Identification of the unstable carotid atherosclerotic plaque: From bench to clinical practice
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».