A national project to improve the quality of secondary prevention strategies: the results of the BRING-UP Prevention study
Bibliographic record
Abstract
Abstract Background Recent Italian real-world data show that more than 30% of patients hospitalised for a documented acute atherothrombotic event are readmitted to hospital in the year following discharge. Adherence to guideline recommendations for secondary prevention strategies appears to be largely inadequate. Aim To try to narrow the gap between what is recommended and what is implemented in clinical practice, we designed a national implementation science project, BRING-UP Prevention, based on educational programmes and patient data collection. Methods BRING-UP Prevention is a nationwide, observational, prospective, multicentre study enrolling patients with a documented prior atherothrombotic event. The study consists of two 3-month enrolment phases followed by a 6-month follow-up with end-point evaluation, with each enrolment phase preceded by an educational intervention to discuss guideline recommendations. The first phase was recently completed. These data relate to the primary endpoint, which was the percentage of patients achieving the target of LDL cholesterol <55 mg/dL. The first phase has recently been completed. These data refer to the primary endpoint, which was the percentage of patients achieving the target LDL cholesterol level of <55 mg/dL. Results 189 cardiology centres collected data on 4790 patients, 2500 discharged from hospital and 2290 managed as outpatients. Follow-up data at 6 months were available for 4643 patients (96.9%). The rate of patients with LDL cholesterol <55 mg/dL increased from 33% to 58.1%, with absolute and relative increases of 25.1 and 76.0%, respectively. Overall, the proportion of patients with LDL cholesterol ≤70 mg/dL increased from 53.5% to 82.2%. At discharge/end of visit, 96% of patients were on statins and 94.7% were still on statins at 6 months. Atorvastatin and rosuvastatin were the most commonly prescribed statins, in more than 75% of cases at high doses. Ezetimibe was prescribed in 84% of cases. The figure shows LDL cholesterol levels at baseline and after 6 months of follow-up. PCSK9Is were prescribed in 7.7% and inclisiran in 2.3% of patients. Pts with partial or total intolerance to statins were 4.5%. Conclusions Data from the first phase of the BRING-UP Prevention study show that: 1) the rate of pts with a LDL cholesterol increased consistently over the 6-month follow-up period; 2) this result was achieved with high intensity statins, very often in combination with ezetimibe, while the use of new lipid-lowering drugs remained limited. These data show that it is possible to significantly increase the percentage of patients achieving guideline-recommended LDL cholesterol levels with a very favourable cost-benefit approach using a high-intensity statin and ezetimibe. The need to use more potent and costly lipid-lowering approaches is limited to a relatively small proportion of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".