Abstract 14391: Biomarkers, Proteomics, and Echocardiography Lend Insights into Cardiovascular Death in HFrEF: A VICTORIA Substudy
Bibliographic record
Abstract
Background: Using data from the VICTORIA ECHO substudy in high-risk patients with heart failure with reduced ejection fraction (HFrEF), we evaluated the potential complementary role of cardiac function, biomarkers, and proteomics in association with cardiovascular death (CVD), a key component of the primary composite (HF hospitalization or CVD). Hypothesis: Measurement of cardiac biomarkers, proteins, and ECHO parameters may provide insights into the mechanism of CVD in HFrEF. Methods: At baseline, left ventricular ejection fraction (LVEF) and LV end-systolic volume index (LVESVI) on ECHO, 6 quantitative biomarkers, and 92 proteins (Olink CVD III panel) were assessed by blinded core labs. Adjusted (MAGGIC) associations and HRs (95% CIs) are presented. Proteomics associations were adjusted for false discovery rate. Results: Of 583 patients, 96 (16.5%) had CVD (21.6/100 pt-years) a median 8 months (IQR 6-11) from randomization. See Table for MAGGIC-adjusted HRs for LVEF, LVESVI, and biomarkers. NT-proBNP was most strongly associated with CVD (HR per doubling 1.53 [1.32-1.77]). Proteins significantly associated with CVD were cathepsin D (2.32 [1.62-3.32]), matrix metalloproteinase-2 (2.31 [1.45-3.68]), IL-2 receptor (1.74 [1.40-2.14]), transferrin receptor (1.64 [1.31-2.04]) and tumor necrosis superfamily (1.88 [1.41-2.50]) (Fig). Conclusions: After MAGGIC adjustment, a rise in LVESVI, but not LVEF, was associated with CVD. Upregulation of selected biomarkers/proteins related to myocardial wall stress, extracellular matrix turnover, inflammation, and oxidative stress were also associated with CVD and may serve as novel future therapeutic targets in HFrEF.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".