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Antimicrob Agents Chemother. Time series analysis as a tool to predict the impact of antimicrobial restriction in antibiotic stewardship programs using the example of multidrug-resistant Pseudomonas aeruginosa
[Source: Antimicrobial Agents and Chemotherapy, full text: (LINK). Abstract, edited.]
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Time series analysis as a tool to predict the impact of antimicrobial restriction in antibiotic stewardship programs using the example of multidrug-resistant Pseudomonas aeruginosa
Matthias Willmann 1,#, Matthias Marschal 1, Florian H?lzl 1, Klaus Schr?ppel 1, Ingo B Autenrieth 1 and Silke Peter 1
Author Affiliations: <SUP>1</SUP>Institute of Medical Microbiology and Hygiene, University of T?bingen, T?bingen, Germany
ABSTRACT
The association between antimicrobial consumption and resistance in non-fermentative gram-negative bacteria is well known. Antimicrobial restriction, implemented in clinical routine by antibiotic stewardship programs (ASPs), is considered as a mean to reduce resistance rates. Whether and how antimicrobial restriction can accomplish this goal is still unknown though. This leads to an element of uncertainty when designing strategies for ASPs. From January 2002 until December 2011 an observational study was performed at the University Hospital T?bingen, Germany to investigate the association between antimicrobial use and resistance rates in Pseudomonas aeruginosa. Transfer function models were used to determine such associations and to simulate antimicrobial restriction strategies. Various positive associations between antimicrobial consumption and resistance were observed in our setting. Surprisingly, impact estimations of different antimicrobial restrictions strategies revealed relatively low intervention expenses to effectively attenuate the observed increase in resistance. For example, a simulated intervention of an annual 4% reduction in the use of meropenem over 3 years from 2009 until 2011 yielded a 62.5% attenuation (95% confidence interval: 15% - 110%) in the rising trend of multidrug-resistant Pseudomonas aeruginosa (34MRGN-PA). Time series analysis models derived from past data may be a tool to predict the outcome of antimicrobial restriction strategies, and could be used to design ASPs.
FOOTNOTES
#Corresponding author. Contact details of corresponding author: Matthias Willmann, MD, MSc, DTM&H, Institute of Medical Microbiology and Hygiene, Elfriede-Aulhorn-Str. 6, 72076, T?bingen Germany. will80@gmx.de
Copyright ? 2013, American Society for Microbiology. All Rights Reserved.
-Matthias Willmann 1,#, Matthias Marschal 1, Florian H?lzl 1, Klaus Schr?ppel 1, Ingo B Autenrieth 1 and Silke Peter 1
Author Affiliations: <SUP>1</SUP>Institute of Medical Microbiology and Hygiene, University of T?bingen, T?bingen, Germany
ABSTRACT
The association between antimicrobial consumption and resistance in non-fermentative gram-negative bacteria is well known. Antimicrobial restriction, implemented in clinical routine by antibiotic stewardship programs (ASPs), is considered as a mean to reduce resistance rates. Whether and how antimicrobial restriction can accomplish this goal is still unknown though. This leads to an element of uncertainty when designing strategies for ASPs. From January 2002 until December 2011 an observational study was performed at the University Hospital T?bingen, Germany to investigate the association between antimicrobial use and resistance rates in Pseudomonas aeruginosa. Transfer function models were used to determine such associations and to simulate antimicrobial restriction strategies. Various positive associations between antimicrobial consumption and resistance were observed in our setting. Surprisingly, impact estimations of different antimicrobial restrictions strategies revealed relatively low intervention expenses to effectively attenuate the observed increase in resistance. For example, a simulated intervention of an annual 4% reduction in the use of meropenem over 3 years from 2009 until 2011 yielded a 62.5% attenuation (95% confidence interval: 15% - 110%) in the rising trend of multidrug-resistant Pseudomonas aeruginosa (34MRGN-PA). Time series analysis models derived from past data may be a tool to predict the outcome of antimicrobial restriction strategies, and could be used to design ASPs.
FOOTNOTES
#Corresponding author. Contact details of corresponding author: Matthias Willmann, MD, MSc, DTM&H, Institute of Medical Microbiology and Hygiene, Elfriede-Aulhorn-Str. 6, 72076, T?bingen Germany. will80@gmx.de
Copyright ? 2013, American Society for Microbiology. All Rights Reserved.
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