Statin use and the risk of acute kidney injury in older adults
Bibliographic record
Abstract
BACKGROUND: As more patients at lower cardiovascular (CV) risk are treated with statins, the balance between cardiovascular benefits and the risk of adverse events becomes increasingly important. METHODS: We did a population-based cohort study (May 1, 2002 to March 30, 2013) using province-wide laboratory and administrative data in Alberta. We studied new statin users aged 66 years of age and older who were not receiving renal replacement therapy at baseline. We assessed statin use at 30-day intervals to allow time-varying assessment of statin exposure in Cox proportional hazards models that examined the relation between statin use and hospitalization with acute kidney injury (AKI). RESULTS: Of the 128,140 new statin users, 47 and 46% were prescribed high- and medium-intensity regimens at the index date. During median follow-up of 4.6 years (interquartile range 2.2, 7.4), 9118 individuals were hospitalized for AKI. Compared to non-use, the use of high- and medium-intensity statin regimens was associated with significant increases in the adjusted risks of hospitalization with AKI: hazard ratios 1.16 [95% confidence interval (CI) 1.10, 1.23] and 1.07 (95% CI 1.01, 1.13), respectively. Risks of AKI were higher among women than men, and among users of angiotensin converting enzyme inhibitors/angiotensin receptor blockers than non-users, and among diuretic users (p for interaction 0.002, 0.01, and 0.04 respectively). CONCLUSIONS: We found a graded, independent association between the intensity of statin use and the risk of hospitalization with AKI, although the absolute magnitude of the excess risk was small.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".