Identification of the unstable carotid atherosclerotic plaque: From bench to clinical practice
Bibliographic record
Abstract
Cardiovascular disease is the leading cause of death worldwide and accounts for approximately 30% of deaths each year in Canada. Indeed there are approximately 62,000 strokes in Canada each year causing a deep burden on society. It is clear that methods to determine which patients are at highest risk for stroke are greatly needed. Current guidelines suggest surgical management for carotid plaques based only on stenosis. However, it is well understood that stenosis is an incomplete indicator of plaque instability and that plaque morphology may play a more important role in determining carotid plaque instability. With this idea in mind, numerous groups have made progress in identifying unstable carotid plaques based on visual classifications of echodensity and texture, computer assisted methods of echodensity measurement, and more recently computer assisted methods of texture classification. Herein we discuss the results from three manuscripts published as part of my doctoral thesis, with the objective of better identifying the unstable carotid plaque. We have used two approaches to this problem: digital image analysis (echodensity and texture measurement) of carotid plaque ultrasound images and the measurement of a novel biomarker, cholesterol efflux capacity. Firstly, we have performed a validation study of the digital image analysis program in order to determine which imaging features could predict plaque instability assessed by the 'gold standard' histology. We identified combinations of plaque morphological features from image analysis that can predict histological features of instability and also determined that unstable carotid plaques appear echolucent and homogenous on ultrasound. Secondly, we applied this image analysis program in patients with bilateral carotid stenosis, of which one side was undergoing surgery. We investigated whether features of instability in a high-grade stenosis plaque (undergoing surgery) were correlated with features of instability in the contralateral plaque (any stenosis - high or low-grade). We found moderate correlation in the whole population and that correlation of morphological features between sides increases when the patient has bilateral hemodynamically significant stenosis. Lastly, we investigated the association of cholesterol efflux capacity, a metric of high-density lipoprotein quality, with severity of carotid atherosclerosis as assessed by stenosis, histological plaque instability, and cerebrovascular symptomatology. We noted significant inverse associations between cholesterol efflux capacity, carotid stenosis, and plaque instability. However, we did not identify associations with cerebrovascular symptomatology. The results of these studies taken together, improve on our understanding of unstable carotid plaques, and may be implemented into clinical practice in the near future to better identify patients at high-risk for plaque rupture and stroke.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".