Cardiac Rehabilitation and Secondary Prevention in Patients with Coronary Artery Disease and Atrial Fibrillation
Bibliographic record
Abstract
Background: Referral to and participation in cardiac rehabilitation (CR) in Canada and elsewhere remains suboptimal. The evidence for the benefits of CR in reducing incidence of atrial fibrillation (AF) in patients with coronary artery disease (CAD) is modest. Furthermore, whether multifactorial risk factor intervention is effective in improving prognosis in patients with AF remains unclear. Methods: We studied these questions by conducting two projects. Project 1 is a systematic review, which evaluated evidence on the effects of multifactorial risk factor intervention in patients with AF. Project 2 is a retrospective cohort study, which evaluated the relationships of CR completion status and cardiorespiratory fitness (CRF) across a CR program with the risk of incident AF. These analyses in Project 2 were performed by linking databases from an Alberta provincial cardiac catheterization registry, a city-wide CR program in Calgary, and Alberta provincial health administrative datasets. Results: In Project 1, the systematic review suggested that multifactorial risk factor intervention was positively associated with improved AF-related symptoms and health-related quality of life. In Project 2, we first used electrocardiography data to improve the diagnostic yield of administrative data-based AF identification algorithms. We further demonstrated that CR program completion was not associated with lower risk of incident AF after adjusting for baseline characteristics. However, both baseline CRF, 12-week CRF, and CRF changes following CR completion had inverse dose-dependent relationships with the risk of incident AF. Furthermore, we developed a risk prediction model for incident AF in patients completing a CR program, which showed good discrimination and was well calibrated in predicting the risk of AF at 5-years follow-up. Conclusions: These findings have enhanced the importance of multifactorial risk factor intervention in managing patients with AF, and added to the current state of knowledge of CR in improving the prognosis of patients with CAD, thereby providing further support for the promotion of CR. Furthermore, the risk prediction model will help prioritize resources for patients who are at high risk of developing AF and can benefit the most from screening for AF and participating in personalized CR services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".