Meropenem and piperacillin/tazobactam optimised dosing regimens for critically ill patients receiving renal replacement therapy
Bibliographic record
Abstract
PURPOSE: Optimal dosing of meropenem and piperacillin/tazobactam in critically ill patients receiving renal replacement therapy (RRT) is uncertain due to variable pharmacokinetics. We aimed to develop generalisable optimised dosing recommendations for these antibiotics. METHODS: Prospective, multinational pharmacokinetic study including patients requiring various forms of RRT. Independent population PK models were developed, externally validated and applied to perform Monte Carlo dosing simulations using Monolix and Simulx. We calculated the probability that these dosing regimens achieved standard and high therapeutic unbound antibiotic concentrations over 100% of the dosing interval for the treatment of Enterobacterales and Pseudomonas aeruginosa. RESULTS: We enrolled 300 patients from 22 intensive care units across 12 countries receiving continuous veno-venous haemodialysis (13.0%), haemofiltration (23.3%), haemodiafiltration (48.4%) or sustained low-efficiency dialysis (15.3%). Models were developed using data from 234 patients (8322 samples) and validated with 66 additional patients (560 samples). Predictive performance was high, with mean prediction errors of - 5.2% for meropenem and - 16.9% for piperacillin. Dosing simulations showed that meropenem and piperacillin/tazobactam dosing requirements were dependent on urine output and RRT intensity and duration (p < 0.05). In all scenarios, extended/continuous infusions led to a better achievement of effective concentrations with lower daily doses compared to short infusion. Dosing nomograms were developed to inform dosing for different RRT settings, urine outputs, and target concentrations. CONCLUSION: RRT intensity and duration and urine output determine meropenem and piperacillin/tazobactam dosing requirements in critically ill patients receiving RRT. Extended/continuous infusions facilitate the attainment of effective concentrations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".