Practical considerations for individualizing drug dosing in critically ill adults receiving renal replacement therapy
Bibliographic record
Abstract
Critically ill patients with sepsis admitted to the intensive care unit (ICU) often present with or develop renal dysfunction requiring renal replacement therapy (RRT) in addition to antimicrobial therapy. While early and appropriate antimicrobials for sepsis have been associated with an increased probability of survival, adequate dosing is also required in these patients. Adequate dosing of antimicrobials refers to dosing strategies that achieve serum drug levels at the site of infection that are able to provide a microbiological and/or clinical response while avoiding toxicity from excessive antibiotic exposure. Therapeutic drug monitoring (TDM) is the recommended strategy to achieve this goal, however, TDM is not routinely available in all ICUs and for all antimicrobials. In the absence of TDM, clinicians are therefore required to make dosing decisions based on the clinical condition of the patient, the causative organism, the characteristics of RRT, and an understanding of the physicochemical properties of the antimicrobial. Pharmacokinetics (PK) of antimicrobials can be highly variable between critically ill patients and also within the same patient over the course of their ICU stay. The initiation of RRT, which can be in the form of intermittent hemodialysis, continuous, or prolonged intermittent therapy, further complicates the predictability of drug disposition. This variability highlights the need for individualized dosing. This review highlights the practical considerations for the clinician for antimicrobial dosing in critically ill patients receiving RRT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".