Peri-operative pharmacokinetics of cefazolin prophylaxis during valve replacement surgery
Bibliographic record
Abstract
OBJECTIVE: There is little prospective data to guide effective dosing for antibiotic prophylaxis during surgery requiring cardiopulmonary bypass (CPB). We aim to describe the effects of CPB on the population pharmacokinetics (PK) of total and unbound concentrations of cefazolin and to recommend optimised dosing regimens. METHODS: Patients undergoing CPB for elective cardiac valve replacement were included using convenience sampling. Intravenous cefazolin (2g) was administered pre-incision and re-dosed at 4 hours. Serial blood and urine samples were collected and analysed using validated chromatography. Population PK modelling and Monte-Carlo simulations were performed using Pmetrics® to determine the fractional target attainment (FTA) of achieving unbound concentrations exceeding pre-defined exposures against organisms known to cause surgical site infections for 100% of surgery (100% fT>MIC). RESULTS: From the 16 included patients, 195 total and 64 unbound concentrations of cefazolin were obtained. A three-compartment linear population PK model best described the data. We observed that cefazolin 2g 4-hourly was insufficient to achieve the FTA of 100% fT>MIC for Staphylococcus aureus and Escherichia coli at serum creatinine concentrations ≤ 50 μmol/L and for Staphylococcus epidermidis at any of our simulated doses and serum creatinine concentrations. A dose of cefazolin 3g 4-hourly demonstrated >93% FTA for S. aureus and E. coli. CONCLUSIONS: We found that cefazolin 2g 4-hourly was not able to maintain concentrations above the MIC for relevant pathogens in patients with low serum creatinine concentrations undergoing cardiac surgery with CPB. The simulations showed that optimised dosing is more likely with an increased dose and/or dosing frequency.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 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".