Association of Statin Therapy with Adverse Clinical Outcomes Among Patients on Dialysis: A Province-Wide Retrospective Cohort Study
Bibliographic record
Abstract
Background: Although statins reduce cardiovascular (CV) events in non-dialysis chronic kidney disease, their benefit in dialysis patients is unclear. We evaluated the association between statin use, major cardiovascular events (MACE), and all-cause mortality in chronic dialysis patients in Alberta, Canada Methods: A retrospective study of 6,453 adults starting dialysis (2010–2019) that included statin users (n=4,483) and non-users (n=1,970). Primary outcome was a composite MACE (CV death, non-fatal myocardial infarction (MI), non-fatal stroke, or heart failure (HF) hospitalization); secondary outcome was all-cause mortality. Analyses were adjusted for demographics, clinical characteristics, and medications Results: Over a median 3.6-year follow-up, participants (median age 62.2 years, 37.6% male) were mostly on hemodialysis (71.3%) with common comorbidities like hypertension (88.6%), diabetes (58.1%), coronary artery disease (41.3%), and HF (37.1%). Statin use was not associated with reduced MACE risk (adjusted hazard ratio [aHR] 0.98, 95% CI: 0.89–1.09; p=0.760), nor its individual components. However, it was significantly associated with lower all-cause mortality (aHR 0.84, 95% CI: 0.77–0.91; p<0.001), with benefit greatest in those aged 50–65 years (aHR 0.67). Continuous and new statin users had lower mortality (aHRs 0.69 and 0.81), while prior users (statin use discontinued after dialysis initiation) showed increased risk (aHR 1.98) Conclusion: In this province-wide dialysis cohort, statin use was not associated with a reduction in MACE but linked to a reduced risk of all-cause mortality, suggesting a potential survival benefit warranting further studies Funding: Private Foundation SupportAssociation between statin use and the risk of major adverse cardiovascular events (MACE) and their components, all-cause mortality, and all-cause hospitalization.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".