Feasibility and Utility of Anatomical and Physiological Evaluation of Coronary Disease With Cardiac CT in Severe Aortic Stenosis (FUTURE-AS Registry): Rationale and Design
Bibliographic record
Abstract
Background Coronary artery disease (CAD) in patients with severe aortic stenosis (AS) is common and may be associated with worse outcomes. Computed tomography coronary angiography (CTCA) and fractional flow reserve derived from computed tomography (FFR CT ) are tools for comprehensive coronary assessment. The utility and safety of CTCA and FFR CT in the work-up for transcatheter aortic valve replacement (TAVR) is not established, especially in an evolving landscape that involves younger TAVR patients. The FUTURE-AS Registry will assess the utility and safety of cardiac-optimized CTCA and FFR CT to evaluate CAD and guide referral for downstream invasive coronary angiography (ICA) in patients with severe AS being considered for TAVR. Methods FUTURE-AS is an international, prospective, multicenter registry of patients with severe AS referred for TAVR being assessed for CAD with CTCA and FFR CT . The primary end point is the per-patient sensitivity and negative predictive value of CTCA and FFR CT for identifying anatomical and physiologically significant CAD compared to ICA and invasive FFR. The safety end point is the incidence of symptomatic hypotension or bradycardia requiring intervention following the administration of nitroglycerin or β-blocker medications. Feasibility end points include the incidence of noninterpretable CTCA scans and CTCA scans not adequate for FFR analysis. Other utility end points include specificity, positive predictive value, and accuracy of CTCA and FFR CT . Lastly, the potential of a CTCA and FFR CT guided strategy to defer pre-TAVR ICA will be assessed. Conclusions FUTURE-AS will characterize the utility, safety, and feasibility of CTCA and FFR CT for coronary assessment pre-TAVR.
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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 | 0.051 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".