The Impact of Lifestyle Intervention Programs on Long-Term Cardiac Event-Free Survival in Patients With Established Coronary Artery Disease
Bibliographic record
Abstract
Coronary artery disease (CAD) is a leading global cause of morbidity and mortality, necessitating comprehensive approaches for its management. This systematic review evaluates the long-term impact of structured lifestyle intervention programs on cardiac event-free survival in patients with established CAD. A total of eight studies, including randomized controlled trials (RCTs) and prospective cohort studies, were analyzed, encompassing diverse interventions such as cardiac rehabilitation, dietary modifications, exercise programs, and psychosocial support. The findings indicate that lifestyle interventions significantly improve event-free survival, reduce recurrent cardiac events, and enhance overall health markers as compared to usual care. Intensive interventions, such as comprehensive cardiac rehabilitation, showed the most pronounced benefits, including regression of coronary artery stenosis measured through angiographic imaging and a reduced need for revascularization (relative risk reduction up to 45%; p < 0.05). Flexible and accessible approaches, like home-based or telephonic rehabilitation, demonstrated potential in improving adherence, measured by program completion rates and self-reported lifestyle changes, and outcomes in specific populations such as elderly or high-risk patients. Limitations include variability in intervention intensity, small sample sizes in some studies, and differences in adherence definitions and measurement methods. This review highlights the critical role of lifestyle modifications as a cornerstone of secondary prevention strategies in CAD management and suggests that technology-based and demographic-specific interventions may hold promise for improving long-term outcomes. Future research should focus on long-term sustainability and optimizing tailored intervention designs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".