Effectiveness of quality improvement interventions in cardiac rehabilitation on processes and patient outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: Significant variability in cardiac rehabilitation (CR) programme content and delivery persists. Quality improvement interventions enhance adherence to standards and reduce variability, yet synthesized evidence of their characteristics and effectiveness in CR is lacking. This meta-analysis aimed to evaluate the effects of quality improvement interventions on CR processes and patient outcomes. METHODS AND RESULTS: Scopus, CENTRAL, Medline, Embase, and CINAHL were searched for studies published from January 2000 to November 2024. Study selection in Covidence, data extraction, and risk of bias assessment were completed. Where possible, meta-analyses were conducted using RevMan v5.3, random-effects model. Outcomes not suitable for meta-analysis were reported narratively. Fifteen studies (76 856 participants) were eligible, including 1 randomized controlled trial, 1 retrospective observational study, and 13 pre-post studies. Meta-analysis of 11 studies (17 010 participants) showed that quality improvement interventions significantly improved CR referral [odds ratio (OR) 5.25; 95% confidence interval (CI) 3.11, 8.87]. From subgroup analyses, patient education had the largest effect (OR 8.37; 95% CI 4.32, 16.21), followed by technology (OR 5.56; 95% CI 2.67, 11.58) and process changes (OR 5.31; 95% CI 2.53, 11.12). Narrative synthesis indicated that quality improvement interventions led to significant improvements in time from discharge to scheduled appointment (1/1 studies), attendance (3/4), prescription of guideline-directed medical therapy (1/1), and completion (1/1). Few studies reported patient outcomes. CONCLUSION: Quality improvement interventions improve referral to CR by up to eight times. While caution is warranted, quality improvement interventions may also lower wait times and increase programme utilization. Future studies are needed. REGISTRATION PROSPERO: CRD42024557586.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.007 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".