Dataset Information

Influence of analysis conditions for antimicrobial susceptibility test data on susceptibility rates.

ABSTRACT: BACKGROUND:To support effective antibiotic selection in empirical treatments, infection control interventions, and antimicrobial resistance containment strategies, many medical institutions collect antimicrobial susceptibility test data conducted at their facilities to prepare cumulative antibiograms. AIM:To evaluate how the setpoints of duplicate isolate removal period and data collection period affect the calculated susceptibility rates in antibiograms. METHODS:The Sakai City Medical Center is a regional core hospital for tertiary emergency medical care with 480 beds for general clinical care. In this study, all the Pseudomonas aeruginosa, Escherichia coli, and Klebsiella pneumoniae isolates collected at the Sakai City Medical Center Clinical Laboratory between July 2013 and December 2018 were subjected to antimicrobial susceptibility tests and the resulting data was analyzed. FINDINGS:The longer the duplicate isolate removal period, the fewer the isolates are available for every bacterial species. Differences in the length of the duplicate isolate removal period affected P. aeruginosa susceptibility rates to ?-lactam antibiotics by up to 10.8%. The setpoint of the data collection period affected the antimicrobial susceptibility rates by up to 7.3%. We found that a significant change in susceptibility could be missed depending on the setting of the data collection period, in preparing antibiogram of ?-lactam antibiotics for P. aeruginosa. CONCLUSIONS:When referring to antibiograms, medical professionals involved in infectious disease treatment should be aware that the parameter values, such as the duplicate isolate removal period and the data collection period, affect P. aeruginosa susceptibility rates especially to ?-lactam antibiotics. And antibiogram should be updated within the shortest time period that is practically possible, taking into account restrictions such as numbers of specimen.

SUBMITTER: Hatsuda Y

PROVIDER: S-EPMC7310835 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Influence of analysis conditions for antimicrobial susceptibility test data on susceptibility rates.

Hatsuda Yasutoshi Y Ishizaka Toshihiko T Koizumi Naonori N Yasui Yukako Y Saito Takako T Maki Syou S Omotani Sachiko S Mukai Junji J Tachi Tomoya T Teramachi Hitomi H Myotoku Michiaki M

PloS one 20200623 6

<h4>Background</h4>To support effective antibiotic selection in empirical treatments, infection control interventions, and antimicrobial resistance containment strategies, many medical institutions collect antimicrobial susceptibility test data conducted at their facilities to prepare cumulative antibiograms.<h4>Aim</h4>To evaluate how the setpoints of duplicate isolate removal period and data collection period affect the calculated susceptibility rates in antibiograms.<h4>Methods</h4>The Sakai ...[more]

PMID: 32574199

Similar Datasets

Project description: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.

Project description:BackgroundSome antibiotic pairs display a property known as collateral sensitivity in which the evolution of resistance to one antibiotic increases sensitivity to the other. Alternating between collaterally sensitive antibiotics has been proposed as a sustainable solution to the problem of antibiotic resistance. We aimed to identify antibiotic pairs that could be considered for treatment strategies based on alternating antibiotics.MethodsWe did a retrospective analysis of 448 563 antimicrobial susceptibility test results acquired over a 4-year period (Jan 1, 2015, to Dec 31, 2018) from 23 hospitals in the University of Pittsburgh Medical Center (Pittsburgh, PA, USA) hospital system. We used a score based on mutual information to identify pairs of antibiotics displaying disjoint resistance, wherein resistance to one antibiotic is commonly associated with susceptibility to the other and vice versa. We applied this approach to the six most frequently isolated bacterial pathogens (Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Enterococcus faecalis, Pseudomonas aeruginosa, and Proteus mirabilis) and subpopulations of each created by conditioning on resistance to individual antibiotics. To identify higher-order antibiotic interactions, we predicted rates of multidrug resistance for triplets of antibiotics using Markov random fields and compared these to the observed rates.FindingsWe identified 69 antibiotic pairs displaying varying degrees of disjoint resistance for subpopulations of the six bacterial species. However, disjoint resistance was rarely conserved at the species level, with only 6 (0·7%) of 875 antibiotic pairs showing evidence of disjoint resistance. Instead, more than half of antibiotic pairs (465 [53·1%] of 875) exhibited signatures of concurrent resistance, whereby resistance to one antibiotic is associated with resistance to another. We found concurrent resistance to extend to more than two antibiotics, with observed rates of resistance to three antibiotics being higher than predicted from pairwise information alone.InterpretationThe high frequency of concurrent resistance shows that bacteria have means of counteracting multiple antibiotics at a time. The almost complete absence of disjoint resistance at the species level implies that treatment strategies based on alternating between antibiotics might require subspecies level pathogen identification and be limited to a few antibiotic pairings.FundingUS National Institutes of Health.

Dataset Information

Influence of analysis conditions for antimicrobial susceptibility test data on susceptibility rates.

Publications

Influence of analysis conditions for antimicrobial susceptibility test data on susceptibility rates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets