Literature DB >> 22877228

Evaluating the efficacy of antimicrobial cycling programmes and patient isolation on dual resistance in hospitals.

Karen Chow1, Xiaohong Wang, R Curtiss, Carlos Castillo-Chavez.   

Abstract

Antibiotic-resistant bacteria cause a number of infections in hospitals and are considered a threat to public health. A strategy suggested to curb the development of resistant hospital-acquired infections is antimicrobial cycling, in which antibiotic classes are alternated over time. This can be compared with a mixing programme in which, when given two drugs, half of the physicians prescribe one drug over the other. A mathematical model of antimicrobial cycling in a hospital population setting is developed to evaluate the efficacy of a cycling programme with an emphasis on reducing the emergence and significance of dual resistance. The model also considers the effects of physician compliance and isolating patients harbouring dual-resistant bacteria. Simulation results show that the optimal antimicrobial drug usage programme in hospital populations depends upon the type of resistance being targeted for treatment; a cycling programme is more effective against dual resistance compared with mixing. Patient isolation and high compliance to a cycling programme is also shown to dramatically decrease dual resistance in hospitalized populations. Ultimately, the exclusive use of antimicrobials in fighting nosocomial infection does not solve the problem but just slows down what appears to be a losing battle against drug resistance. We hope that this paper serves to instigate discussion on the many dimensions of the complex problem of drug resistance in hospital settings.

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Year:  2011        PMID: 22877228     DOI: 10.1080/17513758.2010.488300

Source DB:  PubMed          Journal:  J Biol Dyn        ISSN: 1751-3758            Impact factor:   2.179


  12 in total

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2.  Bifurcation analysis and global dynamics of a mathematical model of antibiotic resistance in hospitals.

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

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Review 4.  Modelling the transmission of healthcare associated infections: a systematic review.

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5.  Homogeneity of antimicrobial policy, yet heterogeneity of antimicrobial resistance: antimicrobial non-susceptibility among 108,717 clinical isolates from primary, secondary and tertiary care patients in London.

Authors:  Luke S P Moore; Rachel Freeman; Mark J Gilchrist; Myriam Gharbi; Eimear T Brannigan; Hugo Donaldson; David M Livermore; Alison H Holmes
Journal:  J Antimicrob Chemother       Date:  2014-08-12       Impact factor: 5.790

6.  Evaluation of a Mixing versus a Cycling Strategy of Antibiotic Use in Critically-Ill Medical Patients: Impact on Acquisition of Resistant Microorganisms and Clinical Outcomes.

Authors:  Nazaret Cobos-Trigueros; Mar Solé; Pedro Castro; Jorge Luis Torres; Mariano Rinaudo; Elisa De Lazzari; Laura Morata; Cristina Hernández; Sara Fernández; Alex Soriano; José María Nicolás; Josep Mensa; Jordi Vila; José Antonio Martínez
Journal:  PLoS One       Date:  2016-03-16       Impact factor: 3.240

Review 7.  Effectiveness of contact precautions against multidrug-resistant organism transmission in acute care: a systematic review of the literature.

Authors:  C C Cohen; B Cohen; J Shang
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8.  Resource competition may lead to effective treatment of antibiotic resistant infections.

Authors:  Antonio L C Gomes; James E Galagan; Daniel Segrè
Journal:  PLoS One       Date:  2013-12-13       Impact factor: 3.240

9.  Cycling empirical antibiotic therapy in hospitals: meta-analysis and models.

Authors:  Pia Abel zur Wiesch; Roger Kouyos; Sören Abel; Wolfgang Viechtbauer; Sebastian Bonhoeffer
Journal:  PLoS Pathog       Date:  2014-06-26       Impact factor: 6.823

10.  Antibiotic rotation strategies to reduce antimicrobial resistance in Gram-negative bacteria in European intensive care units: study protocol for a cluster-randomized crossover controlled trial.

Authors:  Pleun J van Duijn; Marc J M Bonten
Journal:  Trials       Date:  2014-07-10       Impact factor: 2.279

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