Effectiveness and Utilization of Cardiac Rehabilitation Among People With CKD
Bibliographic record
Abstract
Introduction Cardiac rehabilitation (CR) is a proven therapy for reducing cardiovascular death and hospitalization. Whether CR participation is associated with improved outcomes in patients with chronic kidney disease (CKD) is unknown. Methods We obtained data on all adult patients in Calgary, Alberta, Canada with angiographically proven coronary artery disease from 1996 to 2016 referred to CR from The Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease and TotalCardiology Rehabilitation. An estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m 2 or kidney replacement therapy defined CKD. Predictors of CR use were estimated with multinomial logistic regression. The association between starting versus not starting and completion versus noncompletion of CR and clinical outcomes were estimated using multivariable Cox proportional hazards models. Results Of 23,215 patients referred to CR, 12,084 were eligible for inclusion. Participants with CKD (N = 1322) were older, had more comorbidity, lower exercise capacity on graded treadmill testing, and took longer to be referred and to start CR than those without CKD. CKD predicted not starting CR: odds ratio 0.73 (95% confidence interval [CI] 0.64–0.83). Over a median 1 year follow-up, there were 146 deaths, 40 (0.3%) from CKD and 106 (1.0%) not from CKD. Similar to those without CKD, the risk of death was lower in CR completers (hazard ratio [HR] 0.24 [95% CI 0.06–0.91) and starters (HR 0.56 [95% CI 0.29– 1.10]) with CKD. Conclusion CR participation was associated with comparable benefits in people with moderate CKD as those without who survived to CR. Lower rates of CR attendance in this high-risk population suggest that strategies to increase CR utilization are needed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".