&lt;p&gt;Blood Neutrophil-to-Lymphocyte Ratio and Urine IL-8 Levels Predict the Type of Bacterial Urinary Tract Infection in Type 2 Diabetes Mellitus Patients&lt;/p&gt;

Sharif-Askari, Fatemeh Saheb; Sharif-Askari, Narjes Saheb; Guella, Adnane; Alabdullah, Ali; Sheleh, Hour Bashar Al; AlRawi, Afnan Maher Hoory; Haddad, Enad Sami; Hamid, Qutayba; Halwani, Rabih; Hamoudi, Rifat

doi:10.2147/idr.s251966

Cited by 14 publications

(11 citation statements)

References 29 publications

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“…Although some patients may present with asymptomatic bacteriuria (ASB), most UTIs in T2DM patients exhibit elevated levels of infection markers in urine analysis ( Sharma et al, 2017 ). Additionally, this study demonstrated that blood routine examination, such as neutrophil count, may also serve as potential indicators, which is consistent with the findings of Fatemeh et al ( Saheb Sharif-Askari et al, 2020 ).…”

Section: Discussionsupporting

confidence: 92%

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

Xiong,

Liu,

et al. 2024

Front. Pharmacol.

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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.

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Section: Discussionsupporting

confidence: 92%

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

Xiong,

Liu,

et al. 2024

Front. Pharmacol.

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“…Increased NLR and IL-8 not only refers to the etiology, but also indicates the occurrence of renal damage. Among the aforementioned markers, NLR had the strongest predictive value for diagnosing UTIs caused by ESBL producing E. coli with 88% sensitivity, 73% specificity, 93% PPV and 24% NPV [ 22 ].…”

Section: Resultsmentioning

confidence: 99%

Biomarkers in urinary tract infections – which ones are suitable for diagnostics and follow-up?

Horváth¹,

Wullt

Naber

et al. 2020

GMS Infectious Diseases; 8:Doc24

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“…[16,45] Additionally, many biomarkers, are impacted by intrinsic host factors such as age, sex, urinary abnormalities, genetic polymorphisms, and comorbid conditions which affect expression levels and responses to infection. [46][47][48] To determine if these three biomarkers were sensitive and specific indicators for UTIs, this study measured them in both Definitive UTI cases (symptomatic cases, diagnosed in a Urology/Urogynecology specialty setting, with uropathogens identified above threshold values by both SUC and M-PCR) and in Definitive non-UTI control cases (asymptomatic based on FDA-defined criteria included in a Symptom Score Analysis)). The Definitive non-UTI cases included asymptomatic individual with detected microbes (asymptomatic bacteriuria).…”

Section: Discussionmentioning

confidence: 99%

Urine Biomarkers Individually and as a Consensus Model Show High Sensitivity and Specificity for Detecting UTIs

Akhlaghpour,

Haley,

Parnell

et al. 2023

Preprint

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We aimed to determine if infection-associated urine biomarkers can differentiate true urinary tract infection (UTI) from non-UTI controls. Midstream clean-catch urine samples were collected from asymptomatic volunteers and symptomatic subjects > 60 years old diagnosed with presumptive UTI in a specialty setting. Microbial identification and density were assessed using multiplex PCR/pooled antibiotic susceptibility test (M-PCR/P-AST) and standard urine culture (SUC). Three biomarkers (NGAL, IL-8, and IL-1β) were measured in the same urine specimens. Definitive UTI cases were symptomatic and had positive microorganism detection by SUC and M-PCR, while definitive non-UTI cases were asymptomatic volunteers regardless of microbial detection. We observed a strong positive correlation (R2 ≈ 1) between microbial density and the biomarkers NGAL, IL-8, and IL-1β. Biomarker consensus criteria of two or more positive biomarkers had sensitivity 90.2%, specificity 91.2%, positive predictive value (PPV) 91.7%, negative predictive value (NPV) 89.7%, accuracy 90.7%, positive likelihood ratio of 10.28, and negative likelihood ratio of 0.11 in differentiating definitive UTI from non-UTI cases, regardless of microbial density. NGAL, IL-8, and IL-1β showed a significant elevation in symptomatic cases with positive microbe identification compared to asymptomatic cases with or without microbe identification. Biomarker consensus exhibited high accuracy in distinguishing UTI from non-UTI cases.

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<p>Blood Neutrophil-to-Lymphocyte Ratio and Urine IL-8 Levels Predict the Type of Bacterial Urinary Tract Infection in Type 2 Diabetes Mellitus Patients</p>

Cited by 14 publications

References 29 publications

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

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

Biomarkers in urinary tract infections – which ones are suitable for diagnostics and follow-up?

Urine Biomarkers Individually and as a Consensus Model Show High Sensitivity and Specificity for Detecting UTIs

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