Assessment of the comparability of CLSI, EUCAST and Stokes antimicrobial susceptibility profiles for Escherichia coli uropathogenic isolates

O'Halloran, C; Walsh, Niamh; O'Grady, Mary Claire; Barry, Louise; Hooton, Carmel; Corcoran, G.D.; Lucey, Brigid

doi:10.1080/09674845.2017.1392736

Cited by 11 publications

(11 citation statements)

References 14 publications

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“…Zone diameter data for first-line antimicrobial agents tested according to CLSI standards against all non-duplicate (first isolate per patient) clinical isolates of Escherichia coli , Klebsiella pneumoniae , and Pseudomonas aeruginosa from 1 January 2017 to 31 December 2017 were extracted from the Laboratory Information Management System and interpreted separately using EUCAST 2018 [2] and CLSI 2018 [3] breakpoints as susceptible, intermediate, or resistant and category agreement (percentage of isolates with the same result) determined. These organisms were selected as they were the commonest Gram-negative isolates in 2017 for which both organisations provide clinical breakpoints, and recent studies have reported discrepancies in their susceptibility interpretation [4] , [5] .…”

mentioning

confidence: 99%

Impact of CLSI and EUCAST breakpoint discrepancies on reporting of antimicrobial susceptibility and AMR surveillance

Cusack

Ashley

Ling

et al. 2019

Clinical Microbiology and Infection

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mentioning

confidence: 99%

Impact of CLSI and EUCAST breakpoint discrepancies on reporting of antimicrobial susceptibility and AMR surveillance

Cusack

Ashley

Ling

et al. 2019

Clinical Microbiology and Infection

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“…During our studies, association between applied interpretation criteria and susceptibility patterns, as well as different phenotypes of resistance to selected antimicrobials among opportunistic haemophili rods were detected. Many authors 14 – 20 point to the differences occurring at the following fields: categorization of drug susceptibility, assignment to individual beta-lactam phenotypes of resistance in Haemophilus spp. bacteria, as well as CLSI 21 and EUCAST 22 cut-off values.…”

Section: Discussionmentioning

confidence: 99%

Prevalence of susceptibility patterns of opportunistic bacteria in line with CLSI or EUCAST among Haemophilus parainfluenzae isolated from respiratory microbiota

Kosikowska

Andrzejczuk

Grywalska

et al. 2020

Sci Rep

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The application of CLSI and EUCAST guidelines led to many discrepancies. Various doubts have already appeared in preliminary stages of microbiological diagnostics of Haemophilus spp. A total of 87 H. parainfluenzae isolates were obtained from throat or nasopharyngeal swabs from adults 18 to 70 years old, both healthy volunteers and patients with chronic diseases between 2013 to 2015 in eastern Poland. Haemophilus spp. were identified by colony morphology, Gram-staining, API NH and MALDI-TOF MS technique. Both susceptibility to various antimicrobials and phenotypes of Haemophilus spp. resistance to beta-lactams were determined. Statistically significant association between applied guidelines and drug resistance patterns were observed to as follows: ampicillin, cefuroxime, cefotaxime, amoxicillin-clavulanate, azithromycin, tetracycline and trimethoprim-sulfamethoxazole. Resistance phenotypes according to CLSI vs. EUCAST were as follows: 3.4% vs. 8.0% for BLNAR and 6.9% vs. 19.5% for BLPACR isolates. In conclusion, this is the first study that reports comparative analysis of drug susceptibility interpretation using CLSI and EUCAST of haemophili rods from human respiratory microbiota in Poland. In case of susceptible, increased exposure (formerly intermediate) category of susceptibility within H. parainfluenzae isolates we have observed EUCAST as more restrictive than CLSI. Moreover, BLNAI and BLPAI phenotype isolates have been observed, as well as BLPBR using only CLSI or EUCAST guidelines, respectively.

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“…Moreover, values for both evaluation criteria are constantly evolving when new data become available and those with the greatest differences (eg ciprofloxacin in E. coli) tended to converge over time. 94 The main task of surveillance systems is to provide an overview of patterns and trends, however some systems may provide additional useful information for risk factor analysis.…”

Section: Amr Surveillance and Monitoring Systemsmentioning

confidence: 99%

<p>Monitoring Antimicrobial Resistance and Drug Usage in the Human and Livestock Sector and Foodborne Antimicrobial Resistance in Six European Countries</p>

Mesa-Varona¹,

Chaintarli²,

Müller‐Pebody³

et al. 2020

IDR

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Introduction: Antimicrobial resistance (AMR), associated with antimicrobial use (AMU), is a major public concern. Surveillance and monitoring systems are essential to assess and control the trends in AMU and AMR. However, differences in the surveillance and monitoring systems between countries and sectors make comparisons challenging. The purpose of this article is to describe all surveillance and monitoring systems for AMU and AMR in the human and livestock sectors, as well as national surveillance and monitoring systems for AMR in food, in six European countries (Spain, Germany, France, the Netherlands, the United Kingdom and Norway) as a baseline for developing suggestions to overcome current limitations in comparing AMU and AMR data. Methods: A literature search in 2018 was performed to identify relevant peer-reviewed articles and national and European grey reports as well as AMU/AMR databases. Results: Comparison of AMU and AMR systems across the six countries showed a lack of standardization and harmonization with different AMU data sources (prescription vs sales data) and units of AMU and AMR being used. The AMR data varied by sample type (clinical/non-clinical), laboratory method (disk diffusion, microdilution, and VITEK, among others), data type, ie quantitative (minimum inhibition concentration (MIC) in mg/ L/inhibition zone (IZ) in mm) vs qualitative data (susceptible-intermediate-resistant (SIR)), the standards used (EUCAST/CLSI among others), and/or the evaluation criteria adopted (epidemiological or clinical). Discussion: A One Health approach for AMU and AMR requires harmonization in various aspects between human, animal and food systems at national and international levels. Additionally, some overlap between systems of AMU and AMR has been encountered. Efforts should be made to improve standardization and harmonization and allow more meaningful analyses of AMR and AMU surveillance data under a One Health approach.

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Assessment of the comparability of CLSI, EUCAST and Stokes antimicrobial susceptibility profiles for Escherichia coli uropathogenic isolates

Abstract: Our data indicate that the discrepancies generated through using different AST methods and different interpretive guidelines may result in confusion and inaccuracy when prescribing treatment for urinary tract infection.

Cited by 11 publications

References 14 publications

Impact of CLSI and EUCAST breakpoint discrepancies on reporting of antimicrobial susceptibility and AMR surveillance

Impact of CLSI and EUCAST breakpoint discrepancies on reporting of antimicrobial susceptibility and AMR surveillance

Prevalence of susceptibility patterns of opportunistic bacteria in line with CLSI or EUCAST among Haemophilus parainfluenzae isolated from respiratory microbiota

<p>Monitoring Antimicrobial Resistance and Drug Usage in the Human and Livestock Sector and Foodborne Antimicrobial Resistance in Six European Countries</p>

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