Development of a urinary-specific antibiogram for gram-negative isolates: Impact of patient risk factors on susceptibility

Rabs, Nicole; Wieczorkiewicz, Sarah M.; Costello, Michael; Zamfirova, Ina

doi:10.1016/j.ajic.2013.11.004

Cited by 13 publications

(9 citation statements)

References 21 publications

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“…To our knowledge, only few previous studies compared resistance rates in community-acquired vs. hospital-acquired isolates, each showing most pronounced differences for Pseudomonas aeruginosa with up to 16 percentage points higher resistance rates in hospital-acquired isolates [ 10 , 23 , 24 ]. Smaller differences of up to 9 percentage points were observed for Escherichia coli and Staphylococcus aureus , and previous antimicrobial treatment and hospitalization within the last 30 days were identified as further risk factors for higher resistance rates.…”

Section: Resultsmentioning

confidence: 99%

“…Further unit-specific analyses have been provided with respect to pediatric patients and elderly people. For pediatric units, researchers observed mainly lower resistance rates for a number of species/antibiotic combinations in comparison to the hospital-wide antibiogram [ 23 , 24 , 27 , 29 , 33 ]. Results were most pronounced for fluoroquinolones with 10 to 25 percentage points lower resistance rates in patients ≤18 years.…”

Section: Resultsmentioning

confidence: 99%

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Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data – The Influence of Different Parameters in a Routine Clinical Microbiology Laboratory

Kohlmann

Gatermann

2016

PLoS ONE

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IntroductionMany clinical microbiology laboratories report on cumulative antimicrobial susceptibility testing (cAST) data on a regular basis. Criteria for generation of cAST reports, however, are often obscure and inconsistent. Whereas the CLSI has published a guideline for analysis and presentation of cAST data, national guidelines directed at clinical microbiology laboratories are not available in Europe. Thus, we sought to describe the influence of different parameters in the process of cAST data analysis in the setting of a German routine clinical microbiology laboratory during 2 consecutive years.Material and MethodsWe developed various program scripts to assess the consequences ensuing from different algorithms for calculation of cumulative antibiograms from the data collected in our clinical microbiology laboratory in 2013 and 2014.ResultsOne of the most pronounced effects was caused by exclusion of screening cultures for multi-drug resistant organisms which decreased the MRSA rate in some cases to one third. Dependent on the handling of duplicate isolates, i.e. isolates of the same species recovered from successive cultures on the same patient during the time period analyzed, we recorded differences in resistance rates of up to 5 percentage points for S. aureus, E. coli and K. pneumoniae and up to 10 percentage points for P. aeruginosa. Stratification by site of care and specimen type, testing of antimicrobials selectively on resistant isolates, change of interpretation rules and analysis at genus level instead of species level resulted in further changes of calculated antimicrobial resistance rates.ConclusionThe choice of parameters for cAST data analysis may have a substantial influence on calculated antimicrobial resistance rates. Consequently, comparability of cAST reports from different clinical microbiology laboratories may be limited. We suggest that laboratories communicate the strategy used for cAST data analysis as long as national guidelines for standardized cAST data analysis and reporting do not exist in Europe.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data – The Influence of Different Parameters in a Routine Clinical Microbiology Laboratory

Kohlmann

Gatermann

2016

PLoS ONE

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“…Using AST data from single patient isolates recovered from different sources would enable search parameters to include the selection of both specimen type [55][56][57][58][59][60] and specific organisms [59][60][61] , thereby providing more realistic algorithms to guide empiric treatment. Antimicrobial prescription and institutional antibiograms differ between various institutions, sometimes even within an individual health-care institution 60,62,63 , and between inpatient and outpatient populations 60,[63][64][65] .…”

Section: The Institutional Antibiogrammentioning

confidence: 99%

Developmental roadmap for antimicrobial susceptibility testing systems

et al. 2018

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Antimicrobial susceptibility testing (AST) technologies help to accelerate the initiation of targeted antimicrobial therapy for patients with infections and could potentially extend the lifespan of current narrow-spectrum antimicrobials. Although conceptually new and rapid AST technologies have been described, including new phenotyping methods, digital imaging and genomic approaches, there is no single major, or broadly accepted, technological breakthrough that leads the field of rapid AST platform development. This might be owing to several barriers that prevent the timely development and implementation of novel and rapid AST platforms in health-care settings. In this Consensus Statement, we explore such barriers, which include the utility of new methods, the complex process of validating new technology against reference methods beyond the proof-of-concept phase, the legal and regulatory landscapes, costs, the uptake of new tools, reagent stability , optimization of target product profiles, difficulties conducting clinical trials and issues relating to quality and quality control, and present possible solutions.

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“…Hebert et al demonstrated a method by which traditional antibiogram data could be reorganized into prescribing decision aids for inpatients . The resulting weighted‐incidence syndromic combination antibiogram (WISCA) included only those organisms associated with syndrome‐specific conditions (eg, pneumonia) and antibiotics that were relevant to the hospital setting . The resulting tool can help predict the likelihood that a particular antibiotic treatment regimen will cover a particular infection rather than target a singular organism.…”

mentioning

confidence: 99%

“…24 The resulting weighted-incidence syndromic combination antibiogram (WISCA) included only those organisms associated with syndrome-specific conditions (eg, pneumonia) and antibiotics that were relevant to the hospital setting. [25][26][27] The resulting tool can help predict the likelihood that a particular antibiotic treatment regimen will cover a particular infection rather than target a singular organism. Presenting susceptibility data in this manner may be easier for clinicians to understand, and it consequently may have a greater impact on empirical antibiotic decision making.…”

mentioning

confidence: 99%

Decisional Guidance Tool for Antibiotic Prescribing in the Skilled Nursing Facility

Davenport

Brodeur

Wolff

et al. 2019

J American Geriatrics Society

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OBJECTIVES To derive weighted‐incidence syndromic combination antibiograms (WISCAs) in the skilled nursing facility (SNF). To compare burden of resistance between SNFs in a region and those with and without protocols designed to reduce inappropriate antibiotic use. DESIGN Retrospective analysis of microbial data from a regional laboratory. SETTING We analyzed 2484 isolates collected at a regional laboratory from a large mixed urban and suburban area from January 1, 2015, to December 31, 2015. PARTICIPANTS A total of 28 regional SNFs (rSNFs) and 7 in‐network SNFs (iSNFs). MEASUREMENTS WISCAs were derived combining Escherichia coli, Proteus mirabilis, Klebsiella pneumoniae, and reports restricted to fluoroquinolones, cefazolin, amoxicillin clavulanate, and trimethoprim/sulfamethoxazole. RESULTS Pooling the target isolates into WISCAs resulted in an average of 28 of 37 achieving a number greater than 30 with an average of 50 isolates (range = 11‐113; >97% urinary). Significant differences were found in antibiotic susceptibility between grouped rSNF data and iSNF data of 75% vs 65% (2.76‐11.77; P = .002). The susceptibilities were higher in iSNFs with active antibiotic reduction protocols compared with iSNFs without protocols and rSNFs (effect size = .79 vs .67 and .65, respectively) (I2 = 93.33; P < .01). Susceptibilities to cefazolin (95% vs 76%; P < .001) and fluoroquinolones (72% vs 64%; P = .048) were significantly higher in iSNFs with active urinary tract infection protocols as compared with iSNFs without antibiotic reduction protocols. CONCLUSION These results suggest that WISCAs can be developed in most SNFs, and their results can serve as indicators of successful antibiotic stewardship programs. J Am Geriatr Soc 68:55–61, 2019

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Development of a urinary-specific antibiogram for gram-negative isolates: Impact of patient risk factors on susceptibility

Cited by 13 publications

References 21 publications

Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data – The Influence of Different Parameters in a Routine Clinical Microbiology Laboratory

Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data – The Influence of Different Parameters in a Routine Clinical Microbiology Laboratory

Developmental roadmap for antimicrobial susceptibility testing systems

Decisional Guidance Tool for Antibiotic Prescribing in the Skilled Nursing Facility

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