Clinical evaluation of the acuitas® AMR gene panel for rapid detection of bacteria and genotypic antibiotic resistance determinants

Rapid metagenomic sequencing for diagnosis and antimicrobial sensitivity prediction of canine bacterial infections

Antimicrobial resistance is a major threat to human and animal health. There is an urgent need to ensure that antimicrobials are used appropriately to limit the emergence and impact of resistance. In the human and veterinary healthcare setting, traditional culture and antimicrobial sensitivity testing typically requires 48-72 h to identify appropriate antibiotics for treatment. In the meantime, broad-spectrum antimicrobials are often used, which may be ineffective or impact non-target commensal bacteria. Here, we present a rapid, culture-free, diagnostics pipeline, involving metagenomic nanopore sequencing directly from clinical urine and skin samples of dogs. We have planned this pipeline to be versatile and easily implementable in a clinical setting, with the potential for future adaptation to different sample types and animals. Using our approach, we can identify the bacterial pathogen present within 5 h, in some cases detecting species which are difficult to culture. For urine samples, we can predict antibiotic sensitivity with up to 95 % accuracy. Skin swabs usually have lower bacterial abundance and higher host DNA, confounding antibiotic sensitivity prediction; an additional host depletion step will likely be required during the processing of these, and other types of samples with high levels of host cell contamination. In summary, our pipeline represents an important step towards the design of individually tailored veterinary treatment plans on the same day as presentation, facilitating the effective use of antibiotics and promoting better antimicrobial stewardship.

Faster infection diagnostics for intensive care unit (ICU) patients

INTRODUCTION

: The patient admitted to intensive care units (ICU) is critically ill, to some extent immunosuppressed, with a high risk of infection, sometimes by multidrug-resistant microorganisms. In this context, the intensivist expects from the microbiology service quick and understandable information so that appropriate antimicrobial treatment for that particular patient and infection can be initiated.

AREAS COVERED

: In this review of recent literature (2015-2021), we identified diagnostic methods for the most prevalent infections in these patients through a search of the databases Pubmed, evidence-based medicine online, York University reviewers group, Cochrane, MBE-Trip, and Sumsearch using the terms: adult, clinical laboratory techniques, critical care, early diagnosis, microbiology, molecular diagnostic techniques, spectrometry and metagenomics.

EXPERT OPINION

: There has been an exponential surge in diagnostic systems used directly on blood and other samples to expedite microbial identification and antimicrobial susceptibility testing of pathogens. Few studies have thus far assessed their clinical impact; final outcomes will also depend on preanalytical and post-analytical factors. Besides, many of the resistance mechanisms cannot yet be detected with molecular techniques, which impairs the prediction of the actual resistance phenotype.

Nonetheless, this is an exciting field with much yet to explore.

Interplay between Phenotypic Resistance to Relevant Antibiotics in Gram-Negative Urinary Pathogens: A Data-Driven Analysis of 10 Years' Worth of Antibiogram Data

The global emergence of antimicrobial resistance (AMR) has become a critical issue for clinicians, as it puts the decades of developments in the medical field in jeopardy, by severely limiting the useful therapeutic arsenal of drugs, both in nosocomial and community-acquired infections. In the present study, a secondary analysis of taxonomic and resistance data was performed, corresponding to urinary tract infections (UTIs) caused by Gram-negative bacteria, detected between 1 January 2008 to 31 December 2017 at the Albert Szent-Györgyi Health Center, University of Szeged. The following were identifiable from the data collected: year of isolation; outpatient (OP)/inpatient (IP) origin of the isolate; taxonomy; and susceptibility/resistance to selected indicator antibiotics. Principal component analysis (PCA) and a correlation matrix were used to determine the association between the presences of resistance against indicator antibiotics in each taxonomic group. Overall, data from n = 16,240 outpatient and n = 13,964 inpatient Gram-negative UTI isolates were included in the data analyses. In E. coli, strong positive correlations were seen between resistance to ciprofloxacin (CIP) and gentamicin (GEN) resistance (OP: r = 0.6342, p = 0.049; IP: r = 0.9602, p < 0.001), whereas strong negative correlations were shown for fosfomycin (FOS) and nitrofurantoin (NIT) resistance (OP: r = -0.7183, p = 0.019; IP: r = -0.7437; p = 0.014). For Klebsiella spp. isolates, CIP resistance showed strong positive correlation with resistance to third-generation cephalosporins (3GC) and GEN (r = 0.7976, p = 0.006 and r = 0.7428, p = 0.014, respectively) in OP isolates, and with resistance to trimethoprim-sulfamethoxazole (SXT) and FOS (r = 0.8144, p = 0.004 and r = 0.7758, p < 0.001, respectively) in IP isolates. For members of the Citrobacter-Enterobacter-Serratia group, the resistance among indicator antibiotics showed a strong positive correlation, with the exception of FOS resistance. In the Proteus-Providencia-Morganella group, the strongest association was noted between CIP and SXT resistance (OP: r = 0.9251, p < 0.001; IP: r = 0.8007; p = 0.005). In the case of OP Acinetobacter spp., CIP showed strong and significant positive correlations with most indicator antibiotics, whereas for IP isolates, strong negative correlations arose among imipenem (IMI) resistance and resistance to other drugs. For Pseudomonas spp., strong and positive correlations were noted among resistance to β-lactam antibiotics and aminoglycosides, with the exception of ceftazidime (CEFT), showing strong, but negative correlations. Though molecular tests and sequencing-based platforms are now considered as the gold-standard for AMR surveillance, standardized collection of phenotypic resistance data and the introduction of Big Data analytic methods may be a viable alternative for molecular surveillance, especially in low-resource settings.