Reading and understanding an antibiogram

Submitted: 6 October 2016
Accepted: 20 October 2016
Published: 15 December 2016
Abstract Views: 18542
PDF: 5863
Appendix: 913
HTML: 11593
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

In recent years, we have been facing a significant increase in antimicrobial resistance and complex enzymatic mechanisms. The challenge of antibiotic resistance is becoming dramatic. Among gram-positive it is spreading resistance to glycopeptides, reducing the possibility to use these drugs empirically. Among gram-negative rods, beside the spreading of extended-spectrumβ-lactamases, there is an increased diffusion of carbapenemases. In order to administer the correct antibiotic therapy, physicians need a rapid and correct interpretation with non-automated tests to implement appropriate therapeutic strategies. The automated reporting systems do not always provide complete and accurate information on antimicrobial resistance phenotype, making it difficult to interpret. Recently, the European Committee of Antimicrobial Susceptibility Testing (EUCAST) has proposed a new breakpoint system to be adopted by European countries. An interpretative reading of an antibiogram aims to analyze the overall susceptibility pattern, not just the result for an individual antibiotic, and so to predict the underlying resistance mechanisms. The purpose of this work is to guide physicians in reading and understanding antibiograms through an attempt of phenotypic interpretation of resistance mechanism.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

PlumX Metrics

PlumX Metrics  provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.

Citations

How to Cite

Tascini, C., Sozio, E., Viaggi, B., & Meini, S. (2016). Reading and understanding an antibiogram. Italian Journal of Medicine, 10(4), 289–300. https://doi.org/10.4081/itjm.2016.794