Nationwide Antibiogram Analysis Using NCCLS M39-A Guidelines

Zapantis, Antonia; Lacy, Melinda K.; Horvat, Rebecca T.; Grauer, Dennis; Barnes, Brian J.; O’Neal, Brian; Couldry, Rick

doi:10.1128/jcm.43.6.2629-2634.2005

Cited by 63 publications

(55 citation statements)

References 20 publications

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“…Respondents reported publishing at least annually (98%), eliminating duplicates (89%), not including surveillance cultures (83%), and including at least 30 isolates for each organism (64%) (10). Studies that assessed compliance by direct inspection of antibiograms showed lower adherence than those that used self-reported compliance (9,11).…”

Section: Discussionmentioning

confidence: 99%

Challenges in Preparation of Cumulative Antibiogram Reports for Community Hospitals

et al. 2015

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e Knowledge of local antimicrobial resistance is critical for management of infectious diseases. Community hospitals' compliance with Clinical and Laboratory Standards Institute (CLSI) guidance for creation of cumulative antibiograms is uncertain. This descriptive cohort study of antibiogram reporting practices included community hospitals enrolled in the Duke Infection Control Outreach Network. Cumulative antibiograms from 2012 were reviewed for criteria on reporting practices and compliance with CLSI guidelines. Microbiology personnel were sent a voluntary, electronic survey on antibiogram preparation practices. Data were compiled using descriptive statistics. Thirty-two of 37 (86%) hospitals provided antibiograms; 26 of 37 (70%) also provided survey responses. Twelve (38%) antibiograms specified methods used for compiling data and exclusion of duplicates. Eight (25%) reported only species with >30 isolates. Of the 24 that did not follow the 30-isolate rule, 3 (13%) included footnotes to indicate impaired statistical validity. Twenty (63%) reported at least 1 pathogen-drug combination not recommended for primary or supplemental testing per CLSI. Thirteen (41%) separately reported methicillin-resistant and -susceptible Staphylococcus aureus. Complete compliance with CLSI guidelines was observed in only 3 (9%) antibiograms. Survey respondents' self-assessment of full or partial compliance with CLSI guidelines was 50% and 15%, respectively; 33% reported uncertainty with CLSI guidelines. Full adherence to CLSI guidelines for hospital antibiograms was uncommon. Uncertainty about CLSI guidelines was common. Alternate strategies, such as regional antibiograms using pooled data and educational outreach efforts, are needed to provide reliable and appropriate susceptibility estimates for community hospitals. F acility-specific cumulative antibiograms serve several important purposes in the care of patients with infectious diseases. For clinicians, knowledge of local drug resistance rates improves the selection of empirical antibiotics prior to return of culture and susceptibility results. In addition, cumulative susceptibility data are used to track changes in resistance over time, perform surveillance for emergence of drug-resistant organisms, and identify areas for intervention by hospital infection prevention and antimicrobial stewardship programs (1). For example, the Centers for Disease Control and Prevention includes two functions of the cumulative antibiogram as "core" elements of hospital antimicrobial stewardship programs: (i) tracking antimicrobial resistance and (ii) regular reporting of information on antibiotic resistance to relevant hospital staff (2). Cumulative antibiogram preparation and distribution are considered an essential function of the clinical microbiology laboratory (3). Antibiogram data can also improve hospital antibiotic formulary decisions and local protocols such as surgical prophylaxis or empirical treatment guidelines.The Clinical and Laboratory Standards Institute (CLSI) first provided...

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Section: Discussionmentioning

confidence: 99%

Challenges in Preparation of Cumulative Antibiogram Reports for Community Hospitals

et al. 2015

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“…Gram positive and gram-negative isolates bacterial sensitivity to commonly used antimicrobials (amoxicillin, cephalothin, gentamicin) were investigated by disk diffusion method using NCCLS guidelines [20,21] Data were entered and analyzed by Stata v. 8. Person identification code was used as primary sampling unit.…”

Section: Methodsmentioning

confidence: 99%

Bacterial Contamination and Resistance to Commonly Used Antimicrobials of Healthcare Workers' Mobile Phones in Teaching Hospitals, Kerman, Iran

Sepehri¹

2009

American Journal of Applied Sciences

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Problem Statement:The contamination rates of Health Care Worker's (HCW) mobile phones and resistance to commonly used antimicrobials were evaluated in three teaching hospitals in Kerman, Iran. So, we examined 150 randomly selected HCWs in three teaching hospitals in Kerman, Iran, 2007. For each HCW a sterile swab moistened with sterile water was rotated over the surface of both sides of his/her phone, a second swab for the sampling of the dominant hand. Both swabs were cultured by the routine methodology in use at laboratory. Plates were incubated aerobically at 37°C for 48 h. Approach: All samples were examined for the antimicrobial activity for commonly used antimicrobials using disc diffusion method. Results: A total of 48 (32.0%, CI95 24.6-40.1%) mobile phones and 59 (39.3%, CI95 31.5-47.6%) of dominant hands had bacterial contamination and Staphylococcus epidermidis was the most commonly cultured organisms from all sites. The resistance rates to commonly used antimicrobials in isolated bacteria from phones and dominant hand varied from 6.7% for cephalothin to 25% for amoxicillin, respectively. Conclusions/Recommendations: The kind of isolated microorganisms and their susceptibility to commonly used antimicrobials from dominant hands were almost similar with those from phones (p<0.05). In conclusion, the results indicated that the rate of bacterial contamination of the HCW's phone is just below 50%, accompanying with a resistance rate to the common used antibiotics in one fourth of all the cases. Therefore, mobile phones could be an important source of nosocomial infections and the spread of bacterial resistance bacteria in medical healthcare settings.

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“…It is important to emphasize that the increasing challenge and complexity of antimicrobial susceptibility testing, analysis and reporting requires the interdisciplinary collaboration and efforts of microbiologists, epidemiologists, clinicians and pharmacists in order to generate and communicate the most accurate and useful susceptibility data possible [1,4,5] . The RDT model developed here is an example of this type of interdisciplinary approach and one that we believe provides a relatively straightforward yet dynamic therapeutic guide and therapeutic decision-making learning tool.…”

Section: Resultsmentioning

confidence: 99%

“…The RDT model discussed here may also serve as a more effective tool to understand the prevalence of different resistant phenotypes in a given institution compared to the traditional ABGM. Lastly, the RDT model developed here represents an interdisciplinary collaboration between microbiologists, pharmacists and infection control practitioners as it relates to the complex problem of antimicrobial resistance, an approach that is currently recommended by the infectious disease and infection control community [4,5] .…”

Section: Introductionmentioning

confidence: 99%

Antibiogram-Derived Radial Decision Trees: An Innovative Approach to Susceptibility Data Display

Perla¹,

Belliveau²

2005

American J. of Infectious Diseases

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Hospital antibiograms (ABGMs) are often presented in the form of large 2-factor (single organism vs. single antimicrobial) tables. Presenting susceptibility data in this fashion, although of value, does have limitations relative to drug resistant subpopulations. As the crisis of antimicrobial drug-resistance continues to escalate globally, clinicians need (1) to have access to susceptibility data that, for isolates resistant to first-line drugs, indicates susceptibility to second line drugs and (2) to understand the probabilities of encountering such organisms in a particular institution. This article describes a strategy used to transform data in a hospital ABGM into a probability-based radial decision tree (RDT) that can be used as a guide to empiric antimicrobial therapy. Presenting ABGM data in the form of a radial decision tree versus a table makes it easier to visually organize complex data and to demonstrate different levels of therapeutic decision-making. The RDT model discussed here may also serve as a more effective tool to understand the prevalence of different resistant subpopulations in a given institution compared to the traditional ABGM.

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Nationwide Antibiogram Analysis Using NCCLS M39-A Guidelines

Cited by 63 publications

References 20 publications

Challenges in Preparation of Cumulative Antibiogram Reports for Community Hospitals

Challenges in Preparation of Cumulative Antibiogram Reports for Community Hospitals

Bacterial Contamination and Resistance to Commonly Used Antimicrobials of Healthcare Workers' Mobile Phones in Teaching Hospitals, Kerman, Iran

Antibiogram-Derived Radial Decision Trees: An Innovative Approach to Susceptibility Data Display

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