Assessment and management of statin-associated muscle symptoms (SAMS): A clinical perspective from the National Lipid Association
Bibliographic record
Abstract
Statin-associated muscle symptoms (SAMS) are the most common form of statin intolerance and are associated with increased risk of cardiovascular events that manifest from statin underutilization and discontinuation. The reported frequencies of SAMS are divergent in the literature. The writing group estimates the prevalence of SAMS, namely all muscle symptoms temporally related to statin use but without regard to causality, to be about 10% (range 5% to 25%), and the prevalence of pharmacological SAMS, specifically muscle symptoms resulting from pharmacological properties of the statin, to be about 1-2% (range 0.5% to 4%). In clinical practice, SAMS are likely to result from a combination of pharmacological and nonpharmacological effects, however this does not make the symptoms any less clinically relevant. Regardless of the etiology, SAMS need to be addressed in accordance with patients' preferences and experiences. This clinical perspective reviews the epidemiology and underlying pathophysiology of SAMS, and the cardiovascular consequences resulting from statin discontinuation. We present patient-centered clinical and communication strategies to mitigate SAMS and improve medication adherence and outcomes among statin users. Treatment strategies include 1) optimizing lifestyle interventions, 2) modulating risk factors that may contribute to muscle symptoms, 3) optimizing statin tolerability by dose reduction, decreased dosing frequency, or use of an alternate statin with more favorable pharmacokinetic properties, and 4) use of non-statins, emphasizing those with evidence for atherosclerotic risk reduction, either in combination with or in place of statin therapy depending on the patient's circumstances. The focus of this clinical perspective is sustainable lipoprotein goal achievement, which is important for cardiovascular risk reduction.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".