Nomogram for diagnosing acute pyelonephritis in pediatric urinary tract infection

Influencing Factors of Urinary Tract Stones Complicated by Urinary Tract Infections and the Construction of a Column Chart Prediction Model

Objective: To analyze the influencing factors of urinary tract stones complicated by urinary tract infections and construct a column chart prediction model. Patients and Methods: From July 2020 to October 2023, 345 patients with urinary tract stones admitted to our hospital were collected as the training set, they were separated into an infection group of 51 cases and a non-infection group of 294 cases on the basis of the presence or absence of concurrent urinary tract infections; 192 patients with urinary tract stones were used as the testing set and were divided into an infection group of 26 cases and a non-infection group of 166 cases on the basis of the presence or absence of concurrent urinary tract infections. Data such as gender, age, and procalcitonin (PCT) were recorded. Multi-variable logistic regression analysis was applied to screen predictive factors, R4.0.2 software was applied to construct a column chart model, the calibration curve and Receiver Operating Characteristic (ROC) curve were applied to evaluate the discrimination and calibration of the column chart model; decision curve analysis curve was applied to evaluate the predictive performance of column chart models. Results: The proportions of female, diabetes mellitus, indwelling time of urinary catheter ≥7 days, the PCT, and urine pH in the infected group were greater than those in the non-infected group (p < 0.05). Female, diabetes mellitus, catheter retention time ≥7 days, high PCT, and high urine pH were independent risk factors for urinary calculi complicated with urinary tract infection (p < 0.05). Training set: C-index was 0.913, Area Under Curve (AUC) was 0.943 [95% Confidence Interval (CI) = 0.912-0.973], sensitivity was 86.36%, and specificity was 89.81%, testing set: C-index was 0.905, AUC was 0.959 (95% CI = 0.928-0.989), sensitivity was 84.65%, and specificity was 95.84%, indicating good discriminability of the line graph model; Hosmer-Lemeshow test showed χ2 = 2.843, 2.894, p = 0.944, 0.941, the calibration curve approached the ideal curve, and the line graph model had good calibration. When the risk threshold for urinary tract stones complicated by urinary tract infections was between 0.08 and 0.86, this column chart model provided clinical net benefits. Conclusion: The column chart prediction model for urinary tract stones complicated by urinary tract infections constructed in this study has high predictive efficiency and clinical practical value, and can provide reference for medical staff.

Atlas of patient-reported outcome measures, nomograms and scoring systems used in simple and complicated urinary tract infections: a systematic review

Background

Urinary tract infections (UTIs) are common and result in a significant impact on quality of life (QoL). Despite their prevalence, there seems to be a lack of evidence around patient-reported outcomes and measuring tools such as scoring systems and nomograms in UTIs. Patient-reported outcome measures (PROMs) help us measure patient-related symptoms and their QoL.

Objective

Our literature review shows an up-to-date "atlas" of the available PROMs, nomograms and scoring systems that can help clinicians in treatment decisions and track treatment response in patients with UTIs.

Design

Systematic review of the literature.

Data sources and methods

A comprehensive systematic review was carried out on PubMed Medline, Scopus and CINAHL, according to PRISMA guidelines, using search terms related to PROMs, nomograms and scoring systems used in simple and complicated UTIs. A narrative review was done, and tool characteristics, accuracy, validation, and applicability were collected and summarized.

Results

Sixty-two articles (with 16 different PROMs) were included in the final review. These included generic tools such as the 36-Item Short Form Health Survey and specific tools like the Acute Cystitis Symptom Score and Recurrent Urinary Tract Infection Impact Questionnaire, amongst others. While scoring systems seemed to be used for severe infections such as Fournier's gangrene and emphysematous pyelonephritis, nomograms were primarily used for diagnosis and risk prediction. PROMs are useful tools and have utility within the management of patients with UTIs, but further clarity is needed as to which of these tools is most appropriate for each type of UTI as each offer their respective advantages and disadvantages.

Conclusion

This atlas is the first comprehensive review of PROMs, scoring systems and nomograms in the management of UTIs. While PROMs improve patient care, further standardisation, external validation and accuracy are needed. While nomograms and scoring systems can help clinicians, these must be tailored to individual patients based on their specific clinical scenarios.

Trial registration

PROSPERO registration number CRD42025625865.

Association between hyponatremia and disease severity in pediatric urinary tract infections

BACKGROUND

Acute urinary tract infection (UTI) is a common disease in pediatrics, with around 8% of girls and 2% of boys experiencing a UTI by age 7y/o. UTIs can range from asymptomatic bacteriuria to acute pyelonephritis (APN) in severe cases involving renal parenchymal infection. UTI patients admitted to the pediatric ward usually have more severe clinical presentation, compared to those treated in the outpatient settings. Therefore, it will be helpful to have markers that predict the severity of the disease and the likelihood of having APN.

METHODS

We performed a retrospective review on all pediatric UTI/APN patients treated in the inpatient setting at a Medical Center from October 2012 to September 2022. Patients were assigned to the "hyponatremia" or "eunatremia" group according to their serum sodium concentrations. Detailed information, including renal echo, blood, and urine test results, were collected for the analysis of multivariable logistic regression model.

RESULTS

The study included 344 patients, of which 99 (28.8%) had hyponatremia, and 245 (71.2%) had normal serum sodium levels. The hyponatremia group had higher APN frequency, renal echo abnormality, and higher CRP level. In multivariable analysis, hyponatremia was independently associated with increased serum glucose (OR: 1.01, 95% CI: 1.00-1.03, p = 0.0365) and CRP levels (OR: 1.00, 95% CI: 1.00-1.01, p = 0.0417), without a significant increase in APN frequency as the final diagnosis.

CONCLUSIONS

Our findings suggest that hyponatremia in pediatric UTI patients may indicate a more severe disease, such as APNs, higher CRP levels, or renal echo abnormalities. The complex mechanisms underlying hyponatremia and its predictive value for disease severity warrant further investigation.

Evaluation of new generation systemic immune-inflammation markers to predict urine culture growth in urinary tract infection in children

Objective

Systemic inflammation has been implicated in the development and progression of urinary tract infection (UTI). Accordingly, the aim of this study is to determine whether the white blood cell (WBC), C-reactive protein (CRP), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) are useful markers to predict of urine culture growth in children with UTI. The second aim of this study is to evaluate the prevalence of UTI pathogens, antibiotic resistance patterns, and empirical treatment options in children diagnosed with UTI based on laboratory and clinical findings.

Method

The study population comprised 413 cases (positive urine culture) and 318 cases (negative urine culture) of pediatric patients with UTI.

Results

There was no statistically significant difference observed in the median levels of hemoglobin, hematocrit, and platelet between the negative and positive culture groups. The median levels of monocytes, WBC, NLR, SII, and CRP of the patients with a positive urine culture were shown to be statistically significantly higher than the patients with a negative urine culture. The AUC value was 0.747 (0.710-0.784) for CRP with a cutoff value of 3.2, the sensitivity value was 56.4%, and the specificity value was 98.4% in terms of UTI. The AUC value was 0.733 (0.697-0.769) for SII with a cutoff value of 600, the sensitivity value was 58.4%, and the specificity value was 83.0%. The AUC value was 0.732 (0.697-0.769) for NLR with a cutoff value of 2, the sensitivity value was 57.4%, and the specificity value was 81.1%.

Conclusion

WBC, CRP, NLR, PLR, and SII could potentially serve as useful independent diagnostic or complementary markers for disease in children diagnosed with UTI who exhibit a positive urine culture. Escherichia coli was found to be the most common causative agent, and the commonly prescribed antibiotic was cephalosporin. However, it was observed that all identified agents of pediatric UTIs in our center exhibited high resistance to cefuroxime, trimethoprim-sulfamethoxazole, cefixime, ampicillin, and ceftriaxone.

Artificial Intelligence Can Guide Antibiotic Choice in Recurrent UTIs and Become an Important Aid to Improve Antimicrobial Stewardship

BACKGROUND

A correct approach to recurrent urinary tract infections (rUTIs) is an important pillar of antimicrobial stewardship. We aim to define an Artificial Neural Network (ANN) for predicting the clinical efficacy of the empiric antimicrobial treatment in women with rUTIs.

METHODS

We extracted clinical and microbiological data from 1043 women. We trained an ANN on 725 patients and validated it on 318.

RESULTS

The ANN showed a sensitivity of 87.8% and specificity of 97.3% in predicting the clinical efficacy of empirical therapy. The previous use of fluoroquinolones (HR = 4.23; p = 0.008) and cephalosporins (HR = 2.81; p = 0.003) as well as the presence of Escherichia coli with resistance against cotrimoxazole (HR = 3.54; p = 0.001) have been identified as the most important variables affecting the ANN output decision predicting the fluoroquinolones-based therapy failure. A previous isolation of Escherichia coli with resistance against fosfomycin (HR = 2.67; p = 0.001) and amoxicillin-clavulanic acid (HR = 1.94; p = 0.001) seems to be the most influential variable affecting the output decision predicting the cephalosporins- and cotrimoxazole-based therapy failure. The previously mentioned Escherichia coli with resistance against cotrimoxazole (HR = 2.35; p < 0.001) and amoxicillin-clavulanic acid (HR = 3.41; p = 0.007) seems to be the most influential variable affecting the output decision predicting the fosfomycin-based therapy failure.

CONCLUSIONS

ANNs seem to be an interesting tool to guide the antimicrobial choice in the management of rUTIs at the point of care.