Impact of prior coronary artery bypass grafting and coronary lesion complexity on outcomes of transcatheter aortic valve replacement for severe aortic stenosis
Bibliographic record
Abstract
OBJECTIVE: To investigate the impact of prior coronary artery bypass grafting (CABG) and coronary lesion complexity on transcatheter aortic valve replacement (TAVR) outcomes for aortic stenosis. METHODS: Clinical outcomes of TAVR were retrospectively compared between patients with and without prior CABG, and between patients with prior CABG and without coronary artery disease (CAD). The impact of the CABG SYNTAX score was also evaluated in patients with prior CABG. RESULTS: The study included 1042 patients with a median age and follow-up of 82 years and 25 (range: 0-72) months, respectively. Of these, 175 patients had a history of CABG, while 401 were free of CAD. Patients with prior CABG were more likely to be male and had higher rates of diabetes, peripheral artery disease and atrial fibrillation compared with patients without prior CABG. After 2 : 1 propensity score matching, all-cause mortality ( P = 0.17) and the composite of all-cause mortality, stroke and coronary intervention ( P = 0.16) were similar between patients with (n = 166) and without (n = 304) prior CABG. A 1 : 1 propensity score-matched analysis, however, showed lower rates of all-cause mortality ( P = 0.04) and the composite outcome ( P = 0.04) in patients with prior CABG (n = 134) compared with patients without CAD (n = 134). The median CABG SYNTAX score was 16 (interquartile range: 9.0-23), which was not associated with better/worse clinical outcomes in patients with prior CABG. CONCLUSION: Prior CABG may positively affect mid-term TAVR outcomes for aortic stenosis compared with no CAD when adjusted for other comorbidities. The CABG SYNTAX score did not influence the prognosis after TAVR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".