Economic Evaluation of a Pharmacogenomics Test for Statin-Induced Myopathy in Cardiovascular High-Risk Patients Initiating a Statin
Bibliographic record
Abstract
BACKGROUND: Statins are the mainstay hypercholesterolemia treatment and reduce the risk of cardiovascular events in patients. However, statin therapy is often interrupted in patients experiencing musculoskeletal pain or myopathy, which are common in this patient group. Currently, the standard tests for diagnosing statin myopathies are difficult to interpret. A pharmacogenomics (PGx) test to diagnose statin-induced myopathy would be highly desirable. METHODS: We developed a Markov state model to assess the cost-effectiveness of a hypothetical PGx test, which aims to identify statin-induced myopathy in high-risk, secondary prevention cardiovascular patients. The alternative strategy hypothesized is that physicians or patients interrupt the statin therapy in the presence of musculoskeletal pain. Our model includes health states specific to the PGx test outcome which assesses the impact of test errors. RESULTS: Assuming a perfect test, the results indicate that the PGx test strategy dominates when the test costs less than CAN$356, when the strategy is cost neutral. These results are robust to deterministic and probabilistic sensitivity analyses. CONCLUSION: Our base-case results show that a PGx test for statin-induced myopathy in a high-risk, secondary prevention of a cardiovascular event population would be a dominant solution for a test cost of CAN$356 or less. Furthermore, the modelling of the complete range of diagnostic test outcomes provide a broader understanding of the economic value of the pharmacogenomics test.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".