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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".