Lipoprotein (a) Distribution in Aortic Stenosis Patients
Bibliographic record
Abstract
BACKGROUND: There is currently no medical therapy that can slow down/stop the progression of aortic stenosis (AS). Novel drugs lowering lipoprotein(a) (Lp[a]), a proinflammatory particle linked to AS development, are currently under evaluation, but the proportion of patients with AS and elevated Lp(a) who might benefit from such therapy is not known. OBJECTIVES: The authors sought to characterize the prevalence of high Lp(a) in patients with AS to determine the role of future Lp(a)-lowering therapies. METHODS: In a nonselected Canadian population of AS patients seen in our specialized valve clinic, we assessed Lp(a) levels. High Lp(a) level was defined as an Lp(a) ≥100 nmol/L as per the Canadian Cardiovascular Society's Lipid Guidelines. RESULTS: Lp(a) levels were measured in 162 patients (mean age: 75 ± 10 years, 43% female, mean pressure gradient: 29 ± 14 mm Hg). Mean Lp(a) was 69 ± 79 nmol/L (median: 24 nmol/L, IQR: 19-91 nmol/L), and 39 patients (24%) had a high Lp(a) level. There were no differences in the mean Lp(a) or in the proportion of high Lp(a) levels with respect to sex, age, or AS severity (all P > 0.20). CONCLUSIONS: In this cross-sectional series of unselected AS patients followed in a valve clinic, only 1 in every 4 patients had a significantly elevated Lp(a) level. Novel Lp(a)-lowering therapies may have limited applicability to most patients with AS and highlight the need for further research into understanding the pathophysiology of AS and developing medical therapies targeting different pathways.
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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".