Global update on the in vitro activity of tigecycline and comparators against isolates of Acinetobacter baumannii and rates of resistant phenotypes (2016–2018)
Bibliographic record
Abstract
OBJECTIVES: This study presents 2016-2018 in vitro antimicrobial activity data and rates of resistant phenotypes for clinical isolates of Acinetobacter baumannii from Africa/Middle East, Asia/South Pacific, Europe, Latin America, and North America. METHODS: A total of 4320 A. baumannii isolates were collected across all regions between 2016 and 2018. The in vitro antimicrobial activities of amikacin, colistin, levofloxacin, meropenem, and tigecycline were determined using the broth microdilution methodology of the Clinical and Laboratory Standards Institute. MICs were interpreted using the European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints (version 11.0). Rates of subsets that were resistant to amikacin, colistin, levofloxacin, and meropenem, according to EUCAST breakpoints, are also presented. RESULTS: values, 1 or 2 mg/L) and the lowest rate of resistance was to colistin (1.2%-7.3%). The rates of resistance to the panel of agents were generally lower among A. baumannii from North America (1.3%-42.7%), compared with the other regions. Fewer than 11% of meropenem-resistant A. baumannii were also resistant to colistin. The rates of amikacin-, levofloxacin- and meropenem-resistant A. baumannii were lowest in North America and mostly higher in Africa/Middle East and Latin America. CONCLUSION: In each geographical region, tigecycline and colistin maintained good in vitro antimicrobial activity against isolates of A. baumannii, including antimicrobial-resistant subsets. The higher rates of meropenem-resistant isolates, particularly in Africa/Middle East and Latin America, require continued monitoring because of the scarcity of effective treatment options.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".