Myocardial Revascularization in Patients With 3 Vessel Coronary Artery Disease and Chronic Kidney Disease: Coronary Artery Bypass Grafting Versus Percutaneous Coronary Intervention
Bibliographic record
Abstract
Coronary artery disease (CAD) and chronic kidney disease (CKD) commonly co-exist. Superior outcomes with coronary artery bypass grafting(CABG) compared to percutaneous coronary intervention (PCI) have been identified in patients with 3 vessel CAD (TVD) and CKD but have been limited to mid-term follow-up. Herein, we analyzed the long-term outcomes of patients with TVD and CKD undergoing surgical versus percutaneous revascularization. 1,599 patients with CKD and TVD without STEMI or previous revascularization underwent coronary angiography between 2009 and 2018. The primary outcome was all-cause mortality. Secondary outcomes included rates of readmission for myocardial infarction (MI), stroke, repeat revascularization, and overall rehospitalization. 453 patients were included in the final analysis (PCI 373; CABG 80; median follow-up 9.3 years). All results are presented as CABG versus PCI. The rate of all-cause mortality at the longest follow-up (14.1 years) was significantly lower in patients who underwent CABG (68.9% vs 83.1%, p = 0.039, adjusted Hazard Ratio (aHR) 0.68, 95% confidence interval (CI) 0.47-0.98). Readmission rates for MI (10.2% vs. 28.4%, p = 0.009, aHR 0.37, 95% CI 0.17-0.77) and repeat revascularization (3.1% vs. 24.4%, p < 0.001, aHR 0.09, 95% CI 0.02-0.34) were also lower after CABG than after PCI. No significant difference was observed in the rates of readmission for stroke or all causes. In conclusion, in this retrospective single-center study, we confirmed that the previously described advantages of CABG over PCI in patients with CKD and TVD persist with extended long-term follow-up. CABG should be considered the gold standard approach to revascularization in this patient 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".