Antimicrobial Activity of Ceftazidime–Avibactam and Comparators Against Fluoroquinolone-Resistant <i>Klebsiella pneumoniae</i> Collected Globally from Antimicrobial Testing Leadership and Surveillance: 2018–2019
Bibliographic record
Abstract
This study assessed the in vitro antimicrobial activity of ceftazidime–avibactam (CAZ-AVI) and a panel of comparator agents, including aztreonam, cefepime, ceftazidime, meropenem, imipenem, colistin, piperacillin–tazobactam, and tigecycline against isolates of fluoroquinolone-resistant (FQ-R) Klebsiella pneumoniae collected in 2018 and 2019 from the Antimicrobial Testing Leadership and Surveillance (ATLAS) program. Susceptibility and minimum inhibitory concentration were determined using broth microdilution for all antimicrobial agents by a central reference laboratory according to the Clinical and Laboratory Standards Institute guidelines and European Committee on Antimicrobial Susceptibility Testing guidelines. Of all the K. pneumoniae isolates ( n = 10,906), 44.1% (4,814/10,906) were FQ-R. Of these, 71.3% (3,432/4,814) were extended-spectrum β-lactamase (ESBL)-positive, and 10.4% (499/4,814) were CAZ-AVI-resistant. CAZ-AVI showed high susceptibility (>87%) against all the FQ-R K. pneumoniae isolates. However, metallo- β-lactamase-positive isolates showed low susceptibility (3.8%; 18/470) to CAZ-AVI. Among the different geographical regions, CAZ-AVI showed the highest activity against isolates collected from North America (98.2%, 216/220) and lowest against those collected from Asia Pacific (APAC) (81.7%; 882/1,079). Among comparator agents, carbapenems showed a relatively lower susceptibility (<71.5%), while only tigecycline and colistin were active (>85%) across all isolates. In conclusion, CAZ-AVI may be a potential treatment option for FQ-R K. pneumoniae isolates. However, increasing CAZ-AVI resistance among ESBL-positive and metallo-β-lactamase-positive isolates and in isolates from APAC warrants continuous surveillance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".