Determinants of participation and risk factor control according to attendance in cardiac rehabilitation programmes in coronary patients in Europe: EUROASPIRE IV survey
Bibliographic record
Abstract
Aim The purpose of this study was to describe the proportions of patients referred to and attending cardiac rehabilitation programmes in Europe and to compare lifestyle and risk factor targets achieved according to participation in a cardiac rehabilitation programme. Methods The EUROASPIRE IV cross-sectional survey was undertaken in 78 centres from 24 European countries. Consecutive patients aged <80 years with acute coronary syndromes and/or revascularization procedures were interviewed at least six months after their event. Results A total of 7998 patients (24% females) were interviewed. Overall, 51% were advised to participate in a cardiac rehabilitation programme and 81% of them attended at least half of the sessions; being 41% of the study population. Older patients, women, those at low socio-economic status or enrolled with percutaneous coronary intervention and unstable angina, as well as those with a previous history of coronary disease, heart failure, hypertension or dysglycaemia were less likely to be advised to follow a cardiac rehabilitation programme. People smoking prior to the recruiting event were less likely to participate. The proportions of patients achieving lifestyle targets were higher in the cardiac rehabilitation programme group as compared to the non-cardiac rehabilitation programme group: stopping smoking (57% vs 47%, p < 0.0001), recommended physical activity levels (47% vs 38%, p < 0.0001) and body mass index<30 kg/m2(65% vs 61%, p=0.0007). However, there were no differences in the blood pressure, lipids and glucose control. Patients who attended a cardiac rehabilitation programme had significantly lower anxiety and depression scores and better medication adherence. Conclusions Only half of all coronary patients were referred and a minority attended a cardiac rehabilitation programme. Those attending were more likely to achieve lifestyle targets, had lower depression and anxiety, and better medication adherence. There is still considerable potential to further reduce cardiovascular risk by increasing uptake and fully integrating secondary prevention and cardiac rehabilitation to provide a modern preventive cardiology programme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".