Abstract 15422: Diffuse Myocardial Fibrosis Predicts Outcome in Moderate and Asymptomatic Severe Aortic Stenosis
Bibliographic record
Abstract
Introduction: Patients with moderate or asymptomatic severe aortic stenosis (AS) are at increased risk of cardiovascular events, but markers to guide intervention are lacking. We investigated the use of cardiovascular magnetic resonance (CMR) to identify drivers of outcome. Methods: Moderate (aortic valve area [AVA] 1.0-1.5 cm2) or asymptomatic severe AS patients (AVA≤1.0 cm2 and NYHA functional class I-II) (n=457) were prospectively enrolled from five international sites and parameters including extracellular volume fraction (ECV%) and late gadolinium enhancement were analyzed on CMR. Clinical events were defined as all-cause mortality and admission for heart failure. Results: Moderate AS patients had smaller left ventricular (LV) mass and less diastolic dysfunction compared to asymptomatic severe AS patients, but showed no difference in the extent of myocardial fibrosis and scar. During median 5.7-years follow-up, 83 clinical events occurred. Patients with clinical events had smaller AVA, greater diastolic dysfunction, and higher ECV% (median [interquartile range] ECV% 26.3% [24.3-29.8] vs 28.2% [25.5-30.8], P=0.003). There was a stepwise increase of clinical events across ECV tertiles. ECV% was associated with clinical outcome (adjusted HR 1.05, 95% CI 1.00–1.09, P=0.035), independent of AVA and aortic valve replacement as a time-dependent covariate. The prognostic significance of ECV% was consistent when AS severity was alternatively defined using aortic peak velocity and mean pressure gradient, and also across subgroups by AS severity and symptoms, AVA, aortic peak velocity, mean pressure gradient, and left ventricular ejection fraction (Figure). Conclusions: Increased diffuse myocardial fibrosis is associated with poor outcomes in moderate and asymptomatic severe AS patients and can help identify those who require closer surveillance and possibly earlier intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".