Abstract 15210: Association of Circulating Biomarkers With Progressive Left Ventricular Remodeling and Hypertrophy in Patients With Aortic Stenosis: Results From the Progressa Study
Bibliographic record
Abstract
Introduction: Aortic stenosis (AS) is associated with progressive left ventricular (LV) remodeling and hypertrophy, ultimately leading to cardiac decompensation. Measurement of circulating biomarkers may be useful to identify AS patients with higher risk of maladaptive LV remodeling, a potent marker of the transition to heart failure. We hypothesized that multiple circulating biomarkers are associated with faster progression of LV hypertrophy in patients with AS. Methods: One hundred and forty-six asymptomatic AS patients (66±13years, 75% male) prospectively enrolled in the PROGRESSA study (NCT01679431) were included in this sub-analysis. All patients underwent Doppler-echocardiography to measure AS severity, LV mass indexed to body surface area (LVMi) and LV relative wall thickness (RWT). The annualized changes in LVMi and RWT were calculated between baseline and last follow-up visit (mean follow-up of 2.5±1.88 years). Results: At baseline, 16% of patients had mild (i.e. peak aortic jet velocity [Vpeak] 2.0-2.9 m/s),76% moderate (Vpeak 3.0-3.9 m/s), and 8% severe (Vpeak ≥4.0 m/s) AS, mean LVMi and RWT were 106±23 g/m 2 and 0.47±0.07 respectively. Among biomarkers, baseline level of triglycerides, D-dimer and tumor antigen 125(Ca-125) were significantly associated with LVMi progression rate (β:0.16,p≤0.05), (β:0.18,p≤0.05) and(β:-0.20,p≤0.05) respectively. Soluble (ST2) protein and alkaline phosphatase (ALP) were the only factors independently associated with the annualized change in RWT. In multivariate analysis adjusted for age, gender, comorbidities, AS severity, cardiac biomarkers; triglycerides and D-dimers remained significantly associated with faster progression rate of LVMi (all, p≤0.05), while lower level of CA125 remained significantly associated with LVMi annualized change (β=-0.21, p=0.01). After similar multivariate adjustment, higher levels of ST2 and ALP remained significantly associated with faster progression rate of RWT (all, p≤0.05). Conclusion: Several blood biomarkers were independently associated with faster progression of LV remodeling and hypertrophy in patients with AS. A multiple biomarker approach may be useful to assess LV health and predict the risk of early phases of heart failure in AS.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".