What is known and Objective
Though most medical institutions calculate antimicrobial susceptibility and resistance rates of microbes isolated at their own facility as part of their efforts to promote the proper use of antibiotics, very few, if any, regularly monitor cross‐resistance rates between antimicrobial agents. The authors have devised a tool in the form of a cross‐resistance rate correlation diagram (CRR diagram) that allows easy identification of increases or decreases in, or changes in the pattern of, antimicrobial cross‐resistance. The objective was to perform an analysis by CRR diagrams of the effect of relocation to a newly built facility on antimicrobial resistance and cross‐resistance rates at a medical facility.
Methods
The Sakai City Medical Center relocated in July 2015 to a newly built facility located in a different primary medical care zone 3.5 km away. Based on the drug susceptibility test data compiled at the Sakai City Medical Center, resistance and cross‐resistance rates of Pseudomonas aeruginosa before and after the relocation of the hospital facility were calculated, and the rates were assessed using CRR diagrams.
Results and discussion
It was possible to confirm the effect of hospital relocation on antibiotic susceptibility of P aeruginosa in terms of changes in resistance and cross‐resistance rates. The effect of the facility's relocation on cross‐resistance rates was particularly notable with respect to β‐lactam antibiotics: cross‐resistance rates among β‐lactams decreased substantially, represented as a large wedge‐shaped change towards the origin on the CRR diagram. Rates of cross‐resistance between classes of antibiotics with a different mechanism of antibiotic action changed little.
What is new and conclusion
Including cross‐resistance rates in the routine monitoring of resistance and susceptibility rates practiced by a medical institution can provide a comprehensive insight into the dynamics of bacterial flora in the facility. CRR diagrams, which allow visualization of the status and changes in cross‐resistance, not only provide a new perspective for clinicians, but they also contribute to the proper use of antibiotics and serve as a tool in the education of healthcare professionals and students about antibiotic resistance.
This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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.
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