Effect of Pravastatin on Cardiovascular Events in People With Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Limited data describe the cardiovascular benefit of HMG-CoA reductase inhibitors (statins) in people with moderate chronic kidney disease (CKD). The objective of this analysis was to determine whether pravastatin reduced the incidence of cardiovascular events in people with or at high risk for coronary disease and with concomitant moderate CKD. METHODS AND RESULTS: We analyzed data from the Pravastatin Pooling Project (PPP), a subject-level database combining results from 3 randomized trials of pravastatin (40 mg daily) versus placebo. Of 19 700 subjects, 4491 (22.8%) had moderate CKD, defined by an estimated glomerular filtration rate of 30 to 59.99 mL/min per 1.73 m2 body surface area. The primary outcome was time to myocardial infarction, coronary death, or percutaneous/surgical coronary revascularization. Moderate CKD was independently associated with an increased risk of the primary outcome (adjusted HR 1.26, 95% CI 1.07 to 1.49) compared with those with normal renal function. Among the 4491 subjects with moderate CKD, pravastatin significantly reduced the incidence of the primary outcome (HR 0.77, 95% CI 0.68 to 0.86), similar to the effect of pravastatin on the primary outcome in subjects with normal kidney function (HR 0.78, 95% CI 0.65 to 0.94). Pravastatin also appeared to reduce the total mortality rate in those with moderate CKD (adjusted HR 0.86, 95% CI 0.74 to 1.00, P=0.045). CONCLUSIONS: Pravastatin reduces cardiovascular event rates in people with or at risk for coronary disease and concomitant moderate CKD, many of whom have serum creatinine levels within the normal range. Given the high risk associated with CKD, the absolute benefit that resulted from use of pravastatin was greater than in those with normal renal function.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".