title: 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.
abstract: The association between antimicrobial consumption and resistance in nonfermentative Gram-negative bacteria is well-known. Antimicrobial restriction, implemented in clinical routines by antibiotic stewardship programs (ASPs), is considered a means 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 Tübingen, Tübingen, 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 restriction 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% to 110%) in the rising trend of multidrug-resistant Pseudomonas aeruginosa (three- and four-class-resistant P. 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.
authors: Institute of Medical Microbiology and Hygiene, University of Tübingen, Tübingen, Germany. will80@gmx.de
authors: Matthias
authors: M
authors: Willmann
authors: Matthias
authors: M
authors: Marschal
authors: Florian
authors: F
authors: Hölzl
authors: Klaus
authors: K
authors: Schröppel
authors: Ingo B
authors: IB
authors: Autenrieth
authors: Silke
authors: S
authors: Peter
mesh_terms: D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D024901:Drug Resistance, Multiple, Bacterial; D008826:Microbial Sensitivity Tests; D011550:Pseudomonas aeruginosa / Q000187:drug effects*
chemical_list: D000900:Anti-Bacterial Agents
references: Clin Infect Dis. 2000 Jul;31(1):16-23
references: 10913390
references: Diagn Microbiol Infect Dis. 2011 Jul;70(3):367-72
references: 21683268
references: Lancet Infect Dis. 2011 May;11(5):381-93
references: 21530894
references: Int J Antimicrob Agents. 2000 Feb;14(1):21-31
references: 10717497
references: J Antimicrob Chemother. 2011 Jul;66(7):1625-30
references: 21586594
references: J Hosp Infect. 2009 Dec;73(4):338-44
references: 19699552
references: J Hosp Infect. 2010 May;75(1):23-7
references: 20338669
references: Antimicrob Agents Chemother. 2002 Mar;46(3):854-8
references: 11850272
references: Am J Trop Med Hyg. 2012 Aug;87(2):364-70
references: 22855772
references: Clin Infect Dis. 2007 Sep 1;45 Suppl 2:S112-21
references: 17683015
references: PLoS One. 2010 Mar 01;5(3):e9450
references: 20209164
references: Intensive Care Med. 2006 Sep;32(9):1384-91
references: 16788807
references: J Antimicrob Chemother. 2006 Nov;58(5):1082-5
references: 16950822
references: Clin Microbiol Infect. 2012 Mar;18(3):268-81
references: 21793988
references: JAMA. 1998 Oct 14;280(14):1233-7
references: 9786372
references: Antimicrob Agents Chemother. 2006 Jun;50(6):2192-6
references: 16723582
references: Infect Control Hosp Epidemiol. 2006 Mar;27(3):279-86
references: 16532416
references: Antimicrob Agents Chemother. 2004 Jan;48(1):224-8
references: 14693543
references: Antimicrob Agents Chemother. 2011 Dec;55(12):5597-601
references: 21968357
references: Antimicrob Agents Chemother. 2010 Mar;54(3):1173-8
references: 20065055
references: Infect Control Hosp Epidemiol. 2008 Jul;29(7):615-22
references: 18624668