Antimicrobial Activities of Aztreonam-Avibactam and Comparator Agents against Enterobacterales Analyzed by ICU and Non-ICU Wards, Infection Sources, and Geographic Regions: ATLAS Program 2016–2020
Bibliographic record
Abstract
Increasing antimicrobial resistance among multidrug-resistant (MDR), extended-spectrum β-lactamase (ESBL)- and carbapenemase-producing Enterobacterales (CPE), in particular metallo-β-lactamase (MBL)-positive strains, has led to limited treatment options in these isolates. This study evaluated the activity of aztreonam-avibactam (ATM-AVI) and comparator antimicrobials against Enterobacterales isolates and key resistance phenotypes stratified by wards, infection sources and geographic regions as part of the ATLAS program between 2016 and 2020. Minimum inhibitory concentrations (MICs) were determined per Clinical and Laboratory Standards Institute (CLSI) guidelines. The susceptibility of antimicrobials were interpreted using CLSI and European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints. A tentative pharmacokinetic/pharmacodynamic breakpoint of 8 µg/mL was considered for ATM-AVI activity. ATM-AVI inhibited ≥99.2% of Enterobacterales isolates across wards and ≥99.7% isolates across infection sources globally and in all regions at ≤8 µg/mL. For resistance phenotypes, ATM-AVI demonstrated sustained activity across wards and infection sources by inhibiting ≥98.5% and ≥99.1% of multidrug-resistant (MDR) isolates, ≥98.6% and ≥99.1% of ESBL-positive isolates, ≥96.8% and ≥90.9% of carbapenem-resistant (CR) isolates, and ≥96.8% and ≥97.4% of MBL-positive isolates, respectively, at ≤8 µg/mL globally and across regions. Overall, our study demonstrated that ATM-AVI represents an important therapeutic option for infections caused by Enterobacterales, including key resistance phenotypes across different wards and infection sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".