Statin Treatment Non-adherence and Discontinuation: Clinical Implications and Potential Solutions
Bibliographic record
Abstract
Statins are the most powerful lipid lowering drugs in clinical practice. However, the efficacy of statin therapy, as seen in randomized control trials, is undermined by the documented non-adherence observed in clinical practice. Understanding the clinical consequences of statin non-adherence is an important step in implementing successful interventions aimed at improving adherence. Our previous systematic review included a literature search up to January 2010 on the effects of statin non-adherence or discontinuation on cardiovascular (CV) and cerebrovascular outcomes. We provide an update to this publication and a review of promising interventions that have reported a demonstrated improvement in statin adherence. Through a systematic literature search of PubMed, Ovid Medline, Ovid Embase, CINAHL, Cochrane Library and Web of Science, out of the 3440 initially identified, 13 studies were selected. Non-adherence in a primary prevention population was associated with a graded increase in CV risk. Individuals taking statins for secondary prevention were at particular risk when taking statin with highly variable adherence. Moreover, particular attention is warranted for non-adherence in diabetic and rheumatoid arthritis populations, as non-adherence is significantly associated with CV risk as early as 1 month following discontinuation. Statin adherence, therefore, represents an important modifiable risk factor. Numerous interventions to improve adherence have shown promise, including copayment reduction, automatic reminders, mail-order pharmacies, counseling with a health professional, and fixed-dose combination therapy. Given the complexity of causes underlying statin non-adherence, successful strategies will likely need to be tailored to each patient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".