Nonsteroidal anti-inflammatory drug use and risk of acute kidney injury and hyperkalemia in older adults: a population-based study
Bibliographic record
Abstract
BACKGROUND: Clinical guidelines caution against nonsteroidal anti-inflammatory drug (NSAID) use in older adults. The study objective was to quantify the 30-day risk of acute kidney injury (AKI) and hyperkalemia in older adults after NSAID initiation and to develop a model to predict these outcomes. METHODS: We conducted a population-based retrospective cohort study in Ontario, Canada from 2007 to 2015 of patients ≥66 years. We matched 46 107 new NSAID users with 46 107 nonusers with similar baseline health. The primary outcome was 30-day risk of AKI and secondary outcomes were hyperkalemia and all-cause mortality. RESULTS: NSAID use versus nonuse was associated with a higher 30-day risk of AKI {380 [0.82%] versus 272 [0.59%]; odds ratio (OR) 1.41 [95% confidence interval (CI) 1.20-1.65]} and hyperkalemia [184 (0.40%) versus 123 (0.27%); OR 1.50 (95% CI 1.20-1.89); risk difference 0.23% (95% CI 0.13-0.34)]. There was no association between NSAID use and all-cause mortality. A prediction model incorporated six predictors of AKI or hyperkalemia: older age, male gender, lower baseline estimated glomerular filtration rate, higher baseline serum potassium, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use or diuretic use. This model had moderate discrimination [C-statistic 0.72 (95% CI 0.70-0.74)] and good calibration. CONCLUSIONS: In older adults, new NSAID use compared with nonuse was associated with a higher 30-day risk of AKI and hyperkalemia but not all-cause mortality. Prescription NSAID use among many older adults may be safe, but providers should use caution and assess individual risk.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".