Abstract 255: Temporal Trends and Factors Associated with Referral for Cardiac Rehabilitation Post Acute Coronary Syndrome: Insights from the Canadian Global Registry of Acute Coronary Events (GRACE)
Bibliographic record
Abstract
Introduction: The beneficial effects of cardiac rehabilitation (CR) on morbidity and mortality after an acute coronary syndrome (ACS) are well established. Despite guidelines, CR referral rates have been low. Determining factors associated with CR referral would assist in closing this care gap and improve outcomes. Using the Canadian Global Registry of Acute Coronary Events (GRACE) database, we examined 1) the temporal trends of CR referral rates in Canada and its associated factors in a contemporary setting; 2) use of evidence-based medical therapies after ACS and its relationship with CR referral during index hospitalization. Hypothesis: CR referral rates have increased over time but remain below current guideline recommendations. Methods: From the Canadian GRACE registry, we retrospectively analyzed data from 11 Canadian centers during 2000 - 2007. CR referral rates were established and analyzed over time. We compared the CR Referral group to the Non-CR Referral group using univariate logistic regression in regards to patient characteristics, in-hospital diagnosis, clinical events and investigations. Categorical and continuous variables were compared using the chi-square and the Wilcoxon rank sum tests, respectively, with statistical significance at p<0.05. Data of guideline-recommended medication use at discharge and 6 months post-discharge were also analyzed. Results: In the 8-year period, 3338 patients (median age 64 years, 32% women) were assessed. Initial CR referral rate in 2000 was 2.7% (6/219) and increased to 51.2% (220/430) in 2007 (p<0.0001). Univariate factors for CR referral include younger age, larger infarct size, and a diagnosis of STE-ACS. Univariate factors for non-CR referral include CHF, higher GRACE score, and previous CAD. Hospitals with on-site supervised CR facilities had higher CR referral rates. CR Referral group had higher usage of evidence-based medications at time of discharge as well as 6 months post-discharge (all p< 0.0001). Conclusions: There has been a steady increase in CR referrals; however, contemporary numbers are still below the current recommendation of an 85% referral rate. Higher usage of recommended medications in the CR Referral group were noted, likely reflecting the association of CR referral with overall quality of care. Factors associated with CR referral include younger age, larger infarct size, and STE-ACS. Lack of referral was associated with CHF, previous CAD and high GRACE score. Targeting non-referred populations may improve quality of care and close care gaps in secondary prevention.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".