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Limiting the Emergence of Extended-Spectrum β–Lactamase-Producing Enterobacteriaceae: Influence of Patient Population Characteristics on the Response to Antimicrobial Formulary Interventions

Published online by Cambridge University Press:  21 June 2016

Adam D. Lipworth
Affiliation:
Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia Center for Research and Education on Therapeutics, University of Pennsylvania School of Medicine, Philadelphia
Emily P. Hyle
Affiliation:
Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia Center for Research and Education on Therapeutics, University of Pennsylvania School of Medicine, Philadelphia
Neil O. Fishman
Affiliation:
Division of Infectious Diseases, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia Center for Research and Education on Therapeutics, University of Pennsylvania School of Medicine, Philadelphia
Irving Nachamkin
Affiliation:
Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia Center for Research and Education on Therapeutics, University of Pennsylvania School of Medicine, Philadelphia
Warren B. Bilker
Affiliation:
Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia Center for Research and Education on Therapeutics, University of Pennsylvania School of Medicine, Philadelphia
Ann Marie Marr
Affiliation:
Department of Pharmacy, University of Pennsylvania School of Medicine, Philadelphia
Lori A. Larosa
Affiliation:
Department of Pharmacy, University of Pennsylvania School of Medicine, Philadelphia
Nishaminy Kasbekar
Affiliation:
Department of Pharmacy, University of Pennsylvania School of Medicine, Philadelphia
Ebbing Lautenbach*
Affiliation:
Division of Infectious Diseases, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia Center for Research and Education on Therapeutics, University of Pennsylvania School of Medicine, Philadelphia
*
University of Pennsylvania School of Medicine, Center for Clinical Epidemiology and Biostatistics, 825 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021 (elautenb@cceb.med.upenn.edu)

Abstract

Background.

Effective methods to control the emergence of extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella species (ESBL-EK) remain unclear. Variations in the patient populations at different hospitals may influence the effect of antimicrobial formulary interventions.

Methods.

To examine variations across hospitals in the response to antimicrobial interventions (ie, restriction of ceftazidime and ceftriaxone) designed to curb the spread of ESBL-EK, we conducted a 5-year quasi-experimental study. This study was conducted at 2 hospitals within the same health system: Hospital A is a 625-bed academic medical center, and Hospital B is a 344-bed urban community hospital. All adult patients with a healthcare-acquired clinical culture of ESBL-EK from July 1, 1997 through December 31, 2002 were included.

Results.

After the interventions, the use of ceftriaxone decreased by 86% at Hospital A and by 95% at Hospital B, whereas the use of ceftazidime decreased by 95% at Hospital A and by 97% at Hospital B. The prevalence of ESBL-EK at Hospital A decreased by 45% (P< .001), compared with a 22% decrease at Hospital B (P = .36). The following variables were significantly more common among ESBL-EK-infected patients at Hospital B: residence in a long-term care facility (adjusted odds ratio, 3.77 [95% confidence interval, 1.70-8.37]), advanced age (adjusted odds ratio, 1.04 [95% confidence interval, 1.01-1.06]), and presence of a decubitus ulcer (adjusted odds ratio, 4.13 [95% confidence interval, 1.97-8.65]).

Conclusions.

The effect of antimicrobial formulary interventions intended to curb emergence of ESBL-EK may differ substantially across institutions, perhaps as a result of differences in patient populations. Variability in the epidemiological profiles of ESBL-EK isolates at different hospitals must be considered when designing interventions to respond to these pathogens.

Type
Original Articles
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2006

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References

1.Itokazu, GS, Quinn, JP, Bell-Dixon, C, Kahan, FM, Weinstein, RA. Antimicrobial resistance rates among aerobic gram-negative bacilli recovered from patients in intensive care units: evaluation of a national postmarketing surveillance program. Clin Infect Dis 1996; 23:779784.Google Scholar
2.Burwen, DR, Banerjee, SN, Gaynes, RP. Ceftazidime resistance among selected nosocomial gram-negative bacilli in the United States. National Nosocomial Infections Surveillance System. J Infect Dis 1994; 170:16221625.Google Scholar
3.Bisson, G, Fishman, NO, Patel, JB, Edelstein, PH, Lautenbach, E. Extended-spectrum β-lactamase-producing Escherichia coli and Klebsiella species: risk factors for colonization and impact of antimicrobial formulary interventions on colonization prevalence. Infect Control Hosp Epidemiol 2002; 23:254260.Google Scholar
4.Lautenbach, E, Patel, JB, Bilker, WB, Edelstein, PH, Fishman, NO. Extended-spectrum β-lactamase-producing Escherischia coli and Klebsiella pneumoniae: risk factors for infection and impact of resistance on outcomes. Clin Infect Dis 2001; 32:11621171.Google Scholar
5.Jacoby, GA. Extended-spectrum beta-lactamases and other enzymes providing resistance to oxyimino-beta-lactams. Infect Dis Clin North Am 1997; 11:875887.CrossRefGoogle ScholarPubMed
6.Paterson, DL, Ko, WC, Von Gottberg, A, et al. International prospective study of Klebsiella pneumoniae bacteremia: implications of extended-spectrum beta-lactamase production in nosocomial infections. Ann Intern Med 2004; 140:2632.Google Scholar
7.Meyer, KS, Urban, C, Eagan, JA, Berger, BJ, Rahal, JJ. Nosocomial outbreak of Klebsiella infection resistant to late-generation cephalosporins. Ann Intern Med 1993; 119:353358.Google Scholar
8.Jacobson, KL, Cohen, SH, Inciardi, JF, et al. The relationship between antecedent antibiotic use and resistance to extended-spectrum cephalosporins in group I (3-lactamase-producing organisms. Clin Infect Dis 1995;21:11071113.Google Scholar
9.Paterson, DL, Ko, WC, Gottberg, AV, et al. International prospective study of Klebsiella pneumoniae bacteremia: implications of extended-spectrum β-lactamase production in nosocomial infections. Ann Intern Med 2004; 140:2632.Google Scholar
10.Patterson, JE, Hardin, TC, Kelly, CA, Garcia, RC, Jorgensen, JH. Association of antibiotic utilization measures and control of multiple-drug resistance in Klebsiella pneumonia. Infect Control Hosp Epidemiol 2000; 21:455458.Google Scholar
11.Rice, LB, Eckstein, EC, DeVente, J, Shlaes, DM. Ceftazidime-resistant Klebsiella pneumoniae isolates recovered at the Cleveland Department of Veterans Affairs Medical Center. Clin Infect Dis 1996; 23:118124.Google Scholar
12.Rahal, JJ, Urban, C, Horn, D, et al. Class restriction of cephalosporin use to control total cephalosporin resistance in nosocomial Klebsiella. JAMA 1998; 280:12331237.Google Scholar
13.Go, E, Urban, C, Burns, J, et al. Clinical and molecular epidemiology of Acinetobacter infections sensitive only to polymyxin B and sulbactam. Lancet 1994; 344:13291332.Google Scholar
14.Garner, JS, Jarvis, WR, Emori, TG, Horan, TC, Hughes, JM. CDC definitions for nosocomial infections. Am J Infect Control 1988; 16:128140.CrossRefGoogle ScholarPubMed
15.Harris, AD, Bradham, DD, Baumgarten, M, Zuckerman, IH, Fink, JC, Perencevich, EN. The use and interpretation of quasi-experimental studies in infectious diseases. Clin Infect Dis 2004; 38:15861591.Google Scholar
16.Gross, R, Morgan, AS, Kinky, DE, Weiner, M, Gibson, GA, Fishman, N. Impact of a hospital-based antimicrobial management program on clinical and economic outcomes. Clin Infect Dis 2001; 33:289295.Google Scholar
17.Muto, CA, Jernigan, JA, Ostrowsky, BE, et al. SHEA guideline for preventing nosocomial transmission of multidrug-resistant strains of Staphylococcus aureus and enterococcus. Infect Control Hosp Epidemiol 2003; 24:362386.Google Scholar
18.Knaus, WA, Drapier, EA, Wagner, DP, Zimmerman, JE. APACHE II: a severity of disease classification system. Crit Care Med 1985; 13:818829.Google Scholar
19.National Committee for Clinical Laboratory Standards (NCCLS). Methods for dilution antimicrobial susceptibility tests for bacteria that grow anaerobically: approved standard. Wayne, PA: NCCLS; 2002.Google Scholar
20.National Committee for Clinical Laboratory Standards (NCCLS). Performance standards for antimicrobial susceptibility testing: informational supplement. Wayne, PA: NCCLS; 2002.Google Scholar
21.National Committee for Clinical Laboratory Standards (NCCLS). Performance standards for antimicrobial disk susceptibility testing: approved standard. Wayne, PA: NCCLS; 2002.Google Scholar
22.Armitage, P. Test for linear trend in proportions and frequencies. Biometrics 1955; 11:375386.Google Scholar
23.Kleinbaum, DG, Kupper, LL, Morgenstern, H. Epidemiologic Research: Principles and Quantitative Methods. New York: Van Nostrand Reinhold; 1982.Google Scholar
24.Hosmer, DW, Lemeshow, S. Applied Logistic Regression. New York: John Wiley & Sons; 1989.Google Scholar
25.Sun, J. A non-parametric test for interval-censored failure time data with application to AIDS studies. Stat Med 1996; 15:13871395.Google Scholar
26.Robins, JM, Greenland, S. The role of model selection in causal inference from nonexperimental data. Am J Epidemiol 1986; 123:392402.Google Scholar
27.Maldonado, G, Greenland, S. Simulation study of confounder-selection strategies. Am J Epidemiol 1993; 138:923936.Google Scholar
28.Greenland, S. Modeling and variable selection in epidemiologic analysis. Am J Public Health 1989; 79:340349.Google Scholar
29.Paterson, DL. Extended-spectrum β-lactamases: the European experience. Curr Opin Infect Dis 2001; 14:697701.Google Scholar