Literature DB >> 21788461

Impact of antibiotic exposure patterns on selection of community-associated methicillin-resistant Staphylococcus aureus in hospital settings.

Lidia Kardas-Sloma1, Pierre Yves Boëlle, Lulla Opatowski, Christian Brun-Buisson, Didier Guillemot, Laura Temime.   

Abstract

Community-associated methicillin-resistant S. aureus (CA-MRSA) is increasingly common in hospitals, with potentially serious consequences. The aim of this study was to assess the impact of antibiotic prescription patterns on the selection of CA-MRSA within hospitals, in a context of competition with other circulating staphylococcal strains, including methicillin-sensitive (MSSA) and hospital-associated methicillin-resistant (HA-MRSA) strains. We developed a computerized agent-based model of S. aureus transmission in a hospital ward in which CA-MRSA, MSSA, and HA-MRSA strains may cocirculate. We investigated a wide range of antibiotic prescription patterns in both intensive care units (ICUs) and general wards, and we studied how differences in antibiotic exposure may explain observed variations in the success of CA-MRSA invasion in the hospitals of several European countries and of the United States. Model predictions underlined the influence of antibiotic prescription patterns on CA-MRSA spread in hospitals, especially in the ICU, where the endemic prevalence of CA-MRSA carriage can range from 3% to 20%, depending on the simulated prescription pattern. Large antibiotic exposure with drugs effective against MSSA but not MRSA was found to promote invasion by CA-MRSA. We also found that, should CA-MRSA acquire fluoroquinolone resistance, a major increase in CA-MRSA prevalence could ensue in hospitals worldwide. Controlling the spread of highly community-prevalent CA-MRSA within hospitals is a challenge. This study demonstrates that antibiotic exposure strategies could participate in this control. This is all the more important in wards such as ICUs, which may play the role of incubators, promoting CA-MRSA selection in hospitals.

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Year:  2011        PMID: 21788461      PMCID: PMC3187008          DOI: 10.1128/AAC.01626-10

Source DB:  PubMed          Journal:  Antimicrob Agents Chemother        ISSN: 0066-4804            Impact factor:   5.191


  31 in total

1.  Modelling disease outbreaks in realistic urban social networks.

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2.  National Nosocomial Infections Surveillance (NNIS) System Report, data summary from January 1992 through June 2004, issued October 2004.

Authors: 
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3.  Preliminary analysis of the transmission dynamics of nosocomial infections: stochastic and management effects.

Authors:  B S Cooper; G F Medley; G M Scott
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4.  Nurse-staffing levels and the quality of care in hospitals.

Authors:  Jack Needleman; Peter Buerhaus; Soeren Mattke; Maureen Stewart; Katya Zelevinsky
Journal:  N Engl J Med       Date:  2002-05-30       Impact factor: 91.245

5.  How do physicians and nurses spend their time in the emergency department?

Authors:  J C Hollingsworth; C D Chisholm; B K Giles; W H Cordell; D R Nelson
Journal:  Ann Emerg Med       Date:  1998-01       Impact factor: 5.721

6.  Competition between methicillin-sensitive and -resistant Staphylococcus aureus in the anterior nares.

Authors:  M Dall'Antonia; P G Coen; M Wilks; A Whiley; M Millar
Journal:  J Hosp Infect       Date:  2005-09       Impact factor: 3.926

7.  An agent-based and spatially explicit model of pathogen dissemination in the intensive care unit.

Authors:  John R Hotchkiss; David G Strike; Dana A Simonson; Alain F Broccard; Philip S Crooke
Journal:  Crit Care Med       Date:  2005-01       Impact factor: 7.598

8.  Epidemiology of nosocomial infections caused by methicillin-resistant Staphylococcus aureus.

Authors:  R L Thompson; I Cabezudo; R P Wenzel
Journal:  Ann Intern Med       Date:  1982-09       Impact factor: 25.391

9.  Resource use in the ICU: short- vs. long-term patients.

Authors:  K Stricker; H U Rothen; J Takala
Journal:  Acta Anaesthesiol Scand       Date:  2003-05       Impact factor: 2.105

10.  Modelling of healthcare-associated infections: a study on the dynamics of pathogen transmission by using an individual-based approach.

Authors:  L Milazzo; J L Bown; A Eberst; G Phillips; J W Crawford
Journal:  Comput Methods Programs Biomed       Date:  2011-03-04       Impact factor: 5.428

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  21 in total

1.  Antibiotic reduction campaigns do not necessarily decrease bacterial resistance: the example of methicillin-resistant Staphylococcus aureus.

Authors:  Lidia Kardas-Sloma; Pierre-Yves Boëlle; Lulla Opatowski; Didier Guillemot; Laura Temime
Journal:  Antimicrob Agents Chemother       Date:  2013-07-01       Impact factor: 5.191

2.  Hospital-Acquired Methicillin-resistant Staphylococcus aureus Bacteremia Related to Medicare Antibiotic Prescriptions: A State-Level Analysis.

Authors:  Bryce T Fukunaga; Wesley K Sumida; Deborah A Taira; James W Davis; Todd B Seto
Journal:  Hawaii J Med Public Health       Date:  2016-10

Review 3.  Fluoroquinolone Restriction as an Effective Antimicrobial Stewardship Intervention.

Authors:  Kimberly C Claeys; Teri L Hopkins; Ana D Vega; Emily L Heil
Journal:  Curr Infect Dis Rep       Date:  2018-03-23       Impact factor: 3.725

4.  Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control.

Authors:  Laura Temime; Lulla Opatowski; David Rm Smith
Journal:  Elife       Date:  2021-09-14       Impact factor: 8.140

5.  In vitro activities of antibiotics and antimicrobial cationic peptides alone and in combination against methicillin-resistant Staphylococcus aureus biofilms.

Authors:  Emel Mataraci; Sibel Dosler
Journal:  Antimicrob Agents Chemother       Date:  2012-10-15       Impact factor: 5.191

6.  Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review.

Authors:  D E Ramsay; J Invik; S L Checkley; S P Gow; N D Osgood; C L Waldner
Journal:  Epidemiol Infect       Date:  2018-07-31       Impact factor: 4.434

Review 7.  What should be considered if you decide to build your own mathematical model for predicting the development of bacterial resistance? Recommendations based on a systematic review of the literature.

Authors:  Maria Arepeva; Alexey Kolbin; Alexey Kurylev; Julia Balykina; Sergey Sidorenko
Journal:  Front Microbiol       Date:  2015-04-29       Impact factor: 5.640

8.  Antibiotic-specific differences in the response of Staphylococcus aureus to treatment with antimicrobials combined with manuka honey.

Authors:  Michael Liu; Jing Lu; Patrick Müller; Lynne Turnbull; Catherine M Burke; Ralf C Schlothauer; Dee A Carter; Cynthia B Whitchurch; Elizabeth J Harry
Journal:  Front Microbiol       Date:  2015-01-27       Impact factor: 5.640

9.  Diversity of methicillin-resistant Staphylococcus aureus (MRSA) strains isolated from inpatients of 30 hospitals in Orange County, California.

Authors:  Lyndsey O Hudson; Courtney R Murphy; Brian G Spratt; Mark C Enright; Kristen Elkins; Christopher Nguyen; Leah Terpstra; Adrijana Gombosev; Diane Kim; Paul Hannah; Lydia Mikhail; Richard Alexander; Douglas F Moore; Susan S Huang
Journal:  PLoS One       Date:  2013-04-24       Impact factor: 3.240

10.  Modelling the epidemiology of Escherichia coli ST131 and the impact of interventions on the community and healthcare centres.

Authors:  A Talaminos; L López-Cerero; J Calvillo; A Pascual; L M Roa; J Rodríguez-Baño
Journal:  Epidemiol Infect       Date:  2016-02-03       Impact factor: 4.434

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