Clinical impact of a structured secondary cardiovascular prevention program following acute coronary syndromes: A prospective multicenter healthcare intervention
Bibliographic record
Abstract
BACKGROUND: Structured secondary cardiovascular prevention programs (SSCP) following acute coronary syndromes (ACS) may reduce major adverse cardiovascular events (MACE) through better adherence to post-ACS recommendations. METHODS: Through a prospective multicenter cohort study, we compared the outcomes of two sequential post-ACS patient cohorts, the initial one receiving standard care (SC) followed by one receiving additional interventions (SSCP) aimed at improving patient education as well as healthcare provider and hospital systems. The primary endpoint was MACE at one year. Secondary endpoints included adherence to recommended therapies, attendance to cardiac rehabilitation (CR) and successful achievement of cardiovascular risk factor (CVRF) targets. RESULTS: In total, 2498 post-ACS patients from 4 Swiss university hospitals were included: 1210 vs 1288 in the SC and SSCP groups, respectively. The SSCP group demonstrated a significant increase in attendance to CR programs (RR 1.08, 95%CI 1.02-1.14, P = 0.006), despite not achieving the primary MACE endpoint (HR 0.97, 95%CI 0.77-1.22, P = 0.79). After age-stratification, significant reductions in cardiac death, MI and stroke events (HR 0.53, 95%CI 0.30-0.93, P for interaction = 0.016) were observed for SSCP patients ≤ 65 years old. The SSCP group also scored significantly better for the LDL cholesterol target (RR 1.07, 95%CI 1.02-1.13, P = 0.012), systolic blood pressure target (RR 1.06, 95%CI 1.01-1.13, P = 0.029) and physical activity (RR 1.10, 95%CI 1.01-1.20, P = 0.021). CONCLUSIONS: The implementation of an SSCP post ACS was associated with an improvement in the control of CVRF and attendance to CR programs, and was also associated with significant reductions in cardiac death, MI and stroke at one year for patients ≤65years old.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".