Dose of Cardiac Rehabilitation to Reduce Mortality and Morbidity: A Population‐Based Study
Bibliographic record
Abstract
Background There is wide variability in cardiac rehabilitation (CR) dose (ie, number of sessions) delivered, and no evidence‐based recommendations regarding what dose to prescribe. We aimed to test what CR dose impacts major adverse cardiovascular events (MACEs). Methods and Results This is an historical cohort study of all patients who had coronary artery disease and who initiated supervised CR between 2002 and 2012 from a single major CR center. CR dose was defined as number of visits including exercise and patient education. Follow‐up was performed using record linkage from the Rochester Epidemiology Project. MACEs included acute myocardial infarction, unstable angina, ventricular arrhythmias, stroke, revascularization, or all‐cause mortality. Dose was analyzed in several ways, including tertiles, categories, and as a continuous variable. Cox models were adjusted for factors associated with dose and MACE. The cohort consisted of 2345 patients, who attended a mean of 12.5±11.1 of 36 prescribed sessions. After a mean follow‐up of 6 years, 695 (29.65%) patients had a MACE, including 231 who died. CR dose was inversely associated with MACE (hazard ratio, 0.66 [95% CI]; 0.55–0.91) in those completing ≥20 sessions, when compared with those not exposed to formal exercise sessions (≤1 session; log‐rank P =0.007). We did not find evidence of nonlinearity ( P ≥0.050), suggesting no minimal threshold nor ceiling. Each additional session was associated with a lower rate of MACE (fully adjusted hazard ratio, 0.98 [95% CI, 0.97–0.99]). Greater session frequency was also associated with lower MACE risk (fully adjusted hazard ratio, 0.74 [95% CI, 0.58–0.94]). Conclusions CR reduces MACEs, but the benefit appears to be linear, with greater risk reduction with higher doses, and no upper threshold.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".