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Record W4319812222 · doi:10.1099/jmm.0.001645

In vitro activity of imipenem/relebactam against piperacillin/tazobactam-resistant and meropenem-resistant non-Morganellaceae Enterobacterales and Pseudomonas aeruginosa collected from patients with bloodstream, intra-abdominal and urinary tract infections in Western Europe: SMART 2018–2020

2023· article· en· W4319812222 on OpenAlexaff
James A. Karlowsky, Sibylle Lob, Fakhar Siddiqui, Brune Akrich, C Andrew DeRyke, Katherine Young, Mary Motyl, Stephen Hawser, Daniel F. Sahm

Bibliographic record

VenueJournal of Medical Microbiology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsImipenemPiperacillinMeropenemMicrobiologyTazobactamPseudomonas aeruginosaBroth microdilutionMedicinePiperacillin/tazobactamAmikacinCarbapenemAntibiotic resistanceBiologyAntibioticsMinimum inhibitory concentrationBacteria

Abstract

fetched live from OpenAlex

Introduction. Piperacillin/tazobactam and carbapenems are important agents for the treatment of serious Gram-negative infections in hospitalized patients. Resistance to both agents is a significant concern in clinical isolates of Enterobacterales and Pseudomonas aeruginosa ; new agents with improved activity are needed. Gap Statement. Publication of current, region-specific data describing the in vitro activity of newer agents such as imipenem/relebactam (IMR) against piperacillin/tazobactam-resistant and carbapenem-resistant Enterobacterales and P. aeruginosa are needed to support their clinical use. Aim. To describe the in vitro activity of IMR against non- Morganellaceae Enterobacterales (NME) and P. aeruginosa isolated from bloodstream, intra-abdominal and urinary tract infection samples by hospital laboratories in Western Europe with a focus on the activity of IMR against piperacillin/tazobactam-resistant and meropenem-resistant isolates. Methodology. From 2018 to 2020, 29 hospital laboratories in six countries in Western Europe participated in the SMART global surveillance programme and contributed 9487 NME and 1004 P . aeruginosa isolates. MICs were determined by CLSI broth microdilution testing and interpreted by EUCAST (2021) breakpoints. β-Lactamase genes were identified in selected isolate subsets (2018–2020) and oprD sequenced in molecularly characterized P. aeruginosa (2020). Results. IMR (99.4 % susceptible), amikacin (98.0 %), meropenem (97.7 %) and imipenem (97.6 %) were the most active agents against NME; 83.1 % of NME were piperacillin/tazobactam-susceptible. Relebactam increased imipenem susceptibility of NME from Italy by 8.3 %, from Portugal by 2.9 %, and from France, Germany, Spain and the UK by <1 %. In total, 96.4 % of piperacillin/tazobactam-resistant ( n =1601) and 73.7 % of meropenem-resistant ( n =152) NME were IMR-susceptible. Also, 0.4 % of NME were MBL-positive, 0.9 % OXA-48-like-positive (MBL-negative) and 1.5 % KPC-positive (MBL-negative). Amikacin (95.4 % susceptible) and IMR (94.1 %) were the most active agents against P. aeruginosa ; 81.7 % of isolates were imipenem-susceptible and 79.6 % were piperacillin/tazobactam-susceptible. Relebactam increased susceptibility to imipenem by 12.5 % overall (range by country, 4.3–17.5 %); and by 30.7 % in piperacillin/tazobactam-resistant and 24.3 % in meropenem-resistant P. aeruginosa . In total, 1.6 % of P. aeruginosa isolates were MBL-positive. Seven of eight molecularly characterized IMR-resistant P. aeruginosa isolates from 2020 were oprD -deficient. Conclusion. IMR may be a potential treatment option for bloodstream, intra-abdominal and urinary tract infections caused by NME and P. aeruginosa in Western Europe, including infections caused by piperacillin/tazobactam-resistant and meropenem-resistant isolates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.221
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations4
Published2023
Admission routes1
Has abstractyes

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