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Record W1485888796 · doi:10.1155/2011/971701

Pharmacodynamic Profiling of Antimicrobials against Gram‐negative Respiratory Isolates from Canadian Hospitals

2011· article· en· W1485888796 on OpenAlexafffundabout
Rebecca A. Keel, George G. Zhanel, Sheryl Zelenitsky, David P. Nicolau

Bibliographic record

VenueCanadian Journal of Infectious Diseases and Medical Microbiology · 2011
Typearticle
Languageen
FieldMedicine
TopicAntibiotics Pharmacokinetics and Efficacy
Canadian institutionsUniversity of Manitoba
FundersJanssen PharmaceuticalsMerck CanadaAstraZeneca
KeywordsPharmacodynamicsGramMedicineAntimicrobialProfiling (computer programming)MicrobiologyIntensive care medicineBiologyPharmacologyBacteriaComputer sciencePharmacokinetics

Abstract

fetched live from OpenAlex

The objective of this study was to assess the profile of a variety of dosing regimens for common intravenous antibiotics against contemporary Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa isolates collected in Canada during 2009, using pharmacodynamic modelling techniques. Monte Carlo simulation was conducted for standard and/or prolonged infusion regimens of cefepime, ceftazidime, ceftriaxone, ciprofloxacin, doripenem, ertapenem, meropenem and piperacillin/tazobactam. The cumulative fraction of response (CFR) was calculated using bactericidal targets for each regimen against each species. All cefepime, doripenem, ertapenem and meropenem regimens achieved optimal exposures against Enterobacteriaceae, whereas target attainment was organism and dose dependent for the other agents. These results support that the currently recommended antimicrobial dosing regimens generally attain acceptable exposures to achieve the requisite pharmacodynamic targets against the Enterobacteriaceae species; however, they fall short of obtaining optimal bactericidal exposures against P aeruginosa. BACKGROUND: With diminishing antimicrobial potency, the choice of effective empirical therapy has become more challenging. Thus, the pharmacodynamic evaluation of potential therapies is essential to identify optimal agents, doses and administration strategies. METHODS: Monte Carlo simulation was conducted for standard and/or prolonged infusion regimens of cefepime, ceftazidime, ceftriaxone, ciprofloxacin, doripenem, ertapenem, meropenem and piperacillin/tazobactam. Minimum inhibitory concentrations were obtained for Escherichia coli (n=64 respiratory isolates), Enterobacter cloacae (n=53), Klebsiella pneumoniae (n=75) and Pseudomonas aeruginosa (n=273) throughout Canada. The cumulative fraction of response (CFR) was calculated using bactericidal targets for each regimen against each species. A CFR ≥90% was defined as optimal. RESULTS: All cefepime, doripenem, ertapenem and meropenem regimens achieved optimal exposures against Enterobacteriaceae, whereas target attainment was organism and dose dependent for the other agents. Prolonged infusion doripenem and meropenem 1 g and 2 g every 8 h, along with standard infusion doripenem and meropenem 2 g every 8 h, were the only regimens to attain optimal exposures against P aeruginosa. Ciprofloxacin had the lowest CFR against P aeruginosa, followed by cefepime. Among the P aeruginosa isolates collected in the intensive care unit (ICU) compared with the wards, differences of 0.5% to 10% were noted in favour of non‐ICU isolates for all agents; however, marked differences (10% to 15%) in CFR were observed for ciprofloxacin in favour of ICU isolates. CONCLUSION: Standard dosing of cefepime, doripenem, ertapenem and meropenem has a high likelihood of obtaining optimal pharmacodynamic indexes against these Enterobacteriaceae. For P aeruginosa, aggressive treatment with high‐dose and/or prolonged infusion regimens are likely required to address the elevated resistance rates of respiratory isolates from Canada.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations6
Published2011
Admission routes3
Has abstractyes

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