Pharmacokinetics and Pharmacodynamics of Meropenem in Febrile Neutropenic Patients with Bacteremia
Bibliographic record
Abstract
BACKGROUND: Pharmacodynamic investigations with antimicrobials define the relationship between the infecting organism and achievable drug concentrations with clinical outcome. OBJECTIVE: To examine this relationship for meropenem in a population of patients who are at high risk of infection-related morbidity and mortality. METHODS: The study was a retrospective analysis of a multicenter, randomized, blinded clinical trial. A population-based predictive model was created using data from adults with febrile neutropenia and the nonparametric modeling program, NPEM. Patient age, body weight, and serum creatinine level were covariates in the model used to predict unbound concentrations for each patient. Pathogen susceptibility was estimated using product literature minimum inhibitory concentrations for effectiveness against 50% of microorganisms (MIC50) for specific organisms. The pharmacodynamic index of percent time above MIC (% T>MIC) was analyzed for its association with clinical outcome. RESULTS: A 2-compartment pharmacokinetic model using patient covariates of body weight and renal function best described the pharmacokinetics of meropenem in febrile neutropenic patients. Sixty patients with confirmed gram-positive or -negative bacteremia were studied. An average of 83% T>MIC was identified for the 42 clinical responders compared with 59% T>MIC for the 18 nonresponders (p = 0.04). An 80% clinical response rate was evident when the % T>MIC for meropenem exceeded 75% of the dosing interval (p = 0.01). CONCLUSIONS: To our knowledge, this is the first published report of a relationship between a pharmacodynamic index and clinical outcome in a febrile neutropenic population. Based on this relationship, dosing with intravenous meropenem 500 mg every 6 hours is predicted to be comparable to the currently recommended 1 g every 8 hours for serious infections. Our model provides further justification for a prospective clinical trial to evaluate a pharmacodynamically targeted meropenem dosing schedule as to its ability to improve clinical outcome in these patients.
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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 | 0.002 | 0.006 |
| 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) | 0.000 | 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".