To improve the quality of secondary prevention strategies in diabetic patients: the BRING-UP Prevention study results
Bibliographic record
Abstract
Abstract Background Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in people with diabetes, making secondary prevention strategies critical to improving patient outcomes. The role of lipid-lowering therapy and blood pressure control is well established, but novel therapies such as SGLT-2 inhibitors and GLP-1 RA are also showing favourable results in reducing cardiovascular events. In addition, optimising glycaemic control and promoting lifestyle changes remain cornerstone strategies for reducing long-term CV risk. Purpose To try to narrow the gap between what is recommended and what is implemented in clinical practice in type 2 diabetic patients, 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 in 2 enrolment phases preceded by an educational intervention to discuss guideline recommendations. Primary endpoint: Rate of patients achieving target LDL cholesterol. Secondary endpoints: rate of patients achieving target blood pressure (<130/80 mmHg) rate of diabetic patients achieving target HbA1c (<7%) rate of overweight patients (BMI >27 kg/m2) achieving at least 10% weight loss. The first phase has recently been completed. Results 189 cardiology centres collected data on 4790 patients, of whom 1317 (27.5%) had diabetes. Follow-up data were available for 1229/1317 patients (93.3%). The figure shows LDL cholesterol levels at baseline and after 6 months. The rate of diabetic patients with LDL cholesterol <55 mg/dL increased from 43.4% to 65.5% with treatment based on statins and ezetimibe in 65.8% of patients. PCSK9 inhibitors were used in 5.6% of patients. HbA1c, measured at follow-up in only 61.8% of patients, was <7% in only 46.7% of patients. Pts with blood pressure <130/80 mmHg were 36.5% at baseline and 42.5% at follow-up. The rate of pts doing light physical activity increased from 36.2% to 50.1%. At baseline, 46.1% of patients had a BMI >27, which decreased to 43.3% at follow-up. 11.3% of overweight patients lost more than 10% of their body weight during follow-up. The most commonly prescribed antidiabetic medications at both baseline and follow-up were metformin (51.1%), SGLT2 inhibitors (49.6%), insulin (27.1%), and GLP-1 RA (19.4%). Conclusions This study shows that it is possible to improve adherence to the guideline target for LDL cholesterol with cost-effective drugs. There seems to be a need for more intensive strategies to improve blood pressure, HbA1c levels and lifestyle changes.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".