Non-steroidal Anti-inflammatory Drug Prescriptions in Living Kidney Donors: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Current guidelines recommend that living kidney donors should avoid non-steroidal anti-inflammatory drugs due to their potential nephrotoxic effects. It is unclear if physicians are adhering to this recommendation. Objective: Our aim was to determine the proportion of living kidney donors that filled a non-steroidal anti-inflammatory drug prescription post-donation and the proportion with measurement of kidney function post-prescription. Design: We conducted a population-based, retrospective cohort study. Setting: We identified kidney donors in Alberta, Canada that had accessed the healthcare system in the outpatient, emergency department, or inpatient setting. Patients: Adult living kidney donors in Alberta, Canada who donated between 2002 and 2019. Measurements: We measured the number of non-steroidal anti-inflammatory drug prescriptions, type of prescribing physician, and evidence of post-prescription measurement of creatinine and potassium. Methods: We identified the proportion of donors who filled a non-steroidal anti-inflammatory drug prescription at least 1 year post-donation. We also assessed how many donors underwent laboratory testing for kidney function and potassium within 14 days following the first prescription. Results: Of the 759 living kidney donors included in our study, 273 (36%) had at least one non-steroidal anti-inflammatory drug prescription over a median follow-up of 7.2 years (interquartile range 3.5-11.5). The proportion of donors with at least one prescription in follow-up remained stable over time (~10% per year). Family physicians accounted for 66% of all non-steroidal anti-inflammatory drug prescriptions. Approximately, 10% of donors had measurements of serum creatinine or potassium post-prescription. Limitations: This study was limited by the inability to capture over-the-counter non-steroidal anti-inflammatory drug use, indication for the prescriptions, and indication for bloodwork being completed in the post-prescription period. Conclusions: Over one-third of living kidney donors are prescribed non-steroidal anti-inflammatory drugs despite current guideline recommendations, with only a minority undergoing post-prescription laboratory testing. Further research assessing outcomes following non-steroidal anti-inflammatory drug use is recommended to better inform optimal pain-management strategies for living kidney donors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".