ArticlePloS one2016
Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data--The Influence of Different Parameters in a Routine Clinical Microbiology Laboratory.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Linking antimicrobial resistance surveillance to antibiotic policy in healthcare settings: the COMBACTE-Magnet EPI-Net COACH project.The Journal of antimicrobial chemotherapy · 2020Pooled it
- Determinants of methicillin-resistant Staphylococcus aureus (MRSA) prevalence in the Asia-Pacific region: A systematic review and meta-analysis.Journal of global antimicrobial resistance · 2019Pooled it
- Proposed Risk of Bias Assessment Tool forPathogens (Basel, Switzerland) · 2026Article
- The impact of faster antimicrobial susceptibility testing report and dual antimicrobial susceptibility testing cards on the antibiotic therapy and economics of hospitalized bloodstream infection patients.BMC microbiology · 2026Article
- Microbiological Profiles and Resistance Patterns in Pediatrics With Cancer: An 8-Year Study at a Comprehensive Cancer Center in Jordan.Cancer reports (Hoboken, N.J.) · 2025Article
- Drug combinations targeting antibiotic resistance.npj antimicrobials and resistance · 2024Review
- Change in Klebsiella pneumoniae susceptibility profile after the arrival of ceftazidime-avibactam in an Argentinean intensive care unit: a new ecological landscape.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2024Article
- Implications of deduplication on the detection rates of multidrug-resistant organism (MDRO) in various specimens: insights from the hospital infection surveillance program.Antimicrobial resistance and infection control · 2024Article
- [Prevalence and antimicrobial susceptibility of Klebsiella pneumoniae in a Cardiology Hospital].Revista medica del Instituto Mexicano del Seguro Social · 2024Observational
- Diagnostic Accuracy of Hospital Antibiograms in Predicting the Risk of Antimicrobial Resistance in Enterobacteriaceae Isolates: A Nationwide Multicenter Evaluation at the Veterans Health Administration.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- Deficiencies of Rule-Based Technology-Generated Antibiograms for Specialized Care Units.Antibiotics (Basel, Switzerland) · 2023Article
- The Path of More Resistance: a Comparison of National Healthcare Safety Network and Clinical Laboratory Standards Institute Criteria in Developing Cumulative Antimicrobial Susceptibility Test Reports and Institutional Antibiograms.Journal of clinical microbiology · 2022Article
- Geographical information system and spatial-temporal statistics for monitoring infectious agents in hospital: a model using Klebsiella pneumoniae complex.Antimicrobial resistance and infection control · 2021Article
- The antibiogram: key considerations for its development and utilization.JAC-antimicrobial resistance · 2021Review
- Importance of Susceptibility Rate of 'the First' Isolate: Evidence of Real-World Data.Medicina (Kaunas, Lithuania) · 2020Article
- Recommendations for the surveillance of multidrug-resistant bacteria in Italian long-term care facilities by the GLISTer working group of the Italian Association of Clinical Microbiologists (AMCLI).Antimicrobial resistance and infection control · 2020Article
- Bacteriology and Antimicrobial Resistance in Vanuatu: January 2017 to December 2019.Antibiotics (Basel, Switzerland) · 2020Article
- Influence of analysis conditions for antimicrobial susceptibility test data on susceptibility rates.PloS one · 2020Article
- Article
- Microbiology Investigation Criteria for Reporting Objectively (MICRO): a framework for the reporting and interpretation of clinical microbiology data.BMC medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.