Lipid-lowering therapy in patients with coronary heart disease: an Italian real-life survey. Results from the Survey on Risk FactOrs and CardiovascuLar secondary prEvention and drug strategieS (SOFOCLES) in Italy
Bibliographic record
Abstract
In patients at high cardiovascular risk, a low-density lipoprotein cholesterol (LDL-C) reduction of ≥50% from baseline and an LDL-C goal of <70 mg/dL (or <55 mg/dL in very high-risk patients) are recommended. Multiple registry and retrospective studies have shown that patients with high atherosclerotic cardiovascular risk often do not reach the targets defined by the European Society of Cardiology guidelines as a result of suboptimal management of LDL-C. Here, we report the data on lipid-lowering therapy and lipid targets from the Survey on Risk FactOrs and CardiovascuLar secondary prEvention and drug strategieS (SOFOCLES), an observational, prospective study designed to collect data on patients with ischemic heart disease treated at cardiac outpatient clinics across the Italian national territory. We included patients with known coronary heart disease (CHD) who underwent follow-up visits at various outpatient cardiology clinics. A total of 2532 patients were included (mean age: 67±17 years, 80% male). Among patients with available laboratory data (n=1712), 995 (58%) had LDL-C<70 mg/dL, 717 (42%) had LDL-C≥70 mg/dL, and 470 (27%) had LDL-C<55 mg/dL. Patients who more frequently achieved the recommended LDL-C levels were male, had diabetes, had a higher educational level, and performed intense physical activity. Statins were used in 2339 (92%) patients, high-intensity statins (e.g., rosuvastatin 20/40 mg or atorvastatin 40/80 mg) in 1547 patients (61% of the whole population and 66% of patients on statins), and ezetimibe in 891 patients (35%). Patients receiving high-intensity statins tended to be younger, not to have diabetes, and to have been included in a cardiac rehabilitation program. In a real-world sample of Italian patients with CHD, adherence to lipid-lowering therapy fell markedly short of optimal levels. Many patients did not achieve the LDL-C target of 70 mg/dL, and even fewer reached the LDL-C target of 55 mg/dL. Notably, patients with a lower educational level had a greater likelihood of being undertreated. Strategies aimed at improving preventive interventions for CHD and overcoming social disparities should be evaluated and optimized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".