<i>Acinetobacter baumannii</i> treatment strategies: a review of therapeutic challenges and considerations
Bibliographic record
Abstract
ABSTRACT Antimicrobial resistance poses a major challenge in the treatment of Acinetobacter baumannii. Acinetobacter spp. are intrinsically resistant to a number of commonly used antibiotics. Over the past 3 years, the European and American Professional Societies have provided important guidelines on the treatment options for carbapenem-resistant A. baumannii (CRAB). Here, we review the recent literature on combination regimens for CRAB as well as carbapenem-susceptible A. baumannii infections. We discuss the strengths and weaknesses of various agents used in combination, depending on the site of infection and their pharmacokinetic properties. Consistent with the 2024 Infectious Diseases of America (IDSA) update, sulbactam-durlobactam, in combination with background carbapenem therapy, remains the combination with the greatest reduction in mortality for pulmonary infections and has promising outcomes in bloodstream infections with CRAB. Sulbactam-based combination therapy remains an ideal part of targeted strategies and has been shown to be associated with reduced mortality. Certain agents have been highlighted in the literature for suboptimal outcomes, primarily pulmonary infections treated with cefiderocol, tigecycline, and eravacycline. Studies including non-pulmonary infections, specifically bacteremia and central nervous system (CNS) infections, are overall limited to case series and subgroup analyses. Important areas for further research include breakpoint evaluations for eravacycline and minocycline as well as subclinical resistance in cefiderocol.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".