LDL-cholesterol levels and lipid lowering therapy in secondary prevention. Baseline data from the BRING-UP prospective registry
Bibliographic record
Abstract
AIMS: To narrow the gap between guidelines recommendation for secondary cardiovascular prevention and clinical practice, we designed a national project based on educational programs and patient data collection. METHODS: BRING-UP Prevention is an observational, prospective, multicentre study on patients with an atherothrombotic event enrolled in 2 phases: an educational intervention followed by two 3-months data collection, followed by 6 and 12-month follow-up, when the primary, secondary and exploratory endpoints will be evaluated. Clinical characteristics, treatments and target achievement for LDL cholesterol and other modifiable risk factors at baseline are reported in this manuscript. RESULTS: From September 2023 to February 2024, 189 cardiology centers included 4790 patients, 2500 hospitalized, and 2290 managed as outpatients. Of the 4790 patients, 98 % had CAD, 6.1 % CVD, and 6.9 % PAD. Mean age was 67 ± 11 years, 20 % were females. Patients with LDL-C levels <55 mg/dL were 32.6 %. Patients at target for blood pressure were 39.2 %. Diabetic patients were 27.5 %, HbA1c <7 % was reported in 43.5 % of them. Statins prescription increased from 69 % at entry to 96 % at discharge/end of visit. In 74.5 % of patients, statins were prescribed in combination with ezetimibe. PCSK9-i or inclisiran were prescribed in a low rate of patients. CONCLUSION: These data show that a low percentage of patients was at goal for LDL-C level and blood pressure. The 6-month follow-up visit will allow us to evaluate the changes in modifiable risk factors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".