Visualization of cross-resistance between antimicrobial agents by asymmetric multidimensional scaling.

WHAT IS KNOWN AND OBJECTIVE: In our previous studies, we developed a cross-resistance rate (CRR) correlation diagram (CRR diagram) that visually captures the magnitude of CRRs between antimicrobials using scatter plots. We used asymmetric multidimensional scaling (MDS) to transform cross-resistance similarities between antimicrobials into a 2-dimensional map and attempted to visually express them. We also explored the antibiograms of Pseudomonas aeruginosa before and after the transfer to newly built hospitals, and we determined by the CRR diagram that the CRRs among ß-lactam antimicrobials other than carbapenems decreased substantially with the facility transfer. The present study tests whether the analysis of CRRs by asymmetric MDS can be used as new visual information that is easy for healthcare professionals to understand. METHOD: We tested the impact of changes in the nosocomial environment due to institutional transfers on CRRs among antimicrobials in asymmetric MDS, as well as contrasted the asymmetric MDS map and CRR diagram. RESULTS AND DISCUSSION: In the asymmetric MDS map, antimicrobial groups with the same mechanism of action were displayed close together, and antimicrobial groups with different mechanisms of action were displayed separately. The asymmetric MDS map drawn solely for antimicrobials belonging to the group with the same mechanism of action showed similarities to the CRR diagram. Also, the distance of each antimicrobial to other antimicrobials shown in the asymmetric MDS map was negatively correlated with the CRRs for them against that antimicrobial. WHAT IS NEW AND CONCLUSION: The asymmetric MDS map expresses the dissimilarity as distances between agents, and there are no meanings or units on the ordinate and abscissa axes of the output map. In contrast, the CRR diagram expresses the antimicrobials' resistance status as values, such as resistance rate and CRR. By analysing the CRRs in the asymmetric MDS, it is feasible to visually recognize cross-resistance similarities between antimicrobial groups as distances. The use of the asymmetric MDS combined with the CRR diagram allows us to visually understand the resistance and cross-resistance status of each antimicrobial agent as a 2-dimensional map, as well as to understand the trends and characteristics of the data by means of quantitative values.

Monitoring antimicrobial cross-resistance with cross-resistance rate correlation diagrams: Changes in antibiotic susceptibility of Pseudomonas aeruginosa due to hospital relocation.

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 ß-lactam antibiotics: cross-resistance rates among ß-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.

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

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 ß-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 ß-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 ß-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.