Cardiac Rehabilitation Registries as Tools for Quality Improvement and Research: Insights From the SWEDEHEART-CR registry
Bibliographic record
Abstract
Cardiac rehabilitation (CR) is a well established intervention for secondary prevention after myocardial infarction (MI), yet substantial variability in CR delivery and patient outcomes persists globally. This review provides an in-depth overview of the SWEDEHEART-CR registry, a comprehensive Swedish national quality registry that has facilitated continuous quality improvement and clinical research in secondary prevention since its inception in 2005. SWEDEHEART-CR now achieves full national coverage on the centre level, capturing detailed longitudinal data on patient risk factors, psychosocial outcomes, exercise capacity, and adherence to CR components for more than 8500 post-MI patients annually. We discuss how the registry's validated quality index enables benchmarking and ongoing monitoring of care processes, highlighting substantial advances in cardiovascular risk management alongside enduring challenges. Furthermore, we highlight SWEDEHEART-CR's role as a pioneering platform for registry-based randomised controlled trials, enabling pragmatic evaluations of novel rehabilitation interventions such as tele-rehabilitation. The review also addresses ongoing large-scale studies that use the registry to investigate physical activity patterns after MI and how to implement optimal CR processes and structures in everyday clinical care. Finally, we explore future opportunities for international collaboration through harmonised CR registries to advance quality improvement and embedded clinical research on a broader scale. The SWEDEHEART-CR registry exemplifies how high-quality registry data can enhance equitable, evidence-based delivery of care and inform clinical practice and policy to improve cardiovascular outcomes.
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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 | 0.168 | 0.334 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".