Background: The COVID-19 pandemic has substantially affected the antibiotic stewardship activities in most hospitals of India.
Aims: We conducted an antibiotic point prevalence survey (PPS) immediately after the decline of a major COVID-19 wave at a dedicated COVID-19 hospital. By doing so we aimed to identify the antibiotic prescription patterns, identify factors influencing the choice of antibiotics, and identify/develop strategies to improve the antibiotic stewardship program in such setups.
Methods: The PPS was single-centred, cross-sectional, and retrospective in nature. Patients admitted in various wards and intensive care units (ICUs) between September 2021 to October 2021 were included in our PPS.
Results: Of the included 460 patients, 192 were prescribed antibiotics. Of these 192 patients, ICU-admitted patients had the highest number of antibiotics prescribed i.e. 2.09 ± 0.92. Only a minor fraction (7.92 %) of antibiotics prescriptions were on the basis of culture reports. Most of the antibiotics were prescribed empirically by the parenteral route. The most common group of antibiotics prescribed were third-generation cephalosporins. Carbapenems were the most common designated antibiotics prescribed. A large number of patients (22.40 %) were prescribed a double anaerobic coverage.
Conclusion: The strategies that we identified to improve the antibiotic stewardship program at our institute included reviving the culture of sending culture reports to prescribe antibiotics, improving surgical prophylaxis guidelines, training resident doctors to categorize antibiotic prescriptions appropriately, closely monitoring prescriptions providing double anaerobic coverage, and improving the electronic medical record system for improving prescription auditing.
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http://dx.doi.org/10.1016/j.infpip.2022.100253 | DOI Listing |
Pediatr Infect Dis J
January 2025
Department of Paediatrics, University of Melbourne.
Background: Lower respiratory tract infections (LRTIs) remain a leading cause of community-acquired and nosocomial infection in children and a common indication for antimicrobial use and intensive care admission. Determining the causative pathogen for LRTIs is difficult and traditional culture-based methods are labor- and time-intensive. Emerging molecular diagnostic tools may identify pathogens and detect antimicrobial resistance more quickly, to enable earlier targeted antimicrobial therapy.
View Article and Find Full Text PDFBackground: has recently been categorized as low-risk for AmpC β-lactamase inducible production, but research on outcomes in bacteremia by antibiotic choice is limited.
Objectives: This study examined the clinical characteristics and outcomes of patients with ceftriaxone-susceptible bacteremia who received AmpC-directed β-lactam therapy vs. narrower spectrum therapies.
Front Vet Sci
December 2024
Faculty of Veterinary Medicine, Helsinki One Health, University of Helsinki, Helsinki, Finland.
Background: Canine gastroenteritis (CGE) is a common cause for seeking veterinary care in companion animal medicine and an area where antibiotics have been reported to be widely used. Therefore, creating relevant benchmarks for antibiotic use in CGE is important when implementing and analyzing antibiotic stewardship interventions. The aim of this paper was to describe the level and temporal trend of systemic antibiotic use for CGE in Sweden between 2020 and 2023.
View Article and Find Full Text PDFFront Public Health
December 2024
Institute of Pharmacy, Faculty of Pharmaceutical and Allied Health Sciences, Lahore College for Women University, Lahore, Pakistan.
J Infect
December 2024
Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK. Electronic address:
Background: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.
Methods: We used XGBoost machine learning models to predict antimicrobial resistance to seven antibiotics in patients with Enterobacterales bloodstream infection. Models were trained using hospital and community data from Oxfordshire, UK, for patients with positive blood cultures between 01-January-2017 and 31-December-2021.
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