Comparative efficacy of coronary artery bypass surgery vs. percutaneous coronary intervention in patients with diabetes and multivessel coronary artery disease with or without chronic kidney disease
Bibliographic record
Abstract
The optimal method of coronary revascularization among patients with diabetes mellitus (DM) and multivessel coronary artery disease (CAD) complicated by chronic kidney disease (CKD) remains unknown. To examine the impact of coronary artery bypass surgery (CABG) vs. percutaneous coronary intervention (PCI) on cardiovascular outcomes in patients with diabetes with and without CKD. We conducted an ‘as-treated’ subgroup analysis of the FREEDOM trial to examine the therapeutic efficacy of CABG vs. PCI among patients with DM stratified by the presence (n = 451) or absence (n = 1392) of CKD. We defined CKD as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2. Baseline characteristics and clinical outcomes were compared between PCI and CABG groups within each CKD stratum. The primary endpoint was the composite occurrence of all-cause death, stroke or myocardial infarction [major adverse cardiovascular and cerebrovascular events (MACCE)]. Event rates were estimated at 5 years using the Kaplan–Meier approach and hazard ratios (HRs) for CABG (vs. PCI) were generated using Cox regression. Patients with CKD (mean eGFR 47 mL/min/1.73m2) were older and more often female compared to those without renal impairment. Over a median follow-up of 3.8 years, the effect of CABG on MACCE was consistent among those with CKD (26.0% vs. 35.6%; HR [95% CI]: 0.73 [0.50–1.05]) and without CKD (16.2% vs. 23.6%; HR [95% CI)]: 0.76 [0.58–1.00]) with no evidence of interaction (pint = 0.83). Stroke rates were non-significantly higher with CABG whereas rates of MI and repeat revascularization were significantly reduced with CABG in both groups. Compared to PCI, the effects of CABG on long-term risks for MACCE observed in the FREEDOM trial are preserved among patients with mild to moderate CKD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".