The predictive utility of prior positive urine culture in children with recurrent urinary tract infections

Repurposing antibiotic resistance surveillance data to support treatment of recurrent infections in a remote setting

In northern Australia, a region with limited access to healthcare and a substantial population living remotely, antibiotic resistance adds to the complexity of treating infections. Focussing on Escherichia coli urinary tract infections (UTIs) and Staphylococcus aureus skin & soft tissue infections (SSTIs) captured by a northern Australian antibiotic resistance surveillance system, we used logistic regression to investigate predictors of a subsequent resistant isolate during the same infection episode. We also investigated predictors of recurrent infection. Our analysis included 98,651 E. coli isolates and 121,755 S. aureus isolates from 70,851 patients between January 2007 and June 2020. Following an initially susceptible E. coli UTI, subsequent recovery of a cefazolin (8%) or ampicillin (13%) -resistant isolate during the same infection episode was more common than a ceftriaxone-resistant isolate (2%). For an initially susceptible S. aureus SSTI, subsequent recovery of a methicillin-resistant isolate (8%) was more common than a trimethoprim-sulfamethoxazole-resistant isolate (2%). For UTIs and SSTIs, prior infection with a resistant pathogen was a strong predictor of both recurrent infection and resistance in future infection episodes. This multi-centre study demonstrates an association between antibiotic resistance and an increased likelihood of recurrent infection. Particularly in remote areas, a patient's past antibiograms should guide current treatment choices since recurrent infection will most likely be at least as resistant as previous infection episodes. Using population-level surveillance data in this way can also help clinicians decide if they should switch antibiotics for patients with ongoing symptoms, while waiting for diagnostic results.

Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning-based spectroscopic analysis

The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the antibiotic resistance of bacteria in urinary tract infections (UTIs) based on surface-enhanced Raman scattering (SERS) using a positively charged gold nanoparticle planar solid SERS substrate. Then, an intelligent identification model for SERS spectra based on the deep learning technique is constructed to realize the rapid, ultrasensitive, and non-labeled detection of pathogenic bacteria. A total of 54,000 SERS spectra were collected from 18 isolates belonging to 6 species of common UTI bacteria in this work to realize identification of bacterial species, antibiotic sensitivity, and multidrug resistance (MDR) via convolutional neural networks (CNN). This method significantly simplify the Raman data processing processes without background removing and smoothing, however, achieving 96% above classification accuracy, which was significantly greater than the 85% accuracy of the traditional multivariate statistical analysis algorithm principal component analysis combined with the K-nearest neighbor (PCA-KNN). This work clearly elucidated the potential of combining SERS and deep learning technique to realize culture-free identification of pathogenic bacteria and their associated antibiotic sensitivity.

The microbiome in urinary tract infections in children - an update

PURPOSE OF REVIEW

Urinary tract infection (UTI) is one of the most common pediatric infections worldwide. Recently introduced 16S rRNA sequencing allows detailed identification of bacteria involved in UTI on a species-based level. The urogenital microbiome in children is scarcely investigated, with underlying conditions differing from adults. Improvement in diagnostic and therapeutic approaches can help to minimize unnecessary antibiotic treatments, thereby protecting the physiological microbiome.

RECENT FINDINGS

Healthy bladders of children display a distinct microbiome than those of adults. UTI is characterized by changes in bacterial composition, with a high prevalence of Enterobacterales. There is a correlation between bacterial species and the pH of the urine, so a characteristic age-related pathogen pattern can be found due to the acidic urine in infants and more alkaline urine in older children. Recently, new methods were proposed to overcome the suboptimal diagnostic performance of urine cultures and urine dipstick test. This allows precise treatment decisions and helps to prevent chronification of UTI, related voiding dysfunctions and renal scaring, systemic abiosis, and the development of antibiotic resistance.

SUMMARY

Uropathogens involved in UTIs in children should be identified with precision to allow targeted therapeutic decisions. This can also help preventing the destruction of the microbiome homeostasis, which could result in a life-long dysbiosis. New treatment approaches and recolonization with probiotics are necessary due to increasing intrinsic antibiotic resistance of bacteria.

Antimicrobial Resistance and Genotyping of Bacteria Isolated from Urinary Tract Infection in Children in an Iranian Referral Hospital

Introduction

Urinary tract infection (UTI) is one of the most common bacterial infections in childhood, and the increasing rate of antibiotic resistance to the commonly prescribed antimicrobial agents against it has become a major concern. The aim of this study was to determine the antibiotic resistance and genotyping of bacteria isolated from urine cultures in patients referred to the Children’s Medical Center, Tehran, Iran.

Methods

During the 1-year period, antimicrobial susceptibility profiles of strains isolated from patients with UTI were determined. Typing of the isolates causing nosocomial infections was performed by random amplified polymorphic DNA (RAPD) analysis, and the results were analyzed by Gelcompar II software.

Results

In this study, 203 children (130 girls and 73 boys) were included. The patients’ age ranged from 1 day to 16 years (IQR average=4 months to 4 years). The most frequent isolated organisms were Escherichia coli (118 isolates, 58%), followed by Klebsiella pneumoniae (30 isolates, 15%). Sixty-two strains (18 strains of E. coli, 13 strains of K. pneumoniae, 11 strains of Enterococcus faecium, and five strains of Burkholderia cepacia complex) had criteria of nosocomial infection. A high resistance rate to trimethoprim-sulfamethoxazole (69%) and cefotaxime (60%) was reported in E. coli and K. pneumoniae strains, respectively. Pseudomonas aeruginosa strains showed high sensitivity to amikacin (100%). All E. faecium strains were susceptible to trimethoprim-sulfamethoxazole (100%), and 23% of the strains were resistant to vancomycin. The analysis of RAPD-typing revealed the presence of three clusters in E. coli, two clusters in E. faecium, and one clone in K. pneumoniae. Besides, four out of five isolates of B. cepacia complex had more than 90% genetic similarity.

Conclusion

The most frequent isolated pathogen was E. coli, and an increasing rate of antibiotic resistance to the commonly prescribed antimicrobial agents such as trimethoprim/sulfamethoxazole and cephalosporins was observed. Moreover, the results of this study showed the presence of clones with ≥80% similarity in E. coli, K. pneumoniae, E. faecium, and B. cepacia complex isolates; therefore, the transmission of nosocomial infections from one patient to another or one ward to another is probable.