Percutaneous revascularization improves outcomes in patients with prior coronary artery bypass surgery
Bibliographic record
Abstract
BACKGROUND: Although ACC/AHA guidelines recommend a low threshold for catheterization after coronary artery bypass surgery (CABG), in clinical practice repeat revascularization often appears unfeasible and data on outcomes are scarce. METHODS: Using APPROACH, a clinical data collection and outcome monitoring initiative in Alberta, Canada, we analyzed nonemergency repeat catheterization, revascularization, and mortality rates of all patients with previous CABG, grouped by indication (acute coronary syndromes [ACS], stable angina [SA]) and compared to those of the cohort without previous CABG. RESULTS: Of 7,127 patients, 31.5%, and 11% received percutaneous revascularization (PCI), or reoperation, respectively. Significantly more post-CABG patients were managed medically as compared with the overall APPROACH cohort of coronary disease patients (57.5% vs. 41.5%-P < 0.001). Post CABG patients with ACS received PCI more often than those with SA (36.4% vs. 24.8%). PCI was associated with improved both non-adjusted and adjusted mortality by 22 and 19%, respectively (P < 0.001) during a follow-up of up to 14 years. Patients with diabetes had a higher mortality rate than those without at 1-, 5-, and 10-year follow-up in every treatment group. However PCI was associated with a similar improvement in mortality (HR: 0.76 [95% SD: 0.65-0.90]) in diabetic patients (HR: 0.85 [95% SD: 0.75-0.96]) when compared to medical management. CONCLUSION: Significantly fewer post-CABG patients received repeat revascularization than all-comers however; PCI was associated with improved mortality in both the diabetic and the nondiabetic patient population. These findings support the practice of attempting revascularization in post-CABG patients, particularly in those with an ACS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".