Population pharmacokinetic models of first choice beta-lactam antibiotics for severe infections treatment: What antibiotic regimen to prescribe in children?
Bibliographic record
Abstract
BACKGROUND: To perform a review describing the pharmacokinetic (PK) parameters and covariates of interest of the eight first choice β-lactams (BL) antibiotics for treatment of severe infections in pediatric population. Pediatric sepsis and septic shock reportedly affect 30% of children admitted to pediatric intensive care units, with a 25% mortality rate. Eight BL are included as first choice antibiotic for severe infections in pediatric population in the World Health Organization model list of essential medicines for children. METHODS: The PubMed/Medline databases was searched and included studies if they described a population PK model of piperacillin, amoxicillin, ampicillin, cefotaxime, ceftriaxone, cloxacillin, imipenem or meropenem in neonates or children. We compared the PK parameters for each drug. We analysed the used covariates to estimate PK parameters. We compared the pharmacokinetics/pharmacodynamics (PK/PD) targets and the drug dosing recommendations. RESULTS: Thirty-four studies met inclusion criteria with seven studies for piperacillin, five for amoxicillin, three for ampicillin, three for cefotaxime, two for ceftriaxone, two for imipenem and twelve for meropenem. None met inclusion criteria for cloxacillin. Ages ranged from 0-19.1 years with 12 studies including preterm. Body weight, age and renal function were the three major covariates in neonates and children. Different PK/PD targets were observed (between 40% to 100% of the dosing regimen interval of time over which the unbound (or free) drug concentration remains above the minimal inhibitory concentration (MIC) (fT>MIC) or four times the MIC (fT>4xMIC)). Several drug-dosing regimens were fond recommended according to the age and pathogens MIC using intermittent, timed or continuous infusions. CONCLUSIONS: Consensus is lacking on the optimal dosing regimens for these eight first choice antibiotics. A more personalized approach to antibiotic drugs dosing with individual characteristics of patient and pathogen susceptibility is required. According PK/PD targets and used dosing regimens, prospective clinical studies are required to investigate clinical cure, patient survival and emergence of antimicrobial resistance.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".