Potential benefits of therapeutic drug monitoring for beta-lactam antibiotics in augmented renal clearance patients: a case report
Bibliographic record
Abstract
Augmented renal clearance (ARC) is commonly described in critically ill patients, making drug pharmacokinetics even harder to predict in this population. This case report displays the value of therapeutic drug monitoring (TDM) of piperacillin/tazobactam (PTZ) in this population. We identified two patients with ARC and intermittent administration of PTZ who took part in a prospective, descriptive study conducted at Hôpital du Sacré-Cœur de Montréal. Both had plasma samples drawn at peak, middle, and end of their dosing intervals of PTZ. Minimal inhibitory concentrations (MICs) of 4 and 8 mg/L were chosen to evaluate therapeutic target attainment at middle and end of dosing interval. The first patient was a 52-year-old male with a renal clearance rate estimated at 147 mL/min who received 3.375 g PTZ every 6 h. The second patient, a 49-year-old male, had an estimated renal clearance rate of 163 mL/min and received the same regimen. Both patients had piperacillin concentrations above the target MICs at middle of the dosing interval, but they failed to reach a trough concentration above 8 mg/L. The present case report showcases two patients with subtherapeutic PTZ concentrations despite strict following of local administration protocols. This suboptimal administration could not only lead to treatment failure, but also to the selection and growth of resistant pathogens. Implementing TDM would offer the possibility to adjust drug regimens in real-time and prevent situations like these from occurring.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".