Graft Failure After Coronary Artery Bypass Grafting and Its Association With Patient Characteristics and Clinical Events: A Pooled Individual Patient Data Analysis of Clinical Trials With Imaging Follow-Up
Bibliographic record
Abstract
BACKGROUND: Graft patency is the postulated mechanism for the benefits of coronary artery bypass grafting (CABG). However, systematic graft imaging assessment after CABG is rare, and there is a lack of contemporary data on the factors associated with graft failure and on the association between graft failure and clinical events after CABG. METHODS: We pooled individual patient data from randomized clinical trials with systematic CABG graft imaging to assess the incidence of graft failure and its association with clinical risk factors. The primary outcome was the composite of myocardial infarction or repeat revascularization occurring after CABG and before imaging. A 2-stage meta-analytic approach was used to evaluate the association between graft failure and the primary outcome. We also assessed the association between graft failure and myocardial infarction, repeat revascularization, or all-cause death occurring after imaging. RESULTS: Seven trials were included comprising 4413 patients (mean age, 64.4±9.1 years; 777 [17.6%] women; 3636 [82.4%] men) and 13 163 grafts (8740 saphenous vein grafts and 4423 arterial grafts). The median time to imaging was 1.02 years (interquartile range [IQR], 1.00–1.03). Graft failure occurred in 1487 (33.7%) patients and in 2190 (16.6%) grafts. Age (adjusted odds ratio [aOR], 1.08 [per 10-year increment] [95% CI, 1.01–1.15]; P =0.03), female sex (aOR, 1.27 [95% CI, 1.08–1.50]; P =0.004), and smoking (aOR, 1.20 [95% CI, 1.04–1.38]; P =0.01) were independently associated with graft failure, whereas statins were associated with a protective effect (aOR, 0.74 [95% CI, 0.63–0.88]; P <0.001). Graft failure was associated with an increased risk of myocardial infarction or repeat revascularization occurring between CABG and imaging assessment (8.0% in patients with graft failure versus 1.7% in patients without graft failure; aOR, 3.98 [95% CI, 3.54–4.47]; P <0.001). Graft failure was also associated with an increased risk of myocardial infarction or repeat revascularization occurring after imaging (7.8% versus 2.0%; aOR, 2.59 [95% CI, 1.86–3.62]; P <0.001). All-cause death after imaging occurred more frequently in patients with graft failure compared with patients without graft failure (11.0% versus 2.1%; aOR, 2.79 [95% CI, 2.01–3.89]; P <0.001). CONCLUSIONS: In contemporary practice, graft failure remains common among patients undergoing CABG and is strongly associated with adverse cardiac events.
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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 | 0.046 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.036 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".