Lipid-lowering treatment intensity, persistence, adherence and goal attainment in patients with coronary heart disease
Bibliographic record
Abstract
BACKGROUND: To examine patterns of lipid-lowering therapy (LLT) use, and persistence and adherence among patients with coronary heart disease and their associations with lipoprotein cholesterol (LDL-C) goal attainment. METHODS: Observational study among 26,768 patients who had suffered a myocardial infarction or had been revascularized in Stockholm during 2012 to 2018, and followed up through 2019. Outcomes included initiation of LLT, discontinuation, re-initiation, adherence to treatment and LDL-C goal attainment according to the European dyslipidaemia guidelines from 2011 and 2016 (mainly LDL-C <1.8 mmol/L). RESULTS: 82% of patients commenced or continued LLT within 90 days after discharge. Of those, 71% were dispensed an LLT prescription within 30 days (62% of them for high-intensity LLT). High-intensity LLT prescribing increased over time, from 12% in 2012 to 78% in 2018. During a median follow-up of 3 (IQR 2-5) years 73% continued to fill prescriptions for a statin, 26.3% temporarily or permanently discontinued, and 0.5% changed to non-statin LLT. Only 1.3% discontinued statin treatment permanently. Throughout observation, about 80% of patients showed good statin adherence (proportion of days covered ≥80%). LDL-C target attainment was 52% the first year and <50% during subsequent years. LDL-C goal attainment was highest among patients receiving high-intensity statin treatment and showing good treatment adherence. CONCLUSION: In secondary prevention for patients with established coronary heart disease, the proportion of LDL-C target attainment was low throughout the time period of the study, despite increasing use of high-intensity LLT and good treatment persistence and adherence.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".