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

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.

ANTIBIOGRAM (Antibiogram)

“…The critical issues regarding constructing an antibiogram based on the recommendations of CLSI M39-A4, include the following regulations: the data should be analyzed and presented at least annually, include only final and verified results of at least 30 isolates, and only including isolates from the diagnostic testing (see Table 1). 7,[10][11][12][13] Frequency of data analysis and reporting according to CLSI's recommendations is at least annually. More frequent analysis and reporting can be considered if there are substantial numbers of isolates, significant changes in the percentage of susceptibility of certain bacteria, or if there is a new antibiotic that has been tested during the course of reporting period.…”

“…8,10,11,13 The selection of isolates and the handling of repeated isolates Analysis of antibiogram include only data from the first isolate of a given species per patient per analysis period, irrespective of the specimen sites, antibiotic susceptibility testing profile or phenotypic characteristic. 7,[10][11][12][13] In fact, one patient may have more than one isolates that had been cultured on multiple occasions. How to handle the repeated isolates still becomes the most controversial aspect when constructing an antibiogram.…”

“…According to CLSI's recommendation and supported by the results of several studies, the first isolate patient-based algorithm is better used to handle repeated isolates. [10][11][12][13][14][15] This algorithm requires good recording and reporting system of the microbiology laboratory where it will be applied.…”

Hospital Antibiogram- A Tool for Safe Antimicrobial Therapy

“…The most frequent use of cumulative antibiogram report is in guiding initial empirical antimicrobial therapy decisions for the management of infections in patients for whom microbiological test data do not exist. 4,5 The Clinical and Laboratory Standards Institute (CLSI) has developed guidelines, namely CLSI M39 -A4 guidelines for formulation of cumulative antimicrobial susceptibility test data by health care facilities. 6 CLSI gives practical recommendations for analysis and presentation of antimicrobial susceptibility trends.…”

Resistenzstatistiken – mit Verzerrungen umgehen

“…Leider enthält dieser Beitrag kaum Hinweise auf die inhärenten Probleme von Resistenzstatistiken. Kohlmann und Gatermann haben 2016 eine Analyse der schwierigen Parametrierung von Resistenzstatistiken vorgelegt [11]. Sie fordern eigene Standards in Europa.…”