Carotid Plaque Border Ulceration is Associated With Coronary Artery Disease and Future Cardiovascular Events
Bibliographic record
Abstract
BACKGROUND: Atherosclerosis is a major contributor to cardiovascular events. Increasing evidence suggests that plaque vulnerability to rupture, rather than plaque quantity, is the major determinant of cardiovascular risk. Plaque surface ulceration has been associated with intraplaque hemorrhage and rupture; thus is a useful marker for assessing plaque vulnerability. Fractal analysis has been validated as a tool to quantify carotid plaque border irregularity. The objective of this study was to use fractal analysis to assess the relationship between ultrasound-detected carotid plaque surface irregularity and cardiovascular risk. METHODS: Carotid B-mode ultrasound was performed on 458 consecutive participants referred for coronary angiography. Fractal dimension (FD) was computed for all carotid plaque lesions using B-mode ultrasound images and then averaged to obtain an overall FD value. RESULTS: The mean FD across the study population was 1.1628±0.043. FD was higher in participants with significant CAD (≥50% stenosis) (1.1679±0.045 vs. 1.1545±0.039, p=0.003). Multivariate logistic regression demonstrated that FD was a significant independent risk factor for significant CAD. Furthermore, Kaplan Meier analysis showed that an FD>1.1500 was associated with an increased occurrence of 30-day cardiovascular events (p=0.045). Multivariate cox proportional hazards analysis demonstrated that FD was a significant and independent contributor to 30-day cardiovascular events (RR: 1.85, 95% confidence interval: 1.02-3.54; p=0.04). CONCLUSION: Fractal analysis is a mathematical tool with emerging use in the field of cardiology. The relationship between the FD of carotid plaque lesions and cardiovascular outcomes established in the present study could facilitate its use as an imaging biomarker for cardiovascular risk.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".