Coronary CT angiography instead of invasive angiography before TAVI: Feasibility and outcomes
Bibliographic record
Abstract
AIMS: Concomitant coronary artery disease (CAD) is frequent in transcatheter aortic valve implantation (TAVI) candidates. Despite societal recommendations of performing invasive coronary angiography (ICA) for coronary assessment in the pre-TAVI diagnostic workup, the prognostic value of ICA and beneficial effect of revascularization in these patients remains unclear. We aimed to determine feasibility and outcomes following a strategy of cardiac CT + coronary CT angiography (cCTA) rather than cardiac CT + ICA before TAVI. METHODS AND RESULTS: We performed a single-center, observational cohort study including all patients, without previous coronary intervention, referred to TAVI between April 2020 and November 2021. CAD was assessed by cCTA, and only patients with proximal stenosis >70 %, or left main stenosis >50 %, or cCTA was non-evaluable regarding proximal segments were subsequently referred to ICA. 240 patients were included in the study. No adverse effects to pre-cCTA-scan nitroglycerin administration were observed. On cCTA, 92 % of the patients had atheroscerosis. 191 (80 %) patients had cCTA only performed, while 49 (20 %) patients underwent subsequent ICA. During a median (range) follow-up of 15 (6-25) months, no difference in procedural complication rates, mortality rates, or number of unplanned ICA was observed between patients evaluated with only cCTA vs cCTA+ICA. CONCLUSIONS: Upfront cCTA instead of ICA for assessment of obstructive CAD in the diagnostic workup of patients with severe aortic stenosis referred to TAVI is feasible, safe, and with similar procedural and clinical outcomes. Randomized studies are warranted to further validate the safety of using CTA rather than ICA for coronary assessment in TAVI candidates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".