EU-Wi<i>d</i>e Cross-Section<i>a</i>l Obser<i>v</i>at<i>i</i>o<i>n</i>al Study of Lipid-Modifying Therapy Use in Se<i>c</i>ondary and Pr<i>i</i>mary Care: the DA VINCI study
Bibliographic record
Abstract
AIMS: To provide contemporary data on the implementation of European guideline recommendations for lipid-lowering therapies (LLTs) across different settings and populations and how this impacts low-density lipoprotein cholesterol (LDL-C) goal achievement. METHODS AND RESULTS: An 18 country, cross-sectional, observational study of patients prescribed LLT for primary or secondary prevention in primary or secondary care across Europe. Between June 2017 and November 2018, data were collected at a single visit, including LLT in the preceding 12 months and most recent LDL-C. Primary outcome was the achievement of risk-based 2016 European Society of Cardiology (ESC)/European Atherosclerosis Society (EAS) LDL-C goal while receiving stabilized LLT; 2019 goal achievement was also assessed. Overall, 5888 patients (3000 primary and 2888 secondary prevention patients) were enrolled; 54% [95% confidence interval (CI) 52-56] achieved their risk-based 2016 goal and 33% (95% CI 32-35) achieved their risk-based 2019 goal. High-intensity statin monotherapy was used in 20% and 38% of very high-risk primary and secondary prevention patients, respectively. Corresponding 2016 goal attainment was 22% and 45% (17% and 22% for 2019 goals) for very high-risk primary and secondary prevention patients, respectively. Use of moderate-high-intensity statins in combination with ezetimibe (9%), or any LLT with PCSK9 inhibitors (1%), was low; corresponding 2016 and 2019 goal attainment was 53% and 20% (ezetimibe combination), and 67% and 58% (PCSK9i combination). CONCLUSION: Gaps between clinical guidelines and clinical practice for lipid management across Europe persist, which will be exacerbated by the 2019 guidelines. Even with optimized statins, greater utilization of non-statin LLT is likely needed to reduce these gaps for patients at highest risk.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".