Association between plasma lipoprotein levels and bioprosthetic valve structural degeneration
Bibliographic record
Abstract
Introduction Structural valve degeneration (SVD) leads to the failure of aortic valve bioprostheses. It is suspected that lipid-derived factors could play a role in SVD. We hypothesised that oxidised low-density lipoprotein (OxLDL), OxLDL/LDL, OxLDL/high-density lipoprotein (OxLDL/HDL) and proprotein convertase subtilisin/kexin 9 (PCSK9) may be associated with SVD. Methods We included 199 patients who underwent an aortic valve replacement with a bioprosthesis and had an echocardiography follow-up to evaluate the function of the prosthesis. SVD was defined as an increase in mean transprosthetic gradient (≥10 mm Hg) or a worsening of transprosthetic regurgitation (≥1/3) during the follow-up. Results After a mean follow-up of 8±3.5 years, 41(21%) patients developed SVD. The univariate predictors of SVD were LDL (p=0.03), apolipoprotein B (p=0.01), OxLDL (p=0.02), OxLDL/HDL (p=0.009) and LDL associated with small, dense particles (LDL-C <255Å ) (p=0.02). In a model adjusted for covariates, only OxLDL/HDL (OR 1.49, 95%CI 1.08 to 2.07 per 10 units, p=0.01) remained associated with SVD. There was a significant interaction between OxLDL/HDL and PCSK9 on SVD (p=0.05). After adjustment, compared with patients with low OxLDL/HDL (median, <25.4) and low PCSK9 (median, <298 ng/mL) (referent), patients with both an elevated OxLDL/HDL ratio and PCSK9 had a higher risk of SVD (OR 2.93, 95% CI 1.02 to 9.29, p=0.04). Conclusions OxLDL/HDL ratio is independently associated with SVD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".