Abstract 2655: Correlates Of Aortic Valve Calcification In Aortic Stenosis: Insights From The Astronomer Study
Bibliographic record
Abstract
Aortic valve calcification (AVC), a common finding in aortic stenosis (AS), is a predictor of rapid AS progression and increased risk of cardiac events. We evaluated clinical, echocardiographic and biochemical correlates of AVC to gain insight into pathogenesis and to identify potential targets for intervention. Methods: The ASTRONOMER study (Aortic Stenosis Progression Observation: Measuring Effects of Rosuvastatin) is a randomized trial to assess cholesterol lowering using rosuvastatin on AS progression in mild to moderate AS. Patients with indication for cholesterol lowering were excluded. Severity of AVC was classified as none, mild, moderate and severe according to published criteria. Results: 272 patients (167 men, 105 women; mean age 58.1±13.6 years) have been enrolled with peak and mean AV gradients 41±11 and 23±8 mmHg respectively. Patients with none or mild AVC were compared with patients with moderate or severe AVC (Table ). ACE-I= angiotensin converting enzyme inhibitor, BAV=bicuspid aortic valve, BP=blood pressure, HDL-C=high-density lipoprotein cholesterol, LDL-C= low-density lipoprotein cholesterol, MAC=mitral annular calcification. Regression analysis showed that AVC was associated with age (p<0.001), male sex (p=0.01), systolic BP (p=0.002), LDL-C (p=0.05), MAC (p=0.01) and tricuspid AV (p=0.001). With adjustment for age, correlates of AVC were male sex with odds ratio (OD) 2.04, p<0.01, systolic BP (OD=1.02, p=0.096), and LDL-C (OD 1.44, p=0.077). Conclusions: In patients with AS, age and male sex are the main correlates of AVC. After adjusting for age, AVC is not associated with AV morphology but appears to be related to male sex, BP and LDL-C. Thus BP and LDL-C are promising modifiable targets to prevent AVC which in turn should reduce AS progression.
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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) | 0.000 | 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".