Antimicrobial Dosing Recommendations in Pediatric Continuous Renal Replacement Therapy: A Critical Appraisal of Current Evidence
Bibliographic record
Abstract
Objectives: Continuous renal replacement therapy (CRRT) is commonly employed in critically ill children and is known to affect antimicrobial pharmacokinetics. There is a lack of readily available, evidence-based antimicrobial dosing recommendations in pediatric CRRT. This study aims to quantify commonly used antimicrobial drugs in pediatric CRRT and identify gaps between contemporary literature-based dosing recommendations and those presented in a frequently used dosing reference. Methods: The Pediatric Health Information System (PHIS) database was queried from July 1, 2018 through June 30, 2021 to identify admissions in which antimicrobials were billed on the same day as CRRT. Drugs of interest were selected if at least 10% of admission involved administration on at least one CRRT day, with additional clinically important antimicrobials selected by the authors. A comprehensive literature search was performed to identify antimicrobial pharmacokinetic (PK) studies in children for each selected drug. For identified articles, dosing recommendations were extracted and compared to those in a popular tertiary dosing reference (Lexi-Comp Online database). The level of agreement of the dosing recommendations was assessed. Results: 77 unique antimicrobial agents were identified amongst 812 admissions from 20 different PHIS hospitals. Fifteen antimicrobials were billed on the same day as CRRT in ≥10% of admissions, with 4 additional drugs deemed clinically relevant by the authors. Twenty PK studies were identified for these 19 drugs, and dosing recommendations were included in 8 (42.1%) of them. Seventeen agents (89.5%) had some type of CRRT-specific dosing guidance in Lexi-Comp, with only 1 directly based on a pediatric CRRT study. For the 8 agents with PK data available, Lexi-Comp recommendations matched primary literature dosing guidance in 3 (37.5%). Two (25%) lacked agreement between the Lexi-Comp and primary literature, and the remaining 3 (37.5%) had partial agreement with multiple dosing regimens suggested in the primary literature and at least one of these regimens recommended by Lexi-Comp. Conclusion: Significant gaps exist in the data supporting antimicrobial dosing recommendations for children receiving CRRT. Future studies should focus on antimicrobial dosing in pediatric CRRT, emphasizing provision of robust data from which dosing recommendations can be promptly incorporated into tertiary dosing references.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".