Trends in Antibiotic-Resistant Bacteria Isolated from Screening Clinical Samples in a Tertiary Care Hospital over the 2018-2022 Period.
Delphine LemonnierMarine MachuelOdile ObinGaëtan OuturquinCrespin AdjidéCatherine MulliéPublished in: Antibiotics (Basel, Switzerland) (2023)
To assess the putative impact of the COVID-19 pandemic on multidrug-resistant (MDR) bacteria recovered from routine screening samples and, more globally, the trends in time to first positive screening sample and carriage duration of those bacteria in patients admitted to a tertiary hospital, data from laboratory results were retrospectively mined over the 2018-2022 period. No significant differences could be found in the number of positive patients or MDR isolates per year, time to positive screening, or carriage duration. Extended-spectrum beta-lactamase producers were dominant throughout the studied period but their relative proportion decreased over time as well as that of meticillin-resistant Staphylococcus aureus . Meanwhile, carbapenemase-producing enterobacteria (CPE) proportion increased. Among the 212 CPE isolates, Klebsiella pneumoniae and Escherichia coli were the more frequent species but, beginning in 2020, a significant rise in Enterobacter cloacae complex and Citrobacter freundii occurred. OXA48 was identified as the leading carbapenemase and, in 2020, a peak in VIM-producing enterobacteria linked to an outbreak of E. cloacae complex during the COVID-19 pandemic was singled out. Finally, a worrisome rise in isolates producing multiple carbapenemases (NDM/VIM and mostly NDM/OXA48) was highlighted, especially in 2022, which could lead to therapeutic dead-ends if their dissemination is not controlled.
Keyphrases
- klebsiella pneumoniae
- multidrug resistant
- escherichia coli
- acinetobacter baumannii
- drug resistant
- gram negative
- staphylococcus aureus
- end stage renal disease
- genetic diversity
- chronic kidney disease
- newly diagnosed
- ejection fraction
- prognostic factors
- machine learning
- peritoneal dialysis
- electronic health record
- deep learning
- clinical practice
- big data
- artificial intelligence
- cystic fibrosis
- pseudomonas aeruginosa