Multicenter Evaluation of the Acuitas AMR Gene Panel for Detection of an Extended Panel of Antimicrobial Resistance Genes among Bacterial Isolates.

The Acuitas antimicrobial resistance (AMR) gene panel is a qualitative, multiplex, nucleic acid-based in vitro diagnostic test for the detection and differentiation of 28 antimicrobial resistance markers associated with not susceptible results (NS; i.e., intermediate or resistant) to one or more antimicrobial agents among cultured isolates of select Enterobacterales, Pseudomonas aeruginosa, and Enterococcus faecalis. This study was conducted at four sites and included testing of 1,224 deidentified stocks created from 584 retrospectively collected isolates and 83 prospectively collected clinical isolates. The Acuitas results were compared with a combined reference standard including whole-genome sequencing, organism identification, and phenotypic antimicrobial susceptibility testing. The positive percent agreement (PPA) for FDA-cleared AMR targets ranged from 94.4% for MCR-1 to 100% for armA, CTX-M-2, DHA, IMP, OXA-9, SHV, vanA, and VEB. The negative percent agreement (NPA) for the majority of targets was ≥99%, except for AAC, AAD, CMY-41, P. aeruginosa gyrA mutant, Sul1, Sul2, and TEM targets (range, 96.5% to 98.5%). Three AMR markers did not meet FDA inclusion criteria (GES, SPM, and MCR-2). For each organism, 1 to 22 AMR targets met the minimum reportable PPA/NPA and correlated with ≥80% positive predictive value with associated NS results for at least one agent (i.e., the probability of an organism carrying an AMR marker testing NS to the associated agent). We demonstrate that the Acuitas AMR gene panel is an accurate method to detect a broad array of AMR markers among cultured isolates. The AMR markers were further associated with expected NS results for specific agent-organism combinations.

Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes.

We developed a rapid high-throughput PCR test and evaluated highly antibiotic-resistant clinical isolates of Escherichia coli (n = 2,919), Klebsiella pneumoniae (n = 1,974), Proteus mirabilis (n = 1,150), and Pseudomonas aeruginosa (n = 1,484) for several antibiotic resistance genes for comparison with phenotypic resistance across penicillins, cephalosporins, carbapenems, aminoglycosides, trimethoprim-sulfamethoxazole, fluoroquinolones, and macrolides. The isolates originated from hospitals in North America (34%), Europe (23%), Asia (13%), South America (12%), Africa (7%), or Oceania (1%) or were of unknown origin (9%). We developed statistical methods to predict phenotypic resistance from resistance genes for 49 antibiotic-organism combinations, including gentamicin, tobramycin, ciprofloxacin, levofloxacin, trimethoprim-sulfamethoxazole, ertapenem, imipenem, cefazolin, cefepime, cefotaxime, ceftazidime, ceftriaxone, ampicillin, and aztreonam. Average positive predictive values for genotypic prediction of phenotypic resistance were 91% for E. coli, 93% for K. pneumoniae, 87% for P. mirabilis, and 92% for P. aeruginosa across the various antibiotics for this highly resistant cohort of bacterial isolates.

Rapid Molecular Diagnostics to Inform Empiric Use of Ceftazidime/Avibactam and Ceftolozane/Tazobactam Against Pseudomonas aeruginosa: PRIMERS IV.

BACKGROUND: Overcoming ß-lactam resistance in pathogens such as Pseudomonas aeruginosa is a major clinical challenge. Rapid molecular diagnostics (RMDs) have the potential to inform selection of empiric therapy in patients infected by P. aeruginosa. METHODS: In this study, we used a heterogeneous collection of 197 P. aeruginosa that included multidrug-resistant isolates to determine whether 2 representative RMDs (Acuitas Resistome test and VERIGENE gram-negative blood culture test) could identify susceptibility to 2 newer ß-lactam/ß-lactamase inhibitor (BL-BLI) combinations, ceftazidime/avibactam (CZA) and ceftolozane/tazobactam (TOL/TAZO). RESULTS: We found that the studied RMD platforms were able to correctly identify BL-BLI susceptibility (susceptibility sensitivity, 100%; 95% confidence interval [CI], 97%, 100%) for both BLs-BLIs. However, their ability to detect resistance to these BLs-BLIs was lower (resistance sensitivity, 66%; 95% CI, 52%, 78% for TOL/TAZO and 33%; 95% CI, 20%, 49% for CZA). CONCLUSIONS: The diagnostic platforms studied showed the most potential in scenarios where a resistance gene was detected or in scenarios where a resistance gene was not detected and the prevalence of resistance to TOL/TAZO or CZA is known to be low. Clinicians need to be mindful of the benefits and risks that result from empiric treatment decisions that are based on resistance gene detection in P. aeruginosa, acknowledging that such decisions are impacted by the prevalence of resistance, which varies temporally and geographically.