Association between piperacillin/tazobactam use and acute kidney injury in critically ill patients: a retrospective multicentre cohort study
Bibliographic record
Abstract
BACKGROUND: Piperacillin/tazobactam is one of the most common antibiotics prescribed in the ICU and the combination of piperacillin/tazobactam with vancomycin has been associated with acute kidney injury (AKI) in critically ill patients. However, data on the risk of AKI with piperacillin/tazobactam, despite vancomycin co-exposure, are lacking. OBJECTIVES: To investigate the association of piperacillin/tazobactam with AKI and renal replacement therapy (RRT) among adult ICU patients. METHODS: We analysed data from patients included in two open access databases (MIMIC-IV and eICU). Critically ill patients who received piperacillin/tazobactam or cefepime (a cephalosporin with similar broad-spectrum activity to piperacillin/tazobactam) during their first ICU stay were eligible for the study. Marginal structural Cox models, accounting for time-fixed covariates and time-dependent covariates were performed. The primary outcomes were AKI and need of RRT. RESULTS: A total of 20 107 patients were included, with 11 213 in the piperacillin/tazobactam group and 8894 in the cefepime group. Exposure to piperacillin/tazobactam was associated with AKI (HR 1.77; 95% CI 1.51-2.07; P < 0.001) and with need of RRT (HR 1.31; 95% CI 1.08-1.57; P = 0.005). Tests for interaction were not statistically significant for occurrence of AKI and RRT in the subgroup of patients exposed to vancomycin or not (P = 0.26 and P = 0.6, respectively). CONCLUSIONS: In critically ill patients, exposure to piperacillin/tazobactam was associated with increased risk of AKI and with increased risk of RRT, regardless of combination therapy with vancomycin.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".