Acute Kidney Injury and Renal Recovery with the Use of Aminoglycosides: A Large Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Recent acute kidney injury (AKI) guidelines, based on studies performed a decade ago, recommend avoiding aminoglycosides (AGs) in patients at risk of AKI. Whether present patient characteristics and management have changed this risk is uncertain. We determined the current incidence, risk factors and outcomes of AG-AKI. METHODS: We retrospectively identified adult patients who received gentamicin or tobramycin for ≥5 days in 2 large university-affiliated centers, excluding critically ill and dialysis patients. We assessed the incidence of Risk, Injury, Failure, Loss and End-stage kidney disease criteria of AKI risk and then matched each AKI to 2 controls of same age and gender to determine factors associated with AG-AKI and its recovery, defined by a creatinine within 150% of baseline by 21 days. RESULTS: Since 2001, the frequency of AG administration and dosing declined, but the incidence of AG-AKI remained constant. Of the 562 patients who received AG for ≥5 days, 65 (12%) developed AG-AKI after 11 (IQR 8-15) days, with 56, 29 and 15% having stages 1, 2 and 3 AKI, respectively. We matched these to 130 controls. In this nested case-control study, independent AKI risk factors were vancomycin coadministration, high AG trough levels and heart failure. AG-AKI compared to AG exposure without AKI was associated with greater mortality. Renal recovery occurred in 51% of the AKI patients and was less likely with heart failure and higher AKI severity. CONCLUSION: AG administration has recently decreased but the risk of AKI remained unchanged and half of the patients did not recover. Vancomycin coadministration, high AG trough levels and heart failure independently predicted AKI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".