scholarly journals Is stratification of antibiotic susceptibility of urinary pathogens useful in the Emergency Department?

2020 ◽  
Vol 33 (5) ◽  
pp. 379-382
Author(s):  
Yolanda Hernández-Hermida ◽  
Nerea López-Muñoz ◽  
Juan-Ignacio Alós ◽  
Keyword(s):  
Escherichia Coli ◽  
Emergency Department ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Antibiotic Treatment ◽  
Antibiotic Susceptibility ◽  
Demographic Data ◽  
Local Resistance ◽  
Treatment Results

Objective. The aim of the study wat to analyze the antibiotic susceptibility of the pathogens causing urinary tract infection (UTI) and to stratify the results in function of patient´s clinical and demographic dates. Material and methods. The susceptibility of the pathogens isolated in the urine of 144 patients with UTI randomly chosen was analyzed. The results were stratified in function of sex, age, type of UTI, previous UTI and previous antibiotic treatment. Results. The susceptibility of the all isolates and of the Escherichia coli isolates was analyzed. There were significant differences between groups in function of sex (fluoroquinolones), age (cefuroxime, ertapenem and gentamicin), type of UTI (cefuroxime, cefotaxime, ertapenem and fluoroquinolones), previous UTI and previous antibiotic treatment (cefotaxime, fluoroquinolones and fosfomycin). Conclusions. The use of clinical and demographic data according to population and local resistance epidemiology of the pathogen causing UTI may help to select an adequate empirical treatment for UTI.

scholarly journals Bacteriuria With CTX-M-8 Extended-Spectrum β-Lactamase–Producing Escherichia coli in a Patient With Incomplete Kawasaki Disease

2019 ◽  
Vol 6 ◽  
pp. 2333794X1882194
Author(s):  
Kaori Kamijo ◽  
Yoshifusa Abe ◽  
Takehi Kagami ◽  
Kazuhisa Ugajin ◽  
Takeshi Mikawa ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Kawasaki Disease ◽  
Tract Infection ◽  
Antibiotic Treatment ◽  
Voiding Cystourethrography ◽  
Urine Specimen ◽  
Incomplete Kawasaki Disease ◽  
Extended Spectrum

We report the case of a 2-month-old infant with incomplete Kawasaki disease that presented as an apparent urinary tract infection. The patient’s fever persisted despite antibiotic treatment. Intravenous immunoglobulin and aspirin therapy cured both the incomplete Kawasaki disease and bacterial pyuria. Renal sonography, voiding cystourethrography, and renal parenchyma radionuclide scanning did not detect any abnormalities. Temporary dilation of the coronary artery was noted. In a urine specimen obtained through transurethral catheterization, the growth of 105 colony-forming units/mL of extended-spectrum β-lactamase–producing Escherichia coli was detected. Polymerase chain reaction analysis revealed that the enzyme genotype was CTX-M-8, which is a rare type in Japan. In conclusion, attention should be paid to a misleading initial presentation of fever and pyuria, which might be interpreted as urinary tract infection in patients with Kawasaki disease. Furthermore, pediatricians should consider incomplete Kawasaki disease when patients present with fever and pyuria, which are consistent with urinary tract infection, but do not respond to antibiotic treatment.

scholarly journals Development of risk prediction models to predict urine culture growth for adults with suspected urinary tract infection in the emergency department: protocol for an electronic health record study from a single UK university hospital

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Patrick Rockenschaub ◽  
Martin J. Gill ◽  
David McNulty ◽  
Orlagh Carroll ◽  
Nick Freemantle ◽  
...  
Keyword(s):  
Emergency Department ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Hospital Admission ◽  
Risk Prediction ◽  
Antibiotic Treatment ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Electronic Health

Abstract Background Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 h, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions. Methods Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimate the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/2019. Discussion Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.

ANTIBIOTIC SUSCEPTIBILITY PATTERN OF ESCHERICHIA COLI ISOLATED FROM CHILDREN WITH URINARY TRACT INFECTION

2021 ◽  
pp. 152-157
Author(s):  
YOGESH OLI ◽  
GANESH BHANDARI ◽  
UPASHANA BHANDARI ◽  
SUNITA BISTA ◽  
AMRIT KUMAR BHATTARAI ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Antibiotic Susceptibility ◽  
Diffusion Method ◽  
Susceptibility Pattern ◽  
Disk Diffusion Method ◽  
Antibiotic Susceptibility Pattern ◽  
E Coli

Objective: This work aimed to detect the antibiotic susceptibility pattern of Escherichia coli isolated from children, as it is the most predominant pathogen of urinary tract infection (UTI). Methods: About 530 urine samples were collected and tested using the modified Kirby–Bauer disk diffusion method to find the susceptibility pattern of isolated bacteria. Results: Out of a total of 530 samples, 114 (21.50%) showed significant growth. A total of 8 different types of bacteria were isolated from the growth of positive samples. Among the isolates, E. coli 66 (57.8%) was found to be the most predominant organism followed by Klebsiella pneumoniae 18(15.8%), Proteus spp. 10 (8.8%), Staphylococcus aureus 8 (7.0%), Acinetobacter spp. 4 (3.5%), CoNS 4 (3.5%), Enterobacter spp. 2 (1.8%), and Pseudomonas aeruginosa 2 (1.8%). In the present study, out of 66 E. coli, 37 (56.1%) were multidrug-resistant strain. E. coli showed 94.0% resistance to ceftriaxone followed by ceftazidime 86.5% and cefotaxime 70.3%. Imipenem (91.9%) followed by amikacin (89.2%) seems to be the effective drug against UTI causing E. coli in children. Conclusion: Multidrug resistance may possess difficulties with the choice of therapeutic options for the treatment of severe infections.

scholarly journals Development of risk prediction models to predict urine culture growth for patients with suspected urinary tract infection in the emergency department: protocol for an electronic health record study from a single UK hospital

2020 ◽  
Author(s):  
Patrick Rockenschaub ◽  
Martin J Gill ◽  
David McNulty ◽  
Orlagh Carroll ◽  
Nick Freemantle ◽  
...  
Keyword(s):  
Emergency Department ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Hospital Admission ◽  
Risk Prediction ◽  
Antibiotic Treatment ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Electronic Health

Abstract Background: Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 hours, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions.Methods: Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimates the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected urinary tract infection syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/19.Discussion: Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.

scholarly journals Development of risk prediction models to predict urine culture growth for adults with suspected urinary tract infection in the emergency department: protocol for an electronic health record study from a single UK university hospital

2020 ◽  
Author(s):  
Patrick Rockenschaub ◽  
Martin J Gill ◽  
David McNulty ◽  
Orlagh Carroll ◽  
Nick Freemantle ◽  
...  
Keyword(s):  
Emergency Department ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Hospital Admission ◽  
Risk Prediction ◽  
Antibiotic Treatment ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Electronic Health

Abstract Background:Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 hours, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions.Methods:Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimates the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/19. Discussion:Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.

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