The Effect of Renal Replacement Therapy and Antibiotic Dose on Antibiotic Concentrations in Critically Ill Patients: Data From the Multinational Sampling Antibiotics in Renal Replacement Therapy Study
Bibliographic record
Abstract
BACKGROUND: The optimal dosing of antibiotics in critically ill patients receiving renal replacement therapy (RRT) remains unclear. In this study, we describe the variability in RRT techniques and antibiotic dosing in critically ill patients receiving RRT and relate observed trough antibiotic concentrations to optimal targets. METHODS: We performed a prospective, observational, multinational, pharmacokinetic study in 29 intensive care units from 14 countries. We collected demographic, clinical, and RRT data. We measured trough antibiotic concentrations of meropenem, piperacillin-tazobactam, and vancomycin and related them to high- and low-target trough concentrations. RESULTS: We studied 381 patients and obtained 508 trough antibiotic concentrations. There was wide variability (4-8-fold) in antibiotic dosing regimens, RRT prescription, and estimated endogenous renal function. The overall median estimated total renal clearance (eTRCL) was 50 mL/minute (interquartile range [IQR], 35-65) and higher eTRCL was associated with lower trough concentrations for all antibiotics (P < .05). The median (IQR) trough concentration for meropenem was 12.1 mg/L (7.9-18.8), piperacillin was 78.6 mg/L (49.5-127.3), tazobactam was 9.5 mg/L (6.3-14.2), and vancomycin was 14.3 mg/L (11.6-21.8). Trough concentrations failed to meet optimal higher limits in 26%, 36%, and 72% and optimal lower limits in 4%, 4%, and 55% of patients for meropenem, piperacillin, and vancomycin, respectively. CONCLUSIONS: In critically ill patients treated with RRT, antibiotic dosing regimens, RRT prescription, and eTRCL varied markedly and resulted in highly variable antibiotic concentrations that failed to meet therapeutic targets in many 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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".