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

Lipworth, Adam D.; Hyle, Emily P; Fishman, Neil O.; Nachamkin, Irving; Bilker, Warren B.; Marr, Ann Marie; Larosa, Lori A.; Kasbekar, Nishaminy; Lautenbach, Ebbing

doi:10.1086/503016

Cited by 42 publications

(21 citation statements)

References 24 publications

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“…A quasiexperimental design that attempts to control for confounding is the one-group pretest-posttest design with a nonequivalent dependent variable (154,155). For example, Gottesman et al observed a decline in fluoroquinolone-resistant E. coli in urine cultures during a community intervention in which preapproval was required to prescribe ciprofloxacin (155).…”

Section: Nonrandomized Intervention (Quasiexperimental or Beforeaftermentioning

confidence: 99%

Epidemiological Interpretation of Studies Examining the Effect of Antibiotic Usage on Resistance

et al. 2013

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SUMMARY Bacterial resistance to antibiotics is a growing clinical problem and public health threat. Antibiotic use is a known risk factor for the emergence of antibiotic resistance, but demonstrating the causal link between antibiotic use and resistance is challenging. This review describes different study designs for assessing the association between antibiotic use and resistance and discusses strengths and limitations of each. Approaches to measuring antibiotic use and antibiotic resistance are presented. Important methodological issues such as confounding, establishing temporality, and control group selection are examined.

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Section: Nonrandomized Intervention (Quasiexperimental or Beforeaftermentioning

confidence: 99%

Epidemiological Interpretation of Studies Examining the Effect of Antibiotic Usage on Resistance

et al. 2013

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“…Surprisingly, this amount of meropenem reduction needed for an effective impact on 34MRGN-PA incidence is relatively small. In two reports, implementation of ASPs resulted in a 54% to 97% decrease of the targeted antimicrobial agents over a study period of 3 to 5 years (26,27). According to our results, such intensive restriction may not be necessary in all cases, especially as countereffects could be a consequence.…”

Section: Discussionmentioning

confidence: 50%

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

Willmann

Marschal

Hölzl

et al. 2013

Antimicrob Agents Chemother

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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-classresistant 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.

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“…In previous research, a decrease in resistant pathogens, such as Clostridium difficile [18], extended-spectrum β-lactamase-producing E. coli and K. pneumonia [19], and methicillin-resistant Staphylococcus aureus (MRSA) [21] was noted. Decreases in hospital stays [22] and improvements in successful treatments [20] have also been reported.…”

Section: Discussionmentioning

confidence: 95%

“…Prospective antimicrobial audits and feedback systems have also shown benefits such as cost-effectiveness [17], reductions in the inappropriate antibiotic prescriptions [10], decreases in antimicrobial consumption [18][19] and "bug-drug" mismatch [20].…”

Section: Discussionmentioning

confidence: 99%

Prospective antimicrobial audit and feedback did not decrease case fatality: Experiences from a hospital in northern Taiwan

Cheng

Lee

et al. 2016

J Infect Dev Ctries

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Introduction: Although a prospective antimicrobial audit and feedback is an effective strategy in an antibiotic stewardship program, previous researchers have not adequately demonstrated a successful impact on patient outcomes. In this study, the causes of fatalities associated with a prospective antimicrobial audit and feedback were analyzed. Methodology: Between June and September 2014, applications for 16 target parenteral formulas (including ceftriaxone, ceftazidime, cefepime, piperacillin/tazobactam, vancomycin, teicoplanin, ertapenem, imipenem/cilastatin, meropenem, levofloxacin, moxifloxacin, ciprofloxacin, tigecycline, linezolid, daptomycin, and amikacin), which were not approved by infectious diseases (ID) specialists, were followed up until patients were either discharged or passed away. Results: Of the 292 cases studied, 193 (66%) were male, with a mean age (standard deviation) of 65.5 (19.3) years. There were five reasons for rejection, including dosage adjustments (37%), no evidence of bacterial infection (28.8%), modifications according to antimicrobial susceptibility (18.8%), target pathogens not being covered (7.2%), and redundant therapy (4.1%). Multiple logistic regression analysis demonstrated that an age greater than 75 years (odds ratio [OR]: 2.58; 95% confidence interval [CI]: 1.32-5.50; p = 0.005) was associated with significant mortality, while urinary tract (OR: 0.26; 95% CI: 0.09-0.70; p = 0.013) and soft tissue/bone infections (OR: 0.18; 95% CI: 0.05-0.61; p = 0.006) were associated with survival. Adjustments according to ID physicians' recommendations were not statistically significant (OR: 0.53; 95% CI: 0.27-1.06; p = 0.074). Conclusions: Antimicrobial adjustments according to ID physicians' recommendations showed only marginally preventative effects against fatalities.

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

Cited by 42 publications

References 24 publications

Epidemiological Interpretation of Studies Examining the Effect of Antibiotic Usage on Resistance

Epidemiological Interpretation of Studies Examining the Effect of Antibiotic Usage on Resistance

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

Prospective antimicrobial audit and feedback did not decrease case fatality: Experiences from a hospital in northern Taiwan

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