P3412Risk factors, biomarkers and framingham risk estimate fail to identify presence of subclinical atherosclerosis in young individual with family history of premature coronary artery disease
Bibliographic record
Abstract
Abstract Introduction Patients with family history of premature coronary artery disease (CAD) are at increased risk of CAD events at a younger age. Risk factor based approaches and clinical evaluation are most commonly used to assess these individuals. However, it has been recently shown that up to 50% of individual presenting with their first myocardial infarction (MI) were considered to be “low risk” prior to that event. MI is often a result of plaque rupture preceded by progression of subclinical atherosclerosis. Detection of subclinical atherosclerosis may therefore help target prevention of plaque progression. We assessed the value of clinical risk factor, biomarkers and Framingham Risk Score (FRS) in predicting subclinical atherosclerosis in individuals with a family history of premature CAD. Methods From 310 referrals, 222 individuals between the ages of 35 and 55 with a family history of premature CAD (CAD events in first-degree family members (male <55, female <65)) were enrolled for evaluation of risk of CAD. Those with familial hypercholesteremia (possible, probable or definite) were excluded. Patients underwent clinical and risk factor evaluations as well as Cardiac CT or Calcium Score (CS) to assess presence of subclinical / clinical atherosclerosis at the discretion of the treating physician. Results In this pilot, 141 individuals (59% male, mean age 45.9±6.0 years) completed evaluation, and 65 (46%) had evidence of subclinical atherosclerosis on CT coronary angiography or CT calcium score with a mean segment involvement score (SIS) of 2.8 and mean CS of 152, putting them above the 80th percentile for their age and sex. Aside from male sex, age, and smoking history, other traditional risk factors and biomarkers including diabetes mellitus, hypertension, total cholesterol, LDL-C, HDL-C and Cholesterol/HDL-C were not significantly different between those with or without subclinical atherosclerosis (Table 1). Table 1 Conclusion In young individuals with a family history of premature CAD, risk factors, biomarkers, and FRS failed to identify individuals with premature, subclinical atherosclerosis in this pilot study. Detection of subclinical atherosclerosis and early implementation of treatment with the aim of stabilizing plaques and stopping progression might prove vital in reducing events in these individuals. Further studies are warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".