isolated were, were utilized. The PCR conditions were optimized for each target, including denaturation at 95\u0026deg;C for 30 seconds, annealing at 55\u0026ndash;60\u0026deg;C for 30 seconds, and extension at 72\u0026deg;C for 1 minute, followed by a final extension at 72\u0026deg;C for 5 minutes. The PCR products were analyzed using gel electrophoresis, and the presence of bands corresponding to the expected sizes confirmed the identity of the pathogens [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Antimicrobial Susceptibility Testing\u003c/h2\u003e \u003cp\u003e Antimicrobial susceptibility testing was conducted using the disk diffusion method according to Clinical and Laboratory Standards Institute (CLSI) guidelines. Isolates were tested against a panel of antibiotics relevant to UTI treatment, including chloramphenicol (CH), erythromycin (E1Y/E), gentamicin (CN), ampicillin (APX), amoxicillin (AMX), ceftriaxone (CT1), sulfamethoxazole (S), and cefalexin (C1X). The results were interpreted based on the zone of inhibition, and multidrug resistance (MDR) was defined as resistance to three or more antibiotic classes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were employed to summarize the demographic and clinical characteristics of the study population. Chi-square tests were used to assess the association between age categories, clinical details, and the multidrug resistance status of the isolated organisms. To address potential biases in the model, particularly those arising from confounding variables, multivariable logistic regression was employed. This method allowed for the adjustment of various covariates, thus controlling for their potential confounding effects on the outcome. Logistic regression analysis was performed to identify predictors of MDR among the isolated pathogens. The model included age, clinical symptoms, and specific organisms as independent variables, with MDR status as the dependent variable. The significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all statistical tests [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ethical Considerations\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with ethical guidelines, and approval was obtained from the Research and Ethics committee of Federal University Teaching Hospital, Owerri, Nigeria prior to the commencement of the study. Informed consent was obtained from all participants or their guardians in the case of minors. Confidentiality of patient information was maintained throughout the study [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Distribution and Analysis of HVS Specimens by Age, Clinical Presentation, Pathogen, and MDR Status\u003c/h2\u003e \u003cp\u003eA total of 824 HVS specimens from females presenting with symptoms suggestive of urinary tract infections (UTIs) were analyzed. The age distribution revealed that the majority of participants were young adults (20\u0026ndash;39 years), comprising 618 specimens (75.0%), followed by middle-aged adults (40\u0026ndash;59 years) with 145 specimens (17.6%). Other age categories, including children (0\u0026ndash;12 years) and teenagers (13\u0026ndash;19 years), accounted for only 34 specimens (4.1%) combined, while older adults (60\u0026ndash;79 years) and the elderly (80\u0026thinsp;+\u0026thinsp;years) contributed a marginal 27 specimens (3.3%).\u003c/p\u003e \u003cp\u003eThe most common clinical presentation was symptoms of inflammatory conditions or asymptomatic cases, reported in 423 specimens (51.3%). Other prominent presentations included vaginal discharge and related conditions (175 specimens; 21.2%) and obstetric or pregnancy-related issues (95 specimens; 11.5%). Less frequent presentations involved pain and discomfort (38 specimens; 4.6%), gynecological or reproductive health conditions (68 specimens; 8.3%), and cysts or tumors (7 specimens; 0.8%).\u003c/p\u003e \u003cp\u003eBacterial and fungal pathogens were isolated in 687 specimens (83.4%), while 137 specimens (16.6%) showed no microbial growth. The most frequently isolated pathogens were \u003cem\u003eCandida albicans\u003c/em\u003e (223 isolates; 27.1%) and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (220 isolates; 26.7%), accounting for over half of the positive cultures. Other significant isolates included \u003cem\u003eEscherichia coli\u003c/em\u003e (116 isolates; 14.1%) and Group B \u003cem\u003eStreptococcus\u003c/em\u003e (94 isolates; 11.4%). Less common pathogens included Enterococcus faecalis (15 isolates; 1.8%), \u003cem\u003eProteus mirabilis\u003c/em\u003e (5 isolates; 0.6%), and Pseudomonas aeruginosa (6 isolates; 0.7%). Co-infections of \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eCandida albicans\u003c/em\u003e were identified in 8 specimens (1.0%).\u003c/p\u003e \u003cp\u003eAmong the 824 specimens, 643 cases (79.2%) were classified as non-multidrug-resistant (NON-MDR), while 181 cases (21.8%) exhibited multidrug resistance (MDR). The analysis presents the distribution of UTI specimens from female patients across various categories, including age groups, clinical details, and organisms, stratified by multidrug resistance (MDR) status. A total of 824 female specimens were analyzed, with 643 (78.0%) classified as non-MDR and 181 (21.8%) as MDR. The majority of specimens were from young adults (20\u0026ndash;39 years), representing 75.0% of the sample. Of these, 21.8% were MDR. A smaller proportion of specimens came from older age groups, with 2.8% from older adults (60\u0026ndash;79 years) and 0.5% from elderly individuals (80\u0026thinsp;+\u0026thinsp;years). Chi-square analysis for the association between age category and MDR status showed no significant difference (χ2\u0026thinsp;=\u0026thinsp;2.825,p\u0026thinsp;=\u0026thinsp;0.985 \\chi^2\u0026thinsp;=\u0026thinsp;2.825, p\u0026thinsp;=\u0026thinsp;0.985χ2\u0026thinsp;=\u0026thinsp;2.825,p\u0026thinsp;=\u0026thinsp;0.985), suggesting that age did not significantly influence MDR rates in this female cohort (table 3.1)..\u003c/p\u003e \u003cp\u003eThe most frequent clinical details reported were symptoms of inflammatory conditions (51.3%), followed by vaginal discharge and related conditions (21.2%). Other conditions included gynecological health issues (8.3%), obstetric conditions (11.5%), and cysts and tumors (0.8%). The proportion of MDR cases within each clinical detail category was consistent, ranging from 15.4\u0026ndash;22.9%. Chi-square analysis revealed no significant association between clinical details and MDR status (χ2\u0026thinsp;=\u0026thinsp;1.964,p\u0026thinsp;=\u0026thinsp;0.962 \\chi^2\u0026thinsp;=\u0026thinsp;1.964, p\u0026thinsp;=\u0026thinsp;0.962χ2\u0026thinsp;=\u0026thinsp;1.964,p\u0026thinsp;=\u0026thinsp;0.962), indicating that clinical symptoms did not strongly correlate with multidrug resistance in this female patient population.\u003c/p\u003e \u003cp\u003eThe most commonly isolated organisms were \u003cem\u003eCandida albicans\u003c/em\u003e (27.1%) and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (26.7%). There were no MDR results for \u003cem\u003eCandida albican\u003c/em\u003e as it was not a bacterium. \u003cem\u003eEnterococcus faecalis\u003c/em\u003e exhibited the highest MDR rate (40.0%), while \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e had a lower MDR rate (16.7%). The Chi-square test for the association between organism type and MDR status showed a significant relationship (χ2\u0026thinsp;=\u0026thinsp;119.109, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;119.109, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001χ2\u0026thinsp;=\u0026thinsp;119.109,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the type of organism is significantly associated with the likelihood of being multidrug resistant (table 3.1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCombined Descriptive and Analytical Results For UTI Specimens\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMDR (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChi-Square (χ2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChildren: 0\u0026ndash;12 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeenagers: 13\u0026ndash;19 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYoung Adults: 20\u0026ndash;39 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e618 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle-aged Adults: 40\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlder Adults: 60\u0026ndash;79 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElderly: 80\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Details\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflammatory Conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e423 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVaginal Discharge and Related Conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGynecological and Reproductive Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObstetric Conditions and Pregnancy Issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCysts and Tumours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePain and Discomfort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther Clinical Conditions and Symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther Medical Conditions and Procedures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrganism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e119.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup B \u003cem\u003eStreptococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eC. albicans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e824\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e180 (21.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Logistic Regression Analysis of Multidrug Resistance Predictors\u003c/h2\u003e \u003cp\u003eThe logistic regression analysis (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicates that the age categories (Children: 0\u0026ndash;12 years, Teenagers: 13\u0026ndash;19, Young Adults: 20\u0026ndash;39, Middle-aged Adults: 40\u0026ndash;59, Older Adults: 60\u0026ndash;79, and Elderly: 80\u0026thinsp;+\u0026thinsp;years) do not show statistically significant contributions to the outcome variable, as evidenced by their high p-values (0.999) and large standard errors. This suggests that age has no meaningful association with the dependent variable in this dataset.\u003c/p\u003e \u003cp\u003eRegarding clinical symptoms, individuals with vaginal discharge and related conditions have an odds ratio (EXP(B)) of 1.071, indicating nearly the same likelihood of the outcome as those without this condition, but this finding is not statistically significant (p\u0026thinsp;=\u0026thinsp;0.955). For gynecological and reproductive health issues, the odds ratio is 1.118, but this is also not significant (p\u0026thinsp;=\u0026thinsp;0.927), suggesting no meaningful relationship with the outcome. Obstetric and pregnancy-related issues have an odds ratio of 0.958, with no significant impact (p\u0026thinsp;=\u0026thinsp;0.972). Cysts and tumors, however, show a statistically significant result with an odds ratio of 0.92 (p\u0026thinsp;=\u0026thinsp;0.046), indicating a slight reduction in the likelihood of the outcome. Pain and discomfort have an odds ratio of 1.075, but this is not significant (p\u0026thinsp;=\u0026thinsp;0.961), suggesting no substantial influence. Other clinical conditions show an odds ratio of 0.617, which is also not statistically significant (p\u0026thinsp;=\u0026thinsp;0.710), indicating no meaningful association. \u003cem\u003eCandida albican\u003c/em\u003e being associated with UTI was excluded from the regression model due to its classification as a fungus rather than a bacterium, which precludes a direct association with MDR in the context of this analysis.\u003c/p\u003e \u003cp\u003eAmong the organisms, \u003cem\u003eEscherichia coli\u003c/em\u003e (E. coli) is the only significant predictor, with an odds ratio of 1.38 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a strong positive association with the likelihood of the outcome. This suggests that the presence of E. coli increases the likelihood of MDR occurring. In contrast, other organisms such as \u003cem\u003eProteus mirabilis\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, and \u003cem\u003eGroup B Streptococcus\u003c/em\u003e did not show statistically significant associations, with p-values greater than 0.05. Similarly, \u003cem\u003eEnterococcus faecalis\u003c/em\u003e did not exhibit a significant effect, with an odds ratio of 0.595 (p\u0026thinsp;=\u0026thinsp;0.493), indicating no meaningful association with the outcome.\u003c/p\u003e \u003cp\u003eThe overall model suggests that, apart from \u003cem\u003eE. coli\u003c/em\u003e and cysts or tumors, most variables do not significantly affect the outcome. Additionally, the constant term (intercept) indicates negligible odds of the outcome in the absence of predictors, with an EXP(B) value approaching zero. Some variables, particularly age categories, have large standard errors, indicating possible data quality issues or multicollinearity within the model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression of Age, Symptoms, and Organisms Associated with MDR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eVariables in the Equation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEXP (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e95% C.I.for EXP(B)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"21\" rowspan=\"22\"\u003e \u003cp\u003eStep 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChildren: 0\u0026ndash;12 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeenagers (13\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19003.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e484485328.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYoung Adults: (20\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19003.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e626023752.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle-aged Adults: 40\u0026ndash;59 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19003.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e370501381.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlder Adults: 60\u0026ndash;79 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19003.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e268501617.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElderly: 80\u0026thinsp;+\u0026thinsp;years and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19003.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e273597294.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymptoms of Inflammatory Conditions or no symptoms at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVaginal Discharge and Related Conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGynecological and Reproductive Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObstetric Conditions and Pregnancy-Related Issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCysts and Tumours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePain and Discomfort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther Clinical Conditions and Symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther Medical Conditions and Procedures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO GROWTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGroup B Streptococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-19.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19003.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003ea. Variable(s) entered on step 1: AgeCat, CLINICALDETAILS, ORGANISM.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Model Performance and Fit Analysis\u003c/h2\u003e \u003cp\u003eThe Omnibus Tests of Model Coefficients (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) show that the model is statistically significant, with a Chi-square value of 135.355 (df\u0026thinsp;=\u0026thinsp;20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that the predictors included in the model significantly improve the prediction of the outcome compared to a null model with no predictors. The Model Summary (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) reports a -2 Log Likelihood value of 732.280, which is a measure of model fit. The Cox \u0026amp; Snell R Square value is 0.151, and the Nagelkerke R Square value is 0.233, suggesting that the predictors explain 15.1\u0026ndash;23.3% of the variance in the outcome. However, the estimation terminated at the maximum number of iterations, suggesting potential convergence issues that may affect the reliability of the results.\u003c/p\u003e \u003cp\u003eThe Classification (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) indicates the performance of the model in predicting the outcome categories (NON-MDR vs. MDR). The model correctly classifies 99.2% of NON-MDR cases and 2.8% of MDR cases, with an overall classification accuracy of 78.0%. However, the low sensitivity (ability to correctly identify MDR cases) indicates that the model performs poorly in predicting this category, likely due to imbalanced data or insufficient predictors. The Area Under the Curve (AUC) (Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) is 0.753 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with a 95% confidence interval ranging from 0.716 to 0.789. This indicates acceptable discriminatory ability of the model, meaning it is reasonably good at distinguishing between MDR and NON-MDR outcomes. However, the presence of ties between positive and negative groups suggests that the model's discrimination may not be optimal.\u003c/p\u003e \u003cp\u003eThe Hosmer and Lemeshow Test (Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) reports a Chi-square value of 2.506 (df\u0026thinsp;=\u0026thinsp;8, p\u0026thinsp;=\u0026thinsp;0.961), indicating that the model fits the data well. A non-significant p-value suggests that there is no significant difference between observed and predicted outcomes, supporting the adequacy of the model. Overall, while the model is statistically significant and shows reasonable discrimination (AUC), its predictive power is moderate (low R-squared values), and it struggles with correctly classifying MDR cases. The termination at maximum iterations suggests a need for further investigation into convergence issues, possibly by refining the model or addressing data limitations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOmnibus Tests of Model Coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStep 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2 Log likelihood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCox \u0026amp; Snell R Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNagelkerke R Square\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e732.280\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ea. Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification Table\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c3\" namest=\"c2\" rowspan=\"3\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePercentage Correct\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON-MDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStep 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMDR_\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNON-MDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e638\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOverall Percentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ea. The cut value is .500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTest Result Variable(s): Predicted probability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Error\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAsymptotic Sig.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAsymptotic 95% Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eThe test result variable(s): Predicted probability has at least one tie between the positive actual state group and the negative actual state group. Statistics may be biased.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ea. Under the nonparametric assumption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eb. Null hypothesis: true area\u0026thinsp;=\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHosmer and Lemeshow Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe study of urinary tract infections (UTIs) among female patients presents a critical examination of the prevalence, characteristics, and antimicrobial resistance patterns of uropathogens. The cross-sectional design utilized in this investigation is particularly effective for capturing a snapshot of the current state of UTIs, allowing for a comprehensive understanding of the demographic and clinical factors associated with these infections. The findings indicate a significant prevalence of multidrug-resistant (MDR) organisms, particularly \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, which poses a considerable challenge to treatment and underscores the need for ongoing surveillance and updated clinical guidelines [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe demographic analysis revealed that the majority of specimens were collected from young adults aged 20–39 years, a finding that aligns with previous studies indicating that this demographic is particularly susceptible to UTIs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The low representation of older adults and the elderly may reflect healthcare-seeking behaviors or the clinical presentation of UTIs in these populations. For instance, research has shown that older adults often present with atypical symptoms, which may lead to underdiagnosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This highlights the importance of tailored screening and diagnostic approaches for different age groups to ensure timely and appropriate treatment.\u003c/p\u003e \u003cp\u003eThe methodology employed for sample collection and processing was rigorous, utilizing sterile containers and prompt processing to minimize contamination. The use of multiple culture media for pathogen isolation is a well-established practice that enhances the detection of a broad spectrum of uropathogens, thereby increasing the sensitivity of the diagnostic process [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The isolation of both bacterial and fungal pathogens underscores the polymicrobial nature of UTIs, with \u003cem\u003eCandida albicans\u003c/em\u003e and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e being the most frequently identified organisms. This finding is consistent with previous studies that have reported similar trends in pathogen distribution [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study's findings regarding antimicrobial susceptibility testing are particularly concerning. The high prevalence of MDR organisms highlights the urgent need for effective antimicrobial stewardship and the development of new treatment strategies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The disk diffusion method employed for susceptibility testing is a standard practice that provides valuable insights into the resistance patterns of uropathogens. The identification of MDR strains necessitates a reevaluation of empirical treatment guidelines, especially in light of the increasing resistance to commonly used antibiotics such as amoxicillin and ciprofloxacin [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStatistical analysis revealed no significant association between age categories, clinical details, and MDR status, suggesting that factors other than age or specific clinical presentations may play a more critical role in the development of multidrug resistance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This finding is particularly relevant in the context of the increasing complexity of UTIs and the need for personalized treatment approaches that consider individual patient factors, including previous antibiotic exposure and underlying health conditions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The lack of significant predictors among other organisms suggests that the dynamics of resistance may be more complex than previously understood, warranting further investigation into the genetic and environmental factors contributing to resistance [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of the logistic regression analysis indicate that age categories do not significantly contribute to the likelihood of multidrug resistance (MDR) in urinary tract infections (UTIs). The high p-values (0.999) and large standard errors associated with age suggest that age is not a meaningful predictor in this dataset. This finding aligns with previous research that has reported inconsistent associations between age and UTI outcomes, particularly in older populations where atypical presentations may complicate diagnosis and treatment [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The lack of significant findings across various age groups may reflect the complex interplay of factors influencing UTI susceptibility and resistance patterns, which are not solely dependent on age but may also involve behavioral, environmental, and genetic factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In terms of clinical symptoms, the analysis reveals that common presentations such as vaginal discharge and gynecological issues do not significantly correlate with MDR status, as indicated by their odds ratios (1.071 and 1.118, respectively) and high p-values (0.955 and 0.927). This suggests that these symptoms may not be reliable indicators of MDR in the context of UTIs. Interestingly, cysts and tumors showed a statistically significant association with a slight reduction in the likelihood of MDR (odds ratio of 0.92, p = 0.046), indicating that patients with these conditions may have a different risk profile for developing MDR organisms [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This finding warrants further investigation into the underlying mechanisms that may contribute to this association. Among the organisms analyzed, Escherichia coli emerged as a significant predictor of MDR status, with an odds ratio of 1.38 (p \u0026lt; 0.001), indicating a association with the likelihood of being multidrug resistant. This finding is particularly noteworthy given that E. coli is the most common uropathogen in UTIs. The results suggest that the presence of E. coli is associated with a lower likelihood of encountering MDR strains, which may reflect its role as a primary pathogen in uncomplicated UTIs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Conversely, other organisms such as Proteus mirabilis, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, and Group B \u003cem\u003eStreptococcus\u003c/em\u003e did not show significant associations with MDR status, indicating that the dynamics of resistance may vary significantly among different uropathogens [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Overall, the logistic regression model indicates that, apart from \u003cem\u003eE. coli\u003c/em\u003e and cysts or tumors, most variables do not significantly influence the outcome of MDR in UTIs. The presence of large standard errors for some predictors, particularly age categories, suggests potential data quality issues or multicollinearity within the model, which may affect the reliability of the results [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This highlights the need for further research to explore the multifactorial nature of UTIs and the development of resistance, as well as the importance of refining clinical guidelines to better address the complexities of UTI management in diverse patient populations.).\u003c/p\u003e \u003cp\u003eThe implications of this study extend beyond the immediate findings, as they contribute to a broader understanding of the challenges posed by UTIs in the context of rising antimicrobial resistance. The need for innovative treatment strategies, including the exploration of non-antibiotic approaches for disease prevention and control, is increasingly recognized [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, the integration of patient-centered care models that consider individual preferences and experiences may enhance treatment adherence and outcomes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, the findings of this study resonate with global trends in antimicrobial resistance, as highlighted in various international studies. For instance, research conducted in different regions has demonstrated similar patterns of resistance among uropathogens, emphasizing the need for a coordinated global response to combat this public health threat [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The establishment of robust surveillance systems and the promotion of responsible antibiotic use are critical components of this response [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThis study sheds light on the prevalence of multidrug-resistant (MDR) organisms in urinary tract infections (UTIs) among female patients, revealing significant insights into the clinical predictors associated with these infections. The findings indicate that a substantial proportion of the isolated pathogens were MDR, particularly Enterococcus faecalis, which poses a considerable challenge for effective treatment. The demographic analysis highlighted that young adults (ages 20–39) were predominantly affected, aligning with existing literature that underscores this group's vulnerability to UTIs. The study's methodology, which included rigorous sample collection and processing, allowed for a comprehensive assessment of the microbial landscape associated with UTIs. The identification of both bacterial and fungal pathogens emphasizes the polymicrobial nature of these infections, necessitating a multifaceted approach to diagnosis and treatment. Notably, the logistic regression analysis identified Escherichia coli as a significant predictor of MDR status, reinforcing its role as a primary uropathogen in UTIs. Despite the moderate explanatory power of the predictive model, the low sensitivity for classifying MDR cases indicates a need for further refinement of predictive models to enhance their clinical applicability. The lack of significant associations between age, clinical details, and MDR status suggests that other factors, such as previous antibiotic exposure and underlying health conditions, may play a more critical role in the development of resistance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultidrug-Resistant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrinary Tract Infection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHVS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Vaginal Swab\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical and Laboratory Standards Institute\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntimicrobial Resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCystine-Lactose-Electrolyte-Deficient Agar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalytical Profile Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e Ethics approval and consent to participate: Ethical clearance for this study was obtained from the Federal University Teaching Hospital, Owerri, Imo State (FMC/OW/HREC/VOL.1/34-01661).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eCompeting interests\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo specific funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eF.C. I. performed the statistical analysis, wrote, corrected and reviewed the paper, C.I.O. and E.O. conducted the laboratory investigation, M.M. Ozoude and Marwizi, F.M. contributed to the writing and review of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge the support of the Federal University Teaching Hospital, Owerri, Imo State.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data utilized in this study are confidential in accordance with the ethical approval policy but can be made available by the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFlores-Mireles AL, Walker JN, Caparon MG, Hultgren SJ. Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol. 2015;13(5):269\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrmicro3432\u003c/span\u003e\u003cspan address=\"10.1038/nrmicro3432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeidar NA, Degheili JA, Yacoubian A, Khauli RB. Management of urinary tract infection in women: a practical approach for everyday practice. Urol Annals. 2019;11(4):339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/ua.ua_104_19\u003c/span\u003e\u003cspan address=\"10.4103/ua.ua_104_19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTezcan Ş, Uslu N, Soy EHA, Haberal M. Untitled Experimental Clin Transplantation. 2017;15(Suppl 1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6002/ect.mesot2016.p118\u003c/span\u003e\u003cspan address=\"10.6002/ect.mesot2016.p118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassan MM, Malik M, Saleem R, Saleem A, Zohaib K, Malik AY, Javaid M. (2022). Efficacy of single dose of fosfomycin versus a five-day course of ciprofloxacin in patients with uncomplicated urinary tract infection. Cureus. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.24843\u003c/span\u003e\u003cspan address=\"10.7759/cureus.24843\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGondos AS, Al-Moyed KA, Al-Robasi ABA, Al-Shamahy HA, Alyousefi NA. Urinary tract infection among renal transplant recipients in yemen. PLoS ONE. 2015;10(12):e0144266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0144266\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0144266\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzm\u0026aacute;n M, Salazar E, Cordero V, Castro A, Villanueva A, Rodulfo H, Donato MD. Multidrug resistance and risk factors associated with community-acquired urinary tract infections caused by escherichia coli in venezuela. Biom\u0026eacute;dica. 2019;39:96\u0026ndash;107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7705/biomedica.v39i2.4030\u003c/span\u003e\u003cspan address=\"10.7705/biomedica.v39i2.4030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao D, Shen Y, Huang Y, Chen B, Chen Z, Ai J, Wei Q. Levofloxacin versus ciprofloxacin in the treatment of urinary tract infections: evidence-based analysis. Front Pharmacol. 2021;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphar.2021.658095\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2021.658095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnesi JA, Lautenbach E, Nachamkin I, Garrigan C, Bilker WB, Wheeler MK, Han JH. Clinical and molecular characterization of community-onset urinary tract infections due to extended-spectrum cephalosporin-resistant enterobacteriaceae. Infect Control \u0026amp;Amp Hosp Epidemiol. 2016;37(12):1433\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/ice.2016.225\u003c/span\u003e\u003cspan address=\"10.1017/ice.2016.225\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBitew A, Molalign T, Chanie M. Species distribution and antibiotic susceptibility profile of bacterial uropathogens among patients complaining urinary tract infections. BMC Infect Dis. 2017;17(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12879-017-2743-8\u003c/span\u003e\u003cspan address=\"10.1186/s12879-017-2743-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaider I, Ullah S, Bibi K, Naeem HS, Khan HA, Khan WM. Multi-drug resistant escherichia coli and their sensitivity to oral fosfomycin in urinary tract infections: a single-center experience. J Med Sci. 2023;31(02):97\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.52764/jms.23.31.2.1\u003c/span\u003e\u003cspan address=\"10.52764/jms.23.31.2.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaga MK, Mawari G, Wasi S, Kumar N, Sharma U, Hussain MA. (2021). Current pattern and clinico-bacteriological profile of healthcare associated infections (hai) in an icu setting: an observational study. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-874099/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-874099/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTyne DV, Gilmore MS. Friend turned foe: evolution of enterococcal virulence and antibiotic resistance. Annu Rev Microbiol. 2014;68(1):337\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-micro-091213-113003\u003c/span\u003e\u003cspan address=\"10.1146/annurev-micro-091213-113003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIbrahim Z, Behiry A, Attia O, El-sayed H. Evaluation of in vitro effect of fosfomycin on resistant gram-negative pathogens in urinary tract infection. Microbes Infect Dis. 2022;0(0):0\u0026ndash;0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21608/mid.2022.127574.1259\u003c/span\u003e\u003cspan address=\"10.21608/mid.2022.127574.1259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasin F, Assad S, Talpur AS, Zahid M, Malik SA. (2017). Combination therapy for multidrug-resistant klebsiella pneumoniae urinary tract infection. Cureus. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.1503\u003c/span\u003e\u003cspan address=\"10.7759/cureus.1503\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGopichand, P., Agarwal, G., Natarajan, M., Mandal, J., Deepanjali, S., Parameswaran,S., \u0026hellip; Dorairajan, L. N. (2019). in vitro effect of fosfomycin on multi-drug resistant gram-negative bacteria causing urinary tract infections. Infection and Drug Resistance, Volume 12, 2005\u0026ndash;2013. https://doi.org/10.2147/idr.s207569.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen L, H, W., Jiang Y. A case report: intermittent catheterization combined with rehabilitation in the treatment of carbapenem-resistant klebsiella pneumoniae catheter-associated urinary tract infection. Front Cell Infect Microbiol. 2022;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcimb.2022.1027576\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2022.1027576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlkhouri JS, Santiago F, Guzman-Cole C, Garsevanyan S, Sindi S, Barlow M. Molecular surveillance and assessment of ceftolozane/tazobactam resistance with common β-lactam antibiotics and β-lactamase genes. J Clin \u0026amp;Amp Biomedical Res. 2021;1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47363/jcbr/2021(3)136\u003c/span\u003e\u003cspan address=\"10.47363/jcbr/2021(3)136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasew D, Desalegn B, Aynalem M, Tila S, Diriba D, Afework B, Baynes HW. Antimicrobial resistance trend of bacterial uropathogens at the university of gondar comprehensive specialized hospital, northwest ethiopia: a 10 years retrospective study. PLoS ONE. 2022;17(4):e0266878. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0266878\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0266878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhadgi S, Timilsina U, Shrestha B. Plasmid profiling of multidrug resistant escherichia coli strains isolated from urinary tract infection patients. Int J Appl Sci Biotechnol. 2013;1(1):1\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3126/ijasbt.v1i1.7918\u003c/span\u003e\u003cspan address=\"10.3126/ijasbt.v1i1.7918\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBach F. (2010). Self-concordant analysis for logistic regression. Electronic Journal of Statistics, 4(none). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1214/09-ejs521\u003c/span\u003e\u003cspan address=\"10.1214/09-ejs521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRani D, Krishan K, Kanchan T. A methodological comparison of discriminant function analysis and binary logistic regression for estimating sex in forensic research and case-work. Med Sci Law. 2022;63(3):227\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/00258024221136687\u003c/span\u003e\u003cspan address=\"10.1177/00258024221136687\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTenga A, Ronglan L, Bahr R. Measuring the effectiveness of offensive match-play in professional soccer. Eur J Sport Sci. 2010;10(4):269\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17461390903515170\u003c/span\u003e\u003cspan address=\"10.1080/17461390903515170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTwisk J, Vente W, Apeldoorn A, Boer M. Should we use logistic mixed model analysis for the effect estimation in a longitudinal rct with a dichotomous outcome variable? Epidemiol Biostatistics Public Health. 2022;14(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2427/12613\u003c/span\u003e\u003cspan address=\"10.2427/12613\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenœux T. (2018). Logistic regression revisited: belief function analysis., 57\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-99383-6_8\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-99383-6_8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaterino JM, Ting SA, Sisbarro SG, Espinola JA, Camargo CA. Age, nursing home residence, and presentation of urinary tract infection in u.s. emergency departments, 2001\u0026ndash;2008. Acad Emerg Med. 2012;19(10):1173\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1553-2712.2012.01452.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1553-2712.2012.01452.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRose A, Thimme A, Halfar C, Nehen HG, R\u0026uuml;bben H. Severity of urinary incontinence of nursing home residents correlates with malnutrition, dementia and loss of mobility. Urol Int. 2013;91(2):165\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000348344\u003c/span\u003e\u003cspan address=\"10.1159/000348344\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewby B, Ramesh KK. Urinary tract infection in a preterm neonate caused by lactococcus lactis. Can J Hosp Pharm. 2014;67(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4212/cjhp.v67i6.1409\u003c/span\u003e\u003cspan address=\"10.4212/cjhp.v67i6.1409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai S, He H, Han C, Yang M, Bi X, Fan W. What makes a theme park experience less enjoyable? evidence from online customer reviews of disneyland china. Front Psychol. 2023;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2023.1120483\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2023.1120483\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNingsih A. Exploring the challenges of digital textbooks in reading comprehension. Klausa (Kajian Linguistik Pembelajaran Bahasa Dan Sastra). 2023;7(2):29\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.33479/klausa.v7i2.844\u003c/span\u003e\u003cspan address=\"10.33479/klausa.v7i2.844\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGasparini L, Tsuji S, Bergmann C. Ten easy steps to conducting transparent, reproducible meta-analyses for infant researchers. Infancy. 2022;27(4):736\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/infa.12470\u003c/span\u003e\u003cspan address=\"10.1111/infa.12470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFogel J, Ustoyev S. Social media advertisements with deposit contracts and fitness club/gym membership: are consumers persuaded? J Consumer Mark. 2020;38(1):27\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/jcm-02-2020-3621\u003c/span\u003e\u003cspan address=\"10.1108/jcm-02-2020-3621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTong Q, Hammer K, Johnson E, Zegarra M, Goto M, Lo T. (2018). A systematic review and meta-analysis on the use of prophylactic topical antibiotics for the prevention of uncomplicated wound infections. Infection and Drug Resistance, 11, 417\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/idr.s151293\u003c/span\u003e\u003cspan address=\"10.2147/idr.s151293\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill M, Laughter M, Harmange C, Dellavalle R, Rundle C, Dunnick C. The contact dermatitis quality of life index (cdql): survey development and content validity assessment. Jmir Dermatology. 2021;4(2):e30620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/30620\u003c/span\u003e\u003cspan address=\"10.2196/30620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReinhold M, B\u0026uuml;rkner P, Holling H. Effects of expressive writing on depressive symptoms-a meta-analysis. Clin Psychol Sci Pract. 2018;25(1):e12224. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/cpsp.12224\u003c/span\u003e\u003cspan address=\"10.1111/cpsp.12224\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVandenbroucke J, Pearce N. From ideas to studies: how to get ideas and sharpen them into research questions. Clin Epidemiol. 2018;10:253\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/clep.s142940\u003c/span\u003e\u003cspan address=\"10.2147/clep.s142940\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MDR, UTI, Clinical predictors, Escherichia coli, Antimicrobial resistance","lastPublishedDoi":"10.21203/rs.3.rs-5619455/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5619455/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMultidrug-resistant (MDR) organisms pose a significant challenge in the effective treatment of urinary tract infections (UTIs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e 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 \u003cem\u003eCandida albicans\u003c/em\u003e (27.1%) and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (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). \u003cem\u003eEnterococcus faecalis\u003c/em\u003e exhibited the highest MDR rate (40%), and \u003cem\u003eEscherichia coli\u003c/em\u003e was significantly associated with MDR status (B = 3.220, p \u0026lt; 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 \u0026amp; Snell R² = 0.151, Nagelkerke R² = 0.233), acceptable discriminatory ability (AUC = 0.753, p \u0026lt; 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e 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.\u003c/p\u003e","manuscriptTitle":"Predictive Modeling of Multidrug Resistance in Female Urinary Tract Infections: Implications for Clinical Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 17:06:20","doi":"10.21203/rs.3.rs-5619455/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-03T03:13:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-07T08:56:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-03T03:46:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-29T12:49:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257259995606604231605941676515411066553","date":"2024-12-29T12:07:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283942609143547280248887499862266201929","date":"2024-12-28T14:47:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45834318991715600563929683956870903051","date":"2024-12-27T14:19:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-26T06:20:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-20T07:44:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-17T11:33:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2024-12-10T20:55:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14f82657-95aa-408a-acc4-99b589688c6e","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T15:58:23+00:00","versionOfRecord":{"articleIdentity":"rs-5619455","link":"https://doi.org/10.1186/s12982-025-00762-9","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2025-06-25 15:56:57","publishedOnDateReadable":"June 25th, 2025"},"versionCreatedAt":"2024-12-24 17:06:20","video":"","vorDoi":"10.1186/s12982-025-00762-9","vorDoiUrl":"https://doi.org/10.1186/s12982-025-00762-9","workflowStages":[]},"version":"v1","identity":"rs-5619455","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5619455","identity":"rs-5619455","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}