Activity of ceftolozane/tazobactam and imipenem/relebactam against clinical isolates of Enterobacterales and Pseudomonas aeruginosa collected in Greece and Italy—SMART 2017–2021
Bibliographic record
Abstract
PURPOSE: The current study evaluated the in vitro activities of ceftolozane/tazobactam (C/T), imipenem/relebactam (IMI/REL), and comparators against recent (2017-2021) clinical isolates of gram-negative bacilli from two countries in southern Europe. METHODS: Nine clinical laboratories (two in Greece; seven in Italy) each collected up to 250 consecutive gram-negative isolates per year from lower respiratory tract, intraabdominal, urinary tract, and bloodstream infection samples. MICs were determined by the CLSI broth microdilution method and interpreted using 2022 EUCAST breakpoints. β-lactamase genes were identified in select β-lactam-nonsusceptible isolate subsets. RESULTS: C/T inhibited the growth of 85-87% of Enterobacterales and 94-96% of ESBL-positive non-CRE NME (non-Morganellaceae Enterobacterales) isolates from both countries. IMI/REL inhibited 95-98% of NME, 100% of ESBL-positive non-CRE NME, and 98-99% of KPC-positive NME isolates from both countries. Country-specific differences in percent susceptible values for C/T, IMI/REL, meropenem, piperacillin/tazobactam, levofloxacin, and amikacin were more pronounced for Pseudomonas aeruginosa than Enterobacterales. C/T and IMI/REL both inhibited 84% of P. aeruginosa isolates from Greece and 91-92% of isolates from Italy. MBL rates were estimated as 4% of Enterobacterales and 10% of P. aeruginosa isolates from Greece compared to 1% of Enterobacterales and 3% of P. aeruginosa isolates from Italy. KPC rates among Enterobacterales isolates were similar in both countries (7-8%). OXA-48-like enzymes were only identified in Enterobacterales isolates from Italy (1%) while GES carbapenemase genes were only identified in P. aeruginosa isolates from Italy (2%). CONCLUSION: We conclude that C/T and IMI/REL may provide viable treatment options for many patients from Greece and Italy.
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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 | 0.001 |
| 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 | 0.000 |
| 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".