Frequency of susceptibility testing for patients with persistent methicillin-resistant Staphylococcus aureus bacteremia

Assessment of change in antimicrobial susceptibility pattern of bacteria causing orthopaedic infections by sequential sampling: A prospective cohort study

Background

Management of Orthopaedic wound infections often depend on isolation of bacteria species (spp.) and its subsequent antimicrobial susceptibility testing (AST). However, the susceptibility to antibiotics may change over time in the same bacterial spp. particularly after initiation of antibiotic therapy. Repeating AST through sequential sampling can be used for the early detection of changes in antimicrobial susceptibility pattern. However, the recommendations about the optimal frequency of repeat AST for same bacterial spp. isolates from same patient to detect the changes in susceptibility patterns are still not established. Furthermore, no prospective research is available to address the crucial issue. Thus, we aimed this study to evaluate the need of repeat AST through sequential samples from the same site.

Methods

AST was performed on same bacterial spp. isolates from three sequential samples using Kirby-Bauer disc diffusion method. Considering day 1 as control/baseline, changes in antimicrobial susceptibility pattern was interpreted on two sequential instances (on day 3 and day5). Changes were categorized into favorable & unfavorable and major & minor change categories.

Results

The overall change in antimicrobial susceptibility pattern was 28 % on instance 1(on day3) and 36.1 % at instance 2 (on day 5). Susceptible to resistance phenotypic change was 14.9 % at instance 1 and 9.2 % at instance 2.A higher percentage change per case in antimicrobial susceptibility pattern was observed at instance 2. Predominant changes were towards the direction of favorable antimicrobial susceptibility pattern.

Conclusion

The risk of change in antimicrobial susceptibility potential was over 10 % at both the instances. Furthermore, it was higher at instance 2 i.e., at day5, therefore a repeat sequential antimicrobial susceptibility testing would be recommended at later instance.

Less Is More: When to Repeat Antimicrobial Susceptibility Testing

This study investigated the frequency of change of the antimicrobial susceptibility pattern when the same isolate was found in the same patient in various situations. We used laboratory data collected over a period of 8 years (January 2014 to December 2021) at the clinical microbiology laboratory of a tertiary hospital for Escherichia coli, Klebsiella pneumoniae, Enterobacter spp., Pseudomonas aeruginosa, and Staphylococcus aureus. Antimicrobial susceptibility tests (AST) were performed using Vitek 2 automated system. We determined essential agreement and categorical agreement, and introduced the new terms essential MIC increase and change from nonresistant to resistant to present changes in antimicrobial susceptibility over time. During the study period, 18,501 successive AST were included. The risk for S. aureus to be resistant to any antibiotic upon repeated culture was <10% during a follow-up of 30 days. For Enterobacterales, this risk was approximately 10% during a follow-up of 7 days. For P. aeruginosa, this risk was higher. The longer the follow-up period, the higher the risk that the bacteria would show phenotypic resistance. We also found that some drug-bug combinations were more likely to develop phenotypical resistance (i.e., E. coli/amoxicillin-clavulanic acid and E. coli/cefuroxime). A potential consequence of our finding is that if we regard a risk of resistance below 10% as acceptable, it may be feasible to omit follow-up AST within 7 days for the microorganisms investigated in this study. This approach saves money, time, and will reduce laboratory waste. Further studies are needed to determine whether these savings are in balance with the small possibility of treating patients with inadequate antibiotics.

Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance

Timely and efficacious antibiotic treatment depends on precise and quick in silico antimicrobial-resistance predictions. Limited treatment choices due to antimicrobial resistance (AMR) highlight the necessity to optimize the available diagnostics. AMR can be explicitly anticipated on the basis of genome sequence. In this study, we used transcriptomes of 410 multidrug-resistant isolates of Pseudomonas aeruginosa. We trained 10 machine learning (ML) classifiers on the basis of data on gene expression (GEXP) information and generated predictive models for meropenem, ciprofloxacin, and ceftazidime drugs. Among all the used ML models, four models showed high F1-score, accuracy, precision, and specificity compared with the other models. However, RandomForestClassifier showed a moderate F1-score (0.6), precision (0.61), and specificity (0.625) for ciprofloxacin. In the case of ceftazidime, RidgeClassifier performed well and showed F1-score (0.652), precision (0.654), and specificity (0.652) values. For meropenem, KNeighborsClassifier exhibited moderate F1-score (0.629), precision (0.629), and specificity (0.629). Among these three antibiotics, GEXP data on meropenem and ceftazidime improved diagnostic performance. The findings will pave the way for the establishment of a resistance profiling tool that can predict AMR on the basis of transcriptomic markers.

Prediction of Antimicrobial Minimal Inhibitory Concentrations for Neisseria gonorrhoeae using Machine Learning Models

The lowest concentration of an antimicrobial agent that can inhibit the visible growth of a microorganism after overnight incubation is called as minimum inhibitory concentration (MIC) and the drug prescriptions are made on the basis of MIC data to ensure successful treatment outcomes. Therefore, reliable antimicrobial susceptibility data is crucial, and it will help clinicians about which drug to prescribe. Although few prediction studies based on strategies have been conducted, however, no single machine learning (ML) modelling has been carried out to predict MICs in N. gonorrhoeae. In this study, we propose a ML based approach that can predict MICs of a specific antibiotic using unitigs sequences data. We retrieved N. gonorrhoeae genomes from European Nucleotide Archive and NCBI and analysed them combined with their respective MIC data for cefixime, ciprofloxacin, and azithromycin and then we constructed unitigs by using de Brujin graphs. We built and compared 35 different ML regression models to predict MICs. Our results demonstrate that RandomForest and CATBoost models showed best performance in predicting MICs of the three antibiotics. The coefficient of determination, R², (a statistical measure of how well the regression predictions approximate the real data points) for cefixime, ciprofloxacin, and azithromycin was 0.75787, 0.77241, and 0.79009 respectively using RandomForest. For CATBoost model, the R² value was 0.74570, 0.77393, and 0.79317 for cefixime, ciprofloxacin, and azithromycin respectively. Lastly, using feature importance, we explore the important genomic regions identified by the models for predicting MICs. The major mutations which are responsible for resistance against these three antibiotics were chosen by ML models as a top feature in case of each antibiotics. CATBoost, DecisionTree, GradientBoosting, and RandomForest regression models chose the same unitigs which are responsible for resistance. This unitigs-based strategy for developing models for MIC prediction, clinical diagnostics, and surveillance can be applicable for other critical bacterial pathogens.

Dynamic changes of inflammatory response and oxidative stress induced by methicillin-resistant Staphylococcus aureus in mice

This study is to analyze the dynamic changes of inflammation and oxidative stress in mice infected with MRSA and to provide experimental basis for clinically formulating reasonable treatment plans. We established a model of MRSA infection in mice, detected the fluctuations in the concentration of proinflammatory cytokines and oxidative stress factors with time, and combined with the results of microscopic examination of tissue sections to explain the infection in vivo caused by MRSA. The results showed that on the 1st, 3rd, and 7th day of MRSA infection, the number of leukocytes and eosinophils decreased at first and then increased, monocytes increased continuously, and neutrophils and basophils decreased. At the same time, the levels of proinflammatory cytokines IL-1β, IL-6, and TNF-α increased. The concentration of glutathione peroxide decreased, and the oxidative metabolites increased. Tissue sections also showed that inflammation and oxidative stress occurred in mice. It is obvious that MRSA infection can lead to significant inflammation and oxidative stress. Therefore, while treating MRSA infection, attention should be paid to the levels of inflammation and oxidative stress in different periods to achieve better treatment effects.

Left ventricular assist device-associated endocarditis involving multiple clones of Staphylococcus aureus with distinct antimicrobial susceptibility patterns

The case of a patient with left ventricular assist device (LVAD)-associated endocarditis involving multiple clones of Staphylococcus aureus is presented. Different clones with distinct colony morphology were identified from blood cultures collected on the same day and showed diverse antimicrobial resistance patterns. In addition, a difference in antimicrobial susceptibility was observed even within an identical clone recovered 400 days apart due to the loss of SCCmec for methicillin and modification of the 23S rRNA target site for linezolid during a long-term treatment course.

Precision of vancomycin and daptomycin MICs for methicillin-resistant Staphylococcus aureus and effect of subculture and storage

The reproducibility of vancomycin and daptomycin MICs, measured by broth microdilution (BMD) and Etest, was prospectively assessed for 10 methicillin-resistant Staphylococcus aureus (MRSA) isolates from the blood samples from patients on vancomycin therapy. The isolates were tested at the time of isolation from blood and following 5, 10, and 20 subcultures and at 1, 3, 6, and 12 months of storage at -70 °C. The MICs were determined by Etest and BMD using two different manufacturers (BBL and Difco) of cation-adjusted Mueller-Hinton broth (CA-MHB), and using three different drug powders: vancomycin from Sigma, vancomycin from Novation, and daptomycin from Cubist. The antimicrobial concentrations tested were 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 μg/ml. Two isolates were vancomycin intermediate and daptomycin nonsusceptible, and two isolates had reduced susceptibility to vancomycin (BMD MIC, 1.5 or 2.0 μg/ml). The vancomycin MICs were significantly higher in the BBL CA-MHB than those in the Difco CA-MHB, and with Sigma versus Novation vancomycin powder. The daptomycin MICs were also significantly higher in the BBL CA-MHB. The Etest MICs were significantly higher than those obtained by BMD for vancomycin but not for daptomycin. The average precision of the vancomycin BMD MICs when analyzing 20 results was ± 1.10-fold log2 dilutions, and it was ± 1.67-fold for daptomycin (10 results). The average precision for Etest was ± 1.11-fold for vancomycin and ± 1.16-fold for daptomycin. No significant change in MICs was noted following 5, 10, or 20 subcultures or at up to 6 months of frozen storage. However, the vancomycin MICs alone were significantly lower (0.74-fold) following 12 months of frozen storage. From these data, despite variations in CA-MHB and antimicrobial powder, the MIC result precision was <0.5 log2 dilutions in a single laboratory, suggesting that testing interdilution MICs (e.g., MICs between serial 2-fold dilutions) is a possibility. A more accurate method for measuring vancomycin MIC results is thus possible, but further standardization of BMD testing would be required to achieve this goal.

Prevalence and detection of mixed-population enterococcal bacteremia

Mixed-population (heterogeneous) enterococcal bacteremia (MEB) is rarely reported. Based on one occasion in which Vitek2 missed a vancomycin-resistant subpopulation isolated from a patient, we developed a simple method to detect this subpopulation and determined MEB frequency. The four patients presented here had either Enterococcus faecium or Enterococcus faecalis bacteremia caused by both vancomycin-resistant enterococci (VRE) and vancomycin-susceptible enterococci (VSE). No prior common antibiotic therapy was observed, and bacteremia resolved with daptomycin, gentamicin, and/or linezolid treatment. In two cases, VRE presence was missed by Vitek2. To detect the VRE subpopulation, tryptic soy broth was inoculated from positive blood cultures and a saline suspension was inoculated to a vancomycin (6-μg/ml) (V6) plate. Two isolates from each patient were studied further. Relatedness was assessed by multilocus sequence typing, fitness was evaluated by growth curve and competition assays, and vanA presence was determined by PCR. MEB represented ∼5% of all enterococcal bacteremias. All VRE subpopulations grew on V6 plates but were missed in two instances by Vitek2. VRE and VSE isolates from each patient were closely related and did not differ in overall fitness. All four VRE isolates and 2/4 VSE isolates were vanA positive. MEBs occur regardless of prior antimicrobial therapy, are relatively common in our hospital, and are important to detect. As far as we know, this study is the first to report heterogeneous E. faecalis bacteremia. There is a simple method to detect VRE subpopulations that may be missed by Vitek2.