Statin Discontinuation in High-Risk Patients: A Systematic Review of the Evidence
Bibliographic record
Abstract
Hypercholesterolemia is a major risk factor for cardiovascular disease (CVD), the leading cause of death worldwide. Since the late 1980s, statins have emerged as effective lipid-lowering therapies and are now widely used to protect against and slow the progression of CVD and cerebrovascular disease. However, there is a significant gap between disease improvement in clinical trials and daily practice possibly attributable to poor adherence with statin therapy. High discontinuation rates were reported in primary and secondary prevention. This systematic review aims to summarize the current literature regarding the association between statin therapy discontinuation and cardiovascular and cerebrovascular events and all-cause mortality in high-risk patients. Available English literature was reviewed using Medline, Embase, Web of Sciences and the Cochrane Library; 39 studies were identified. In primary and secondary prevention, as well as perioperatively, non-adherence or discontinuation of statin therapy was associated with detrimental effects on cardiovascular and cerebrovascular outcomes, including disease severity and mortality. Importantly, some studies reported that very low adherence and discontinuation was associated with worse outcomes than never using statins. In conclusion, non-adherence and discontinuation of statin therapy significantly increased the incidence of cardiovascular and cerebrovascular events as well as all-cause mortality in high-risk patients. Patients would therefore benefit from closer adherence assessment and education programs aimed at increasing awareness of the risk associated with discontinuation of statin therapy.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".