Abstract 9448: The Effect of Diabetes on Cardiovascular Events After Coronary Artery Bypass Grafting versus Drug-Eluting Stents for Left Main Coronary Artery Disease: A Meta-Analysis
Bibliographic record
Abstract
Background: The optimal method of coronary revascularization for diabetes mellitus (DM) patients with left main coronary artery disease (LMCAD) is controversial in the drug-eluting stent (DES) era. We performed a systematic review and meta-analysis comparing DES-based percutaneous coronary intervention (PCI) to coronary artery bypass grafting (CABG) for LMCAD in DM patients and tested for potential effect measure modification (EMM) by diabetes on major adverse cardiovascular and cerebrovascular events (MACCE). Methods and Results: We included all randomized controlled trials (RCTs) and observational studies comparing CABG to DES-based PCI including DM patients with LMCAD published up to March 1, 2021. We completed separate random-effects meta-analyses for four RCTs (4,356 patients, mean follow-up of 4.9 years) and six observational studies (9,360 patients, mean follow-up of 5.2 years). DM and non-DM patients were at increased risk of the composite endpoint of all-cause mortality, myocardial infarction, stroke, and unplanned revascularization when comparing CABG to DES-based PCI (p-value for interaction = 0.70) (Figure 1, DM =1, Non-DM =0). In observational studies, there was no difference between DES-based PCI and CABG for all-cause mortality in patients with DM (Figure 2). Conclusion: DES-based PCI was associated with an increased risk of MACCE compared to CABG in LMCAD patients irrespectively of DM status. We did not observe significant EMM by DM status. Considering these data, heart teams could consider DM as one of the many components in the clinical decision-making process, but not as a primary deciding factor between DES and CABG for LMCAD.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.065 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".