2893 A. Efficacy and Safety of Aztreonam-Avibactam for the Treatment of Serious Infections Due to Gram-Negative Bacteria, Including Metallo-β-Lactamase-Producing Pathogens: Phase 3 REVISIT Study
Bibliographic record
Abstract
Abstract Background Multidrug resistant (MDR) Gram-negative bacteria, including metallo-β-lactamase (MBL) producers, pose significant treatment challenges. This study investigated efficacy and safety of aztreonam-avibactam (ATM-AVI) in the treatment of complicated intra-abdominal infection (cIAI) or hospital-acquired)/ventilator-associated pneumonia (HAP/VAP) due to Gram-negative bacteria, including MBL-producing MDR pathogens, with limited or no treatment options. Methods REVISIT was a phase 3, prospective, randomized, multicenter, open-label, central assessor-blinded study in hospitalized adults. Patients were randomized 2:1 to ATM-AVI (± metronidazole [MTZ]; cIAI patients only) or meropenem (MER) ± colistin (COL) for 5–14 (cIAI) or 7–14 (HAP/VAP) days. Clinical cure at the test-of-cure (TOC) visit in the intent-to-treat (ITT) and clinically evaluable (CE) analysis sets were the primary efficacy endpoints. Key secondary endpoints included microbiological responses at TOC, 28-day mortality, and safety. No formal hypothesis testing was planned. Results In total, 422 patients were randomized (ATM-AVI ± MTZ, n=282; MER ± COL, n=140). Adjudicated clinical cure rates at TOC are shown in Table 1. Favorable microbiological response rates at TOC (micro-ITT analysis set) were 75.7% for ATM-AVI ± MTZ and 73.9% for MER ± COL; 28-day all-cause mortality rates for ATM-AVI ± MTZ and MER ± COL were 1.9% (4/208) vs 2.9% (3/104), and 10.8% (8/74) vs 19.4% (7/36) in cIAI and HAP/VAP, respectively. Adverse events (AEs) are summarized in Table 2. There were no treatment-related serious AEs in the ATM-AVI group. Conclusion ATM-AVI (± MTZ) was effective in treating patients with cIAI and HAP/VAP, displaying similar efficacy to MER ± COL. ATM-AVI was generally well tolerated. These data support potential use of ATM-AVI for the treatment of serious infections caused by susceptible Gram-negative bacteria. Further analyses will focus on MBL-producing pathogens. Trial registration. NCT03329092. Study sponsored by Pfizer. ATM-AVI is jointly developed with AbbVie, also supported by the United States Biomedical Advanced Research and Development Authority (BARDA) and the European Innovative Medicines Initiative (IMI), under the COMBACTE-CARE consortium. Disclosures Yehuda Carmeli, MD, Merck: Advisor/Consultant|Merck: Grant/Research Support|Merck: Honoraria|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Pfizer: Honoraria|Qpex: Advisor/Consultant|Qpex: Grant/Research Support|Qpex: Honoraria|Roche: Advisor/Consultant|Roche: Grant/Research Support|Roche: Honoraria Georgios L. Daikos, PhD, MSD: Honoraria|Pfizer: Advisor/Consultant|Pfizer: Honoraria|Viatris: Honoraria Rosa-María Jiménez Rodríguez, MD, PhD, Abex: Honoraria|B. Braun: Honoraria|Johnson & Johnson: Honoraria Halley Rogers, MPH, Pfizer: Ownership Interest Michele Wible, MS, Pfizer: Ownership Interest Francis Arhin, PhD, Pfizer: Ownership Interest Joanne Leaney, PhD, Pfizer: Ownership Interest Rienk Pypstra, MD, MBA, Pfizer: Stocks/Bonds Joseph Chow, MD, Pfizer: Ownership Interest
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".