Silent spread of pathogens in hospital settings: a one-week molecular epidemiologic study
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Updated: Sep 13, 2025

Author Spotlight: Advancing Pathogen Detection and Disease Assessment in Real-Time Using M-ROSE
Published on: March 1, 2024
Elif S Tanriverdi1, Yusuf Yakupogullari1, Deste Ceylan1
1Department of Medical Microbiology, Inonu University Faculty of Medicine, Malatya, Türkiye.
Introduction:
Molecular fingerprinting analyses of the pathogens isolated from healthcare-associated infections (HAIs) in an off-outbreak period can provide important data which cannot be obtained by prospective surveillance. Such data may indicate unnoticed breaks in infection control measures and guide in determining targeted interventions or reinforcements. The study aimed to analyze the clonal relatedness of pathogens isolated from HAIs during a period when the facility}'s HAI rate remained stable.
Methodology:
A prospective cross-sectional study was conducted in a university hospital. A total of 105 bacterial pathogens isolated from HAIs in a one-week period were genotyped using pulsed-field gel electrophoresis.
Results:
All 12 enterococci isolates belonged to one of the genotypes that infected more than one patient. There was an epidemic clone in Staphylococcus aureus responsible for 7 out of 12 HAIs caused by this species. Among the Gram-negative bacteria, Acinetobacter baumannii showed the highest clonality, with 3 genotypes responsible for 13 out of 16 HAIs caused by this species. Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa showed the lowest clonality, and their spreads involved a pair of patients in a total of 7 events.
Conclusions:
This study showed that almost half of HAIs were due to clonal spread that was not detected by active surveillance. Enterococci, S. aureus, and A. baumannii had the highest clonality, suggesting that a significant proportion of HAIs could be prevented in healthcare facilities where these pathogens predominate. Repeating comprehensive studies in hospitals at regular intervals will be useful to identify unnoticed breaks in infection control measures.
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