Comparison of different percutaneous revascularisation timing strategies in patients undergoing transcatheter aortic valve implantation
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Bibliographic record
Abstract
BACKGROUND: The optimal timing to perform percutaneous coronary interventions (PCI) in transcatheter aortic valve implantation (TAVI) patients remains unknown. AIMS: We sought to compare different PCI timing strategies in TAVI patients. METHODS: The REVASC-TAVI registry is an international registry including patients undergoing TAVI with significant, stable coronary artery disease (CAD) at preprocedural workup. In this analysis, patients scheduled to undergo PCI before, after or concomitantly with TAVI were included. The main endpoints were all-cause death and a composite of all-cause death, stroke, myocardial infarction (MI) or rehospitalisation for congestive heart failure (CHF) at 2 years. Outcomes were adjusted using the inverse probability treatment weighting (IPTW) method. RESULTS: A total of 1,603 patients were included. PCI was performed before, after or concomitantly with TAVI in 65.6% (n=1,052), 9.8% (n=157) or 24.6% (n=394), respectively. At 2 years, all-cause death was significantly lower in patients undergoing PCI after TAVI as compared with PCI before or concomitantly with TAVI (6.8% vs 20.1% vs 20.6%; p<0.001). Likewise, the composite endpoint was significantly lower in patients undergoing PCI after TAVI as compared with PCI before or concomitantly with TAVI (17.4% vs 30.4% vs 30.0%; p=0.003). Results were confirmed at landmark analyses considering events from 0 to 30 days and from 31 to 720 days. CONCLUSIONS: In patients with severe aortic stenosis and stable coronary artery disease scheduled for TAVI, performance of PCI after TAVI seems to be associated with improved 2-year clinical outcomes compared with other revascularisation timing strategies. These results need to be confirmed in randomised clinical trials.
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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) | 0.000 | 0.001 |
| 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 it