Impact of statin adherence on cardiovascular disease and mortality outcomes: a systematic review
Bibliographic record
Abstract
AIMS: While suboptimal adherence to statin medication has been quantified in real-world patient settings, a better understanding of its impact is needed, particularly with respect to distinct problems of medication taking. Our aim was to synthesize current evidence on the impacts of statin adherence, discontinuation and persistence on cardiovascular disease and mortality outcomes. METHODS: We conducted a systematic review of peer-reviewed studies using a mapped search of Medline, Embase and International Pharmaceutical Abstracts databases. Observational studies that met the following criteria were included: defined patient population;statin adherence exposure; defined study outcome [i.e. cardiovascular disease (CVD), mortality]; and reporting of statin-specific results. RESULTS: Overall, 28 studies were included, with 19 studies evaluating outcomes associated with statin adherence, six with statin discontinuation and three with statin persistence. Among adherence studies, the proportion of days covered was the most widely used measure, with the majority of studies reporting increased risk of CVD (statistically significant risk estimates ranging from 1.22 to 5.26)and mortality (statistically significant risk estimates ranging from 1.25 to 2.54) among non-adherent individuals. There was greater methodological variability in discontinuation and persistence studies. However, findings of increased CVD (statistically significant risk estimates ranging from 1.22 to 1.67) and mortality (statistically significant risk estimates ranging from 1.79 to 5.00) among nonpersistent individuals were also consistently reported. CONCLUSIONS: Observational studies consistently report an increased risk of adverse outcomes associated with poor statin adherence. These findings have important implications for patients and physicians and emphasize the importance of monitoring and encouraging adherence to 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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".