Modelling antimicrobial resistance transmission to guide personalized antimicrobial stewardship interventions and infection control policies in healthcare setting: a pilot study

Infection control programs and antimicrobial stewardship have been proven effective in reducing the burden of diseases due to multidrug-resistant organisms, but quantifying the effect of each intervention is an open issue. For this aim, we propose a model to characterize the effect of interventions...

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Veröffentlicht in:Scientific reports 2023-09, Vol.13 (1), p.15803-15803, Article 15803
Hauptverfasser: Durazzi, Francesco, Pezzani, Maria Diletta, Arieti, Fabiana, Simonetti, Omar, Canziani, Lorenzo Maria, Carrara, Elena, Barbato, Lorenzo, Onorati, Francesco, Remondini, Daniel, Tacconelli, Evelina
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Sprache:eng
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Zusammenfassung:Infection control programs and antimicrobial stewardship have been proven effective in reducing the burden of diseases due to multidrug-resistant organisms, but quantifying the effect of each intervention is an open issue. For this aim, we propose a model to characterize the effect of interventions at single ward level. We adapted the Ross-Macdonald model to describe hospital cross-transmission dynamics of carbapenem resistant Klebsiella pneumoniae (CRKP), considering healthcare workers as the vectors transmitting susceptible and resistant pathogens among admitted patients. The model parameters were estimated from a literature review, further adjusted to reproduce observed clinical outcomes, and validated using real life data from a 2-year study in a university hospital. The model has been further explored through extensive sensitivity analysis, in order to assess the relevance of single interventions as well as their synergistic effects. Our model has been shown to be an effective tool to describe and predict the impact of interventions in reducing the prevalence of CRKP colonisation and infection, and can be extended to other specific hospital and pathological scenarios to produce tailored estimates of the most effective strategies.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-42511-5