Potentially preventable urinary tract infection in patients with type 2 diabetes - A hospital-based study

Characterization of the Phenotypic and Genotypic Antibiotic Resistance Markers in Escherichia coli (E. coli) Associated With Diabetes and Nondiabetic Patients

Introduction: Individuals with diabetes are more susceptible to urinary tract infections (UTIs) than those without the disease. This study aimed to determine the phenotypic and genotypic antibiotic resistance profiles of Escherichia coli in diabetic and nondiabetic patients. Methodology: A total of 374 clean-catch midstream urine specimens were screened for uropathogens, and antibiogram analysis was done on E. coli isolates by the Kirby-Bauer disc diffusion method, followed by phenotypic confirmation of extended spectrum beta-lactamase (ESBL) production. In addition, polymerase chain reaction (PCR) assays were carried out to determine ESBL genotypes. Result: Overall, we observed UTIs prevalence of 19.8% and 10.7% in diabetic and nondiabetic patients. Females exhibited higher UTI prevalence than males in both groups ([71.8% and 28.2%] vs. [85% and 15%]) (p < 0.0001). Among women with and without diabetes, the age groups of 55-64 and 25-34 years showed the highest prevalence of UTIs (25.6% vs. 40%). The most prevalent uropathogen was E. coli (62.2% vs. 75%); multidrug-resistant (MDR) E. coli was (61% vs. 33.3%) and ESBL-E. coli was (34.8% and 20%) in diabetic and nondiabetic patients, respectively. The most common ESBL-mediated gene was blaCTX-M (64.3%) with multiple ESBL genes in some E. coli isolates. High-level resistance was observed for ampicillin (91.2%), cefuroxime (96.7%), ciprofloxacin (44.9%), and trimethoprim (59.4%), and low-level resistance was observed for gentamicin (18.7%), ceftriaxone (20.9%), and nitrofurantoin (19.8%). There was no significant difference between antibiotic resistance in diabetic and nondiabetic patients (p > 0.05). Conclusion: We observed blaCTX-M as the most common ESBL genotype, in combination with other ESBL genes present in some E. coli isolates. Nitrofurantoin and ceftriaxone antibiotics were efficacious. Appropriate prescription of antibiotic therapy, and the prevention of transmission of resistant genes in the context of public health can be facilitated by routine monitoring of the resistance profiles and ESBL markers in patients with and without diabetes.

Enhancing predictive accuracy for urinary tract infections post-pediatric pyeloplasty with explainable AI: an ensemble TabNet approach

Ureteropelvic junction obstruction (UPJO) is a common pediatric condition often treated with pyeloplasty. Despite the surgical intervention, postoperative urinary tract infections (UTIs) occur in over 30% of cases within six months, adversely affecting recovery and increasing both clinical and economic burdens. Current prediction methods for postoperative UTIs rely on empirical judgment and limited clinical parameters, underscoring the need for a robust, multifactorial predictive model. We retrospectively analyzed data from 764 pediatric patients who underwent unilateral pyeloplasty at the Children's Hospital affiliated with the Capital Institute of Pediatrics between January 2012 and January 2023. A total of 25 clinical features were extracted, including patient demographics, medical history, surgical details, and various postoperative indicators. Feature engineering was initially performed, followed by a comparative analysis of five machine learning algorithms (Logistic Regression, SVM, Random Forest, XGBoost, and LightGBM) and the deep learning TabNet model. This comparison highlighted the respective strengths and limitations of traditional machine learning versus deep learning approaches. Building on these findings, we developed an ensemble learning model, meta-learner, that effectively integrates both methodologies, and utilized SHAP(Shapley Additive Explanation, SHAP) to complete the visualization of the integrated black-box model. Among the 764 pediatric pyeloplasty cases analyzed, 265 (34.7%) developed postoperative UTIs, predominantly within the first three months. Early UTIs significantly increased the likelihood of re-obstruction (P < 0.01), underscoring the critical impact of infection on surgical outcomes. In evaluating the performance of six algorithms, TabNet outperformed traditional models, with the order from lowest to highest as follows: Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, and TabNet. Feature engineering markedly improved the predictive accuracy of traditional models, as evidenced by the enhanced performance of LightGBM (Accuracy: 0.71, AUC: 0.78 post-engineering). The proposed ensemble approach, combining LightGBM and TabNet with a Logistic Regression meta-learner, achieved superior predictive accuracy (Accuracy: 0.80, AUC: 0.80) while reducing dependence on feature engineering. SHAP analysis further revealed eGFR and ALB as significant predictors of UTIs post-pyeloplasty, providing new clinical insights into risk factors. In summary, we have introduced the first ensemble prediction model, incorporating both machine learning and deep learning (meta-learner), to predict urinary tract infections following pediatric pyeloplasty. This ensemble approach mitigates the dependency of machine learning models on feature engineering while addressing the issue of overfitting in deep learning-based models like TabNet, particularly in the context of small medical datasets. By improving prediction accuracy, this model supports proactive interventions, reduces postoperative infections and re-obstruction rates, enhances pyeloplasty outcomes, and alleviates health and economic burdens.Level of evidence IV Case series with no comparison group.

Predictive Factors for Urinary Tract Infections in Patients with Type 2 Diabetes

Background/Objectives: Patients with diabetes (DM) are at an increased risk of infection, with urinary tract infections (UTIs) being common among individuals with type 2 DM (T2D). The aim of this study was to determine the prevalence and risk factors for UTIs among hospitalized T2D patients from Timișoara, Romania. Methods: The hospital records of 1139 T2D adult inpatients who were ordered to provide urine cultures during hospitalization were reviewed. Results: The prevalence of UTIs among T2D patients was 19.7%, and was higher in women than in men (27.5% vs. 9.8%, p < 0.0001). Patients with UTIs presented a significantly older age, a longer duration of DM, a higher BMI, higher levels of HbA1c, higher renal function parameters, and more frequent DM-related complications and comorbidities than patients without UTIs. The following predictors were associated with increased UTI risk: age (OR = 1.05, p < 0.0001); duration of DM (OR = 1.04, p < 0.0001); BMI (OR = 1.05, p < 0.0002); HbA1c levels (OR = 1.58, p < 0.0001); female gender (OR = 3.47, p < 0.0001); and the presence of retinopathy (OR = 1.47, p = 0.0118), chronic kidney disease (OR = 3.98, p < 0.0001), distal symmetric polyneuropathy (OR = 7.65, p < 0.0001), and cerebrovascular disease (OR = 4.88, p < 0.0001). The use of sodium-glucose co-transporter 2 (SGLT2) inhibitors did not influence the risk of developing UTIs. Conclusions: T2D patients with prolonged disease duration, poor glycemic control, and DM-related complications are at an increased risk of developing UTIs. Therefore, a targeted therapeutic strategy addressing these risk factors is essential.

Characteristics of Urinary Tract Infections in Patients with Diabetes from Timișoara, Romania: Prevalence, Etiology, and Antimicrobial Resistance of Uropathogens

Background and Objectives: Diabetic patients are more likely to develop infections compared to the general population, especially urinary tract infections (UTIs). The aim of this study was to assess the prevalence of UTIs in a population of patients with diabetes (DM) from Romania, to identify the most common uropathogens and their antimicrobial resistance (AMR) patterns, as well as to determine the correlations between resistance behavior and particularities of patients with UTIs according to DM type. Materials and Methods: The hospital records of 1282 type 1 (T1D) and type 2 DM (T2D) adult inpatients who were ordered urine cultures during hospitalization were reviewed, and all 241 patients who presented a positive urine culture were included in the present study analysis. Results: The prevalence of UTIs in diabetic patients was 18.8% and higher in patients with T2D vs. T1D. Patients with UTIs and T2D had a significantly older age, longer duration of DM, higher waist circumference and body mass index, lower levels of estimated glomerular filtration rate, and more frequent chronic complications of DM than patients with T1D. E. coli was the most frequently isolated uropathogen (56.4%), with a significantly higher incidence for T2D, followed by K. pneumoniae (12.9%) and Enterococcus spp. (9.5%). Although the acquired resistance phenotypes were more frequently isolated in T2D patients (over 90% of the multidrug-resistant and extended-spectrum beta-lactamase-producing isolates, respectively, and 75% of the total carbapenem-resistant organisms), no statistically significant correlation was found regarding the distribution of AMR patterns in the two types of DM. Conclusions: The present study brings new data regarding the prevalence of UTIs in diabetic patients from Western Romania. By identifying the spectrum of uropathogens and their AMR pattern, this paper may contribute to improving UTI management in diabetic patients, thus reducing antibiotic overuse and preventing recurrent UTIs.

Risk factors for urinary tract infection in elderly patients with type 2 diabetes: A protocol for systematic review and meta-analysis

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a frequent chronic condition among the elderly, which increasing their susceptibility to infection. Urinary tract infection (UTI) is one of the most prevalent infections among older people with T2DM. However, the association between geriatric T2DM and the risk of UTI has not been thoroughly researched and is still contentious. Consequently, this protocol describes a systematic review to pinpoint the primary risk factors for UTI among elderly T2DM. Our goal is to improve recommendations for the creation of targeted treatment interventions by examining risk factors for UTI in elderly individuals with T2DM.

METHODS AND ANALYSIS

We will search 4 English literature databases (PubMed, Embase, Web of Science, and Cochrane Library) and 3 major Chinese databases (CNKI, WanFang, and VIP) from the establishment of the database to June 20, 2024. Systematic evaluation and meta-analysis will be conducted on cohort and case-control studies exploring the occurrence and risk determinants of UTI in individuals diagnosed with T2DM. The main focus will be on identifying the risk factors for UTI in elderly diabetic patients. Two researchers will independently review articles, collect data, and evaluate the quality and potential bias of study inclusion. We will use RevMan V.5.4 software to analyze the data. The quality of the included studies will be assessed using the Newcastle-Ottawa scale. In addition, the GRADE (Grade of Recommendations, Assessment, Development, Evaluation) method will be used to examine the quality of evidence for each exposure and outcome of interest.

DISCUSSION

This study aims to illuminate the various risk factors associated with UTI in older patients diagnosed with T2DM. By this thorough investigation, we hope to provide a more comprehensive reference for medical professionals and researchers, thereby supporting the implementation of effective preventive strategies against UTI and improving overall nursing outcomes for this specific patient population.

TRAIL REGISTRATION

PROSPERO (CRD42024559129).

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital for analysis. The data set was split into a training set and a test set in an 8:2 ratio. To handle the data and establish risk warning models, four imputation methods, four balancing methods, three feature screening methods, and eighteen machine learning algorithms were employed. A 10-fold cross-validation technique was applied to internally validate the training set, while the bootstrap method was used for external validation in the test set. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the models. The contributions of features were interpreted using the SHapley Additive ExPlanation (SHAP) approach. And a web-based prediction platform for UTIs in T2DM was constructed by Flask framework. Finally, 106 variables were identified for analysis from a total of 119 literature sources, and 1340 patients were included in the study. After comprehensive data preprocessing, a total of 48 datasets were generated, and 864 risk warning models were constructed based on various balancing methods, feature selection techniques, and a range of machine learning algorithms. The receiver operating characteristic (ROC) curves were used to assess the performances of these models, and the best model achieved an impressive AUC of 0.9789 upon external validation. Notably, the most critical factors contributing to UTIs in T2DM patients were found to be UTIs-related inflammatory markers, medication use, mainly SGLT2 inhibitors, severity of comorbidities, blood routine indicators, as well as other factors such as length of hospital stay and estimated glomerular filtration rate (eGFR). Furthermore, the SHAP method was utilized to interpret the contribution of each feature to the model. And based on the optimal predictive model a user-friendly prediction platform for UTIs in T2DM was built to assist clinicians in making clinical decisions. The machine learning model-based prediction system developed in this study exhibited favorable predictive ability and promising clinical utility. The web-based prediction platform, combined with the professional judgment of clinicians, can assist to make better clinical decisions.

Prevalence of urinary tract infections and risk factors among diabetic patients in Ethiopia, a systematic review and meta-analysis

INTRODUCTION

Urinary tract infection (UTI) is a common clinical problem that comprises 1-6% of medical referrals and includes urinary tract, bladder, and kidney infections. UTI is the most commonly occurring infectious disease in diabetic patients. Therefore, this systematic review and meta-analysis aimed to estimate the prevalence of urinary tract infection and its associated factors in Ethiopia.

METHODS

The online libraries of PubMed, Google Scholar, Scopus, and Science Direct, were searched. Data were extracted using Microsoft Excel and analyzed using STATA statistical software (v. 16). Forest plots, Begg's rank test, and Egger's regression test were all used to check for publication bias. To look for heterogeneity, I2 was computed, and an overall estimated analysis was carried out. Subgroup analysis was done by region, and publication year. Meta-regression analysis using study-level covariates as predictors of study-level estimates to explore the determinants of potential heterogeneity in our pooled estimates. The pooled odds ratio for related covariates was also calculated.

RESULTS

Out of 1128 studies assessed, 14 met our criteria and were included in the study. A total of 3773 people were included in the study. The prevalence of urinary tract infection was estimated to be 15.97% (95% CI: 12.72-19.23). According to subgroup analysis, the highest prevalence was observed in the SNNP region (19.21%) and studies conducted in and after 2018 (17.98%). Being female (AOR = 3.77; 95% CI: 1.88, 5.65), being illiterate (AOR = 5.29; 95% CI: 1.98, 8.61), prior urinary tract infection history (AOR = 3.04; 95% CI: 2.16-3.92) were the predictor of urinary tract infection.

CONCLUSION

The prevalence of urinary tract infections was high in Ethiopia. Female gender, illiteracy, and prior UTI history were associated with urinary tract infections. Since UTIs in diabetic patients has serious medical and public health consequence, screening of UTIs in diabetic patients and early initiation of treatment should become a public health priority.

The prevalence of urinary tract infections in type 2 diabetic patients: a systematic review and meta-analysis

Background

Urinary tract infection is the most common infection in type 2 diabetic patients. Various studies have reported different outbreaks of urinary tract infections in type 2 diabetic patients. Therefore, the present study aimed to determine the prevalence of urinary tract infections in type 2 diabetic patients during a systematic review and meta-analysis in order to develop interventions to reduce the incidence of urinary tract infections in type 2 diabetic patients.

Methods

In this study, systematic review and meta-analysis of study data related to the prevalence of urinary tract infection in type 2 diabetic patients were conducted using keywords including type 2 diabetes, urinary tract infection, diabetes, prevalence, meta-analysis and their English equivalents in SID, MagIran, IranMedex, IranDoc, Google Scholar, Cochrane, Embase, Science Direct, Scopus, PubMed and Web of Science (WoS) databases from 1993 to 2020. In order to perform the analysis of qualified studies, the model of random-effects was used, and the inconsistency of studies with the I2 index was investigated. Data analysis was performed with Comprehensive Meta-Analysis (Version 2).

Results

Based on a total of 15 studies with a sample size of 827,948 in meta-analysis, the overall prevalence of urinary tract infection in patients with type 2 diabetes was 11.5% (95% confidence interval: 7.8–16.7%). The prevalence of urinary tract infections in diabetic Iranian patients increased with increasing number of years of research, (p < 0.05), and with increasing age of participants (p < 0.05), but however the prevalence decreased with increasing sample size (p < 0.05).

Conclusion

This study shows that urinary tract infections are highly prevalent in patients with type 2 diabetes. Therefore, due to the growing prevalence of diabetes and its complications such as urinary tract infections, the need for appropriate screening programs and health care policies is becoming more apparent.