Left Main Coronary Artery Revascularization in Patients with Impaired Renal Function: Percutaneous Coronary Intervention versus Coronary Artery Bypass Grafting
Bibliographic record
Abstract
INTRODUCTION: The evidence about the optimal revascularization strategy in patients with left main coronary artery (LMCA) disease and impaired renal function is limited. Thus, we aimed to compare the outcomes of LMCA disease revascularization (percutaneous coronary intervention [PCI] vs. coronary artery bypass grafting [CABG]) in patients with and without impaired renal function. METHODS: This retrospective cohort study included 2,138 patients recruited from 14 centers between 2015 and 2,019. We compared patients with impaired renal function who had PCI (n= 316) to those who had CABG (n = 121) and compared patients with normal renal function who had PCI (n = 906) to those who had CABG (n = 795). The study outcomes were in-hospital and follow-up major adverse cardiovascular and cerebrovascular events (MACCE). RESULTS: Multivariable logistic regression analysis showed that the risk of in-hospital MACCE was significantly higher in CABG compared to PCI in patients with impaired renal function (odds ratio [OR]: 8.13 [95% CI: 4.19-15.76], p < 0.001) and normal renal function (OR: 2.59 [95% CI: 1.79-3.73]; p < 0.001). There were no differences in follow-up MACCE between CABG and PCI in patients with impaired renal function (HR: 1.14 [95% CI: 0.71-1.81], p = 0.585) and normal renal function (HR: 1.12 [0.90-1.39], p = 0.312). CONCLUSIONS: PCI could have an advantage over CABG in revascularization of LMCA disease in patients with impaired renal function regarding in-hospital MACCE. The follow-up MACCE was comparable between PCI and CABG in patients with impaired and normal renal function.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".