Nomogram for diagnosing acute pyelonephritis in pediatric urinary tract infection

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.