ViResiST: its contribution to our knowledge of the relationship between antimicrobial use and resistance Dominique L. Monnet
About antibiotics... As soon as we use it, we loose it The more we use it, the more we loose it
The Antimicrobial Resistance Spiral ANTIMICROBIAL RESISTANCE 4 - Cross-transmission 1 - Concern 3 - Selection New antimicrobials (promotion) 2 - Broad-spectrum empiric therapy Antimicrobial resistance Dose Duration
Genetic Diversity and Adaptation 2010 Tautavel (France), approx. 450,000 years ago approx. 15,000 generations humans approx. 10 10 generations of 10 9 E. coli Adapted from: Geberding JL, CDC/NCID/HIP, 1999. Picture from: URL: http://www.culture.fr/culture/arcnat/tautavel/en/homme-fr.htm
Illustration: Prittie EJ. Philadeplphia, PA: JC Winston, 1930.
The Antimicrobial Resistance Spiral ANTIMICROBIAL RESISTANCE 1 - Concern New antimicrobials (promotion)? 4 - Cross-transmission 3 - Selection 2 - Broad-spectrum empiric therapy Antimicrobial resistance Dose Duration
Study designs for the relationship between antimicrobial use and resistance
Pharyngeal Carriage of Macrolide-Resistant Streptococci Following Exposure to Azithromycin and Clarithromycin Source: Malhotra-Kumar S, et al. Lancet 2007;369:482-490.
Cumulative Hazard Estimates for Emergence of Fluoroquinolone Resistance Following Fluoroquinolone Exposure 0.20 Probability of resistance 0.15 0.10 0.05 0.00 Fluoroquinone exposed No fluoroquinolone 0 20 40 60 80 Days Source: Harbarth S, et al. Clin Infect Dis 2001;33:1462-1468.
Fluoroquinolone Use and Ciprofloxacin-Resistant P. aeruginosa, SCOPE-MMIT Hospitals, 1999-2000 % Ciprofloxacin-Resistant Pseudomonas aeruginosa (%) 50 40 30 20 10 0 y = 0.0832x + 18.461 R 2 = 0.29 p = 0.01 0 50 100 150 200 250 Total Fluoroquinolone Use (Daily Doses per 1,000 patient-days) Source: Polk RE, et al. 41st ICAAC, Chicago (IL), 2001, late-breaker abstr. UL-1.
Retrospective Information to Guide Empiric Prescription of Antimicrobials Source: Snyder JW, Beam L. University of Louisville Hospital, Louisville (Kentucky), 1994.
Before ViResiST Bzzzz...
5-Month Moving Average Percent Imipenem- Resistant/Intermediate P. aeruginosa and Hospital Imipenem Use, Hospital Vega Baja, Spain, 1991-2002 Average delay = 1 month % Imipenem-resistant/intermediate Pseudomonas aeruginosa 25 20 15 10 5 0 Jan-91 Jul-91 Jan-92 1 DDD/1,000 pat-days +0.40 %R Jul-92 Jan-93 Jul-93 Jan-94 Jul-94 Jan-95 Jul-95 Jan-96 Jul-96 Jan-97 Jul-97 Jan-98 Jul-98 30 25 20 15 10 5 0 Hospital imipenem use (DDD/1,000 patient-days) Updated from: López-Lozano JM, et al. Int J Antimicrob Agents 2000;14:21-30. ViResiST
Applications of ViResiST in other hospitals
%Carbapenem-Resistant Pseudomonas aeruginosa and Carbapenem Use in 4 Hospitals, 1996-2003 Univ. Hospital, Ulm (D) Lepper et al. AAC 2002;46:2920-5. Univ. Hospital, Utah (USA) Samore MH, et al. Unpublished data. Average delay = 0-1 month Carbapenem-resistant P.aeruginosa(%) 40 30 20 10 0 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 2 1.5 1 0.5 0 Carbapenem use (DDD/100 pt-days) Average delay = 0-1 month Univ. Hospital, Antwerp (B) Goossens H, et al. Unpublished data. Centre Hosp. Mulhouse (F) Aujoulat O, Delarbre JM. ViResiST. Carbapenem-resistant P.aeruginosa(%) 40 30 20 10 0 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 5 4 3 2 1 0 Carbapenem use (DDD/100 pt-days) Average delay = 0-2 months Imipenem-resistant P.aeruginosa (%) 40 30 20 10 0 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 0.5 0.4 0.3 0.2 0.1 0 Carbapenem use (DDD/100 pt-days) Average delay = n.a.
%MRSA and Monthly Use of Macrolides, Third-Generation Cephalosporins and Fluoroquinolones, Aberdeen Royal Infirmary, 01/1996-12/2001 Explaining variable for monthly %MRSA Lag (months) Estimated coefficient %MRSA 1 0.420 Macrolide use 1,2,3 0.165 Third-generation cephalosporin use 4,5,6,7 0.290 Fluoroquinolone use 4,5 0.255 Constant - - 36.7 R 2 =0.902 Source: Monnet DL, et al. Emerg Infect Dis 2004;10:1432-1441.
Multivariate time series models to explain hospital MRSA University of Geneva Hospitals, 2000-2006 Antrim Area Hospital, Northern Ireland, 2000-2004 Source: Vernaz N, et al. JAC 2008;62:601-607; Aldeyab MA, et al. JAC 2008;62:593-600.
What did ViResiST achieve? Confirmation of the relationship between antimicrobial use and resistance with new methodology using routine laboratory and pharmacy data Collection of longitudinal, electronic data from several hospitals in Europe Confirm the models with data from these European hospitals (generalisation) Acceptance of the methodology Transition from research to routine practice Use models in real time to guide prescriptions in order to beat resistance before it increases
With ViResiST! Beat the Bug B... B... Before B... B... it Bites
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