Therapeutic drug monitoring-guided piperacillin dosing in critically ill patients undergoing continuous renal replacement therapy: a systematic review
Bibliographic record
Abstract
BACKGROUND: Continuous renal replacement therapy (CRRT) complicates antibiotic dosing in critically ill patients due to altered pharmacokinetics. The optimal dosing of piperacillin remains unclear. Therapeutic drug monitoring (TDM) can personalize piperacillin therapy and improve outcomes. OBJECTIVES: This review investigates the effects of TDM-guided piperacillin dosing on pharmacokinetic target attainment and clinical outcomes in CRRT patients, analyses correlations with clinical outcomes, provides optimal dosing strategies for piperacillin and identifies future research areas. METHODS: A systematic search of PubMed, Scopus and Web of Science was conducted until December 2023, identifying studies on piperacillin pharmacokinetics and clinical outcomes in adult CRRT patients. Data on study characteristics, piperacillin exposures, TDM use, target attainment rates, mortality and length of stay were extracted. The risk of bias was assessed using the Newcastle-Ottawa Scale. RESULTS: Eleven observational studies were included. High pharmacokinetic variability was evident, with piperacillin target non-attainment in up to 74% of cases without TDM. Two studies with routine TDM showed increased target attainment rates of 80%-100%. Mortality ranged from 17% to 56%, with supratherapeutic concentrations (≥100 mg/L) associated with higher mortality. The impact of optimized piperacillin exposures on outcomes was inconclusive. Most studies demonstrated a low risk of bias. CONCLUSIONS: TDM-guided piperacillin dosing in CRRT patients improved target attainment rates (≥80%). Mortality rates ranged from 17% to 56%, with inconsistent correlations between drug exposures and survival. Supratherapeutic concentrations were linked to higher mortality. Standardized TDM protocols are needed. Future research should establish clear exposure-response relationships and the impact of TDM on clinical outcomes.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".