Femoral Plaque Quantification by Ultrasound for the Prediction of Obstructive Coronary Artery Disease in Symptomatic Men and Women
Bibliographic record
Abstract
Cardiovascular risk remains difficult to assess, particularly in women. It is known that patients with ankle-brachial index (ABI)-indicated peripheral arterial disease (PAD) have an increased risk of cardiovascular morbidity and mortality. Femoral ultrasound, a sensitive marker of PAD, has potential value as a screening tool. However, the association of femoral plaque burden with coronary artery disease (CAD) severity and extent remains unknown. Furthermore, little information is available on sex differences in the burden of PAD and its relationship to CAD, a consideration that may improve patient risk management algorithms. To explore this, five hundred study participants (34% female) underwent bilateral carotid and femoral ultrasound within 24 hours of coronary angiography. A subset of 124 participants underwent ABI measurements. It was found that in women, increased combined common femoral and carotid bulb plaque area yielded the strongest association with significant CAD (odds ratio (OR) 7.3 (95% CI 3.5-16.8)), independently of age and traditional cardiac risk factors, while in men, this was achieved by increased combined plaque height at the carotid bulb and femoral bifurcation (OR 4.3 (CI 2.4-8.2)). Additionally, ultrasound-detected femoral plaque burden more accurately identified participants with significant CAD (area under the curve (AUC) = 0.731) than ABI (AUC = 0.578) (p = 0.02). Femoral plaque burden had a higher sensitivity (84%) than ABI (25%) for ruling out disease. In conclusion, we determined sex-specific markers of ultrasound-detected atherosclerotic disease that may improve risk stratification when used uniquely in women and men. A combined assessment of common femoral and carotid plaque area is most advantageous in women, while men may benefit most from a combined analysis of carotid and femoral bifurcation plaque height. Furthermore, femoral ultrasound, while being an equally safe, inexpensive, and rapid tool, more accurately predicts significant coronary disease than a traditional ABI assessment. Used clinically, vascular ultrasound may have immense value when incorporated into cardiovascular management algorithms, especially for those patients in which risk remains uncertain despite the use of conventional stratification tools.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".