Nonsteroidal anti-inflammatory drug use and acute kidney injury in nephrectomies: A retrospective propensity score-matched cohort study
Bibliographic record
Abstract
INTRODUCTION: Nonsteroidal anti-inflammatory drugs (NSAID) are analgesic and spare opioids, but it remains unclear whether perioperative NSAID use worsens renal function after nephrectomy. We therefore tested the hypothesis that perioperative use of NSAID is associated with acute kidney injury (AKI) after nephrectomy surgery. METHODS: This retrospective cohort study included patients ≥18 years old who had partial or radical nephrectomies. Patients who were given intravenous NSAIDs for postoperative analgesia were defined as one group, whereas reference patients did not use any NSAIDs. The primary outcome was the occurrence of postoperative acute kidney injury (AKI), as defined by the Kidney Disease: Improving Global Outcomes criteria. Secondary outcomes included AKI stage, NSAID-related side effects, postoperative hemoglobin, cumulative opioid consumption, and duration of hospitalization. RESULTS: Among 3,359 eligible nephrectomy patients, 78% (2,614) were given NSAIDs. We propensity-score-matched 739 pairs of patients who were or were not given NSAIDs. Patients in the NSAID group did not have more AKI (27.6%vs. 27.9%, HR 0.98 95% CI (0.80-1.19), P = 0.90), nor were their AKI stages worse [OR 0.99 (0.79-1.24), P = 0.91]. No significant differences were detected in NSAID-related side effects [OR 1.50 (0.42, 5.32), P = 0.53]. However, NSAID treatment was associated with shorter postoperative hospitalization: [5 [4,7] vs. 6 [5,7] days, P < 0.001]. CONCLUSIONS: Perioperative use of NSAIDs in patients having nephrectomies was not associated with a greater risk of AKI, and possibly reduced the duration of hospitalization. Prospective interventional data are needed to guide NSAID use in this high-risk patient subset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.002 | 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".