Impact of Lipoprotein(a) on Valvular and Cardiovascular Outcomes in Patients With Calcific Aortic Valve Stenosis
Bibliographic record
Abstract
BACKGROUND: Lp(a) (lipoprotein(a)) is an independent risk factor for calcific aortic valve stenosis (CAVS). Whether patients with CAVS and high Lp(a) levels are at higher risk of valvular or cardiovascular events is unknown. The aim of this study is to determine whether higher Lp(a) levels are associated with valvular and cardiovascular outcomes in patients with CAVS. METHODS AND RESULTS: We identified 1962 patients from the UK Biobank with an electronic health record or self-reported CAVS diagnosis but who did not previously undergo aortic valve replacement (AVR) and had a minimal follow-up time of 2.5 years. Cox proportional hazard regression was used to evaluate the effect of Lp(a) on AVR, AVR or cardiac death, and valvular or cardiovascular events (AVR, cardiac death, myocardial infarction, stroke, heart failure, or coronary artery bypass grafting). The maximal follow-up time was set to 5 years. During the follow-up, 198 patients underwent AVR, 260 had AVR or cardiac death, and 435 had at least 1 valvular or cardiovascular event. Patients with Lp(a) levels ≥125 versus <125 nmol/L were at higher risk of AVR (hazard ratio [HR], 1.58 [95% CI, 1.17-2.12]), AVR or cardiac death (HR, 1.43 [95% CI, 1.10-1.86]), and cardiovascular or valvular events (HR, 1.36 [95% CI, 1.11-1.68]). Point estimates were comparable in men versus women, younger versus older patients, and in patients with higher versus lower plasma C-reactive protein levels. CONCLUSIONS: In patients with CAVS, Lp(a) levels predicted a higher risk of valvular and cardiovascular outcomes. The impact of Lp(a)-lowering therapies on valvular and cardiovascular health should be assessed in a long-term randomized clinical trial.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".