Augmented Renal Clearance in Critical Illness: An Important Consideration in Drug Dosing
Bibliographic record
Abstract
Augmented renal clearance (ARC) is a manifestation of enhanced renal function seen in critically ill patients. The use of regular unadjusted doses of renally eliminated drugs in patients with ARC might lead to therapy failure. The purpose of this scoping review was to provide and up-to-date summary of the available evidence pertaining to the phenomenon of ARC. A literature search of databases of available evidence in humans, with no language restriction, was conducted. Databases searched were MEDLINE (1946 to April 2017), EMBASE (1974 to April 2017) and the Cochrane Library (1999 to April 2017). A total of 57 records were included in the present review: 39 observational studies (25 prospective, 14 retrospective), 6 case reports/series and 12 conference abstracts. ARC has been reported to range from 14-80%. ARC is currently defined as an increased creatinine clearance of greater than 130 mL/min/1.73 m² best measured by 8-24 h urine collection. Patients exhibiting ARC tend to be younger (<50 years old), of male gender, had a recent history of trauma, and had lower critical illness severity scores. Numerous studies have reported antimicrobials treatment failures when using standard dosing regimens in patients with ARC. In conclusion, ARC is an important phenomenon that might have significant impact on outcome in critically ill patients. Identifying patients at risk, using higher doses of renally eliminated drugs or use of non-renally eliminated alternatives might need to be considered in ICU patients with ARC. More research is needed to solidify dosing recommendations of various drugs in patients with ARC.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".