Is Lipoprotein (a) a Risk Factor for Coronary Artery Ectasia?
Bibliographic record
Abstract
BACKGROUND: The pathophysiology of coronary artery ectasia (CAE) is under investigated and not well understood. Atherosclerosis is considered as the main etiologic factor for CAE in adults where more than 50% of CAE patients have atherosclerosis. Recently, lipoprotein (a) (Lp(a)) has emerged as a powerful risk factor for atherosclerosis and coronary artery disease (CAD). Serum levels of Lp(a) in patients with CAE have not been investigated. We assumed that Lp(a) may play a role in the pathogenesis of CAE. Principally, our study aims to identify whether Lp(a) is an independent risk factor for CAE. METHODS: Our study is a prospective pilot study. Study population was collected prospectively from pool of patients referred for elective cardiac catheterization at Jordan University Hospital (JUH) in the period extending from February 17, 2018 to June 31, 2018. Patients were referred for elective coronary angiography after being interviewed and physically examined by a cardiologist (HA). Patients with known history of CAD or who are already on anti-lipidemic drugs either documented in the medical records or by interviewing patients for history of revascularization were excluded from the study. RESULTS: Regarding the primary outcome, there was no significant difference in Lp(a) concentrations between normal and ectasia groups in the general sample (median: 17.5mg/dL vs. 20.4 mg/dL, P value = 0.38). CONCLUSIONS: Our study concludes that there is no detected relationship between elevated Lp(a) levels and developing CAE. CAE was more common in patients with low high-density lipoprotein (HDL) level (compared with patients with normal coronaries), higher total cholesterol level (compared with patients with non-obstructive stenosis) and higher hemoglobin A1c (HbA1c).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".