Co-Amoxiclav as empiric treatment of UTI in children: importance of surveillance in ensuring optimal empiric treatment choice.
James TrayerMichael HorganAnna-Rose PriorMartin RyanMontasser NadeemPublished in: International journal of clinical pharmacy (2021)
Background Urinary tract infections are common and require prompt treatment. Objective To examine the resistance rates of co-amoxiclav in children with urinary tract infection and whether antimicrobial resistance is influenced by other variables. Methods The records and antibiotic susceptibility data of 209 patients admitted with symptomatic urinary tract infection between January 2018 and December 2019 were reviewed. Results We examined 209 patients [mean (SD) age 23.73 (32.86) months], of whom 176 (84.2%) had first urinary tract infection. Escherichia coli was isolated in 190 (90.1%). Uropathogens were sensitive to co-amoxiclav in 47.8% of patients and gentamicin in 95.2%. Combined co-amoxiclav with gentamicin demonstrated antimicrobial sensitivity in 96.2%. Antimicrobial resistance was associated with longer hospital stay (p-value < 0.02). An association was identified between co-amoxiclav resistance and recurrent urinary tract infections. Uropathogens were resistant to co-amoxiclav in 80/176 (45.5%) and 29/33 (87.9%) patients with first and recurrent urinary tract infections, respectively (p-value 0.001). No link was observed between antimicrobial resistance and atypical urinary tract infection. Conclusion Approximately half of children in this cohort had urinary tract infection due to uropathogens resistant to co-amoxiclav. Co-amoxiclav resistance is associate with recurrent infections and longer hospital stays. A combination of co-amoxiclav and gentamicin demonstrates > 96% susceptibility.
Keyphrases
- urinary tract infection
- antimicrobial resistance
- end stage renal disease
- escherichia coli
- young adults
- newly diagnosed
- chronic kidney disease
- ejection fraction
- healthcare
- prognostic factors
- public health
- emergency department
- combination therapy
- machine learning
- pseudomonas aeruginosa
- artificial intelligence
- candida albicans
- patient reported outcomes
- data analysis