Left Ventricular Function Recovery After Coronary Revascularization and Medical Therapy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The prevalence of coronary artery disease (CAD) with reduced left ventricular ejection fraction (LVEF) is rising, but the optimal treatment remains unclear. The present study aims to investigate the impact of revascularization (coronary artery bypass grafting (CABG), or percutaneous coronary intervention (PCI)) and medical therapy (MT) on LVEF recovery in patients with CAD and LVEF ≤ 40%. Methods: All clinical studies which reported LVEF in patients with CAD and LVEF ≤ 40% undergoing either revascularization or MT were included. A systematic literature search up to May 11, 2017 was performed on MEDLINE, EMBASE and the Cochrane Library in Ovid. Studies were assessed for eligibility and data were extracted by independent reviewers in duplicate with disagreements resolved by a third reviewer. Bias assessment was performed using the Newcastle-Ottawa Scale. Random effect meta-analysis was performed on included studies. The primary outcome was change in LVEF after intervention as compared to baseline. Results: 83 cohort studies with a total of 7,157 patients were included. We observed a statistically significant increase in LVEF following revascularization with CABG (MD 7.76; 95% CI: 6.81 to 8.70; p<0.00001; I2=99%) and PCI (MD 6.70; 95% CI: 4.71 to 8.70; p<0.00001; I2=94%) but not after MT (MD 4.06; 95% CI: -2.12 to 10.24; p=0.20; I2=98%). Conclusion: In patients with CAD and LVEF ≤ 40%, revascularization leads to LVEF recovery as compared to MT. There is continued need for RCT level evidence to enable optimization of treatment selection in this difficult population.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".