Reversible myocardial perfusion defects in patients not suffering from obstructive epicardial coronary artery disease as assessed by coronary angiography.
Notice bibliographique
Résumé
In approximately 10-30% of patients presenting with angina complaints, normal or non-obstructive coronary arteries are found on angiography. In this review paper, available literature on the underlying pathophysiological substrate explaining these discrepancies is reviewed. Both histological studies as well as studies using intravascular ultrasound e.g. the PROSPECT trial, show that epicardial coronary vessel significant lumen stenosis may be delayed until a plaque occupies 40% of the internal elastic lamina area. Limited available data suggest that these angiographically undetectable plaques are associated with an abnormal vasodilation capacity of the coronary circulation and may results in reversible perfusion defects on myocardial perfusion imaging (MPI). Organic non-atherosclerotic causes of epicardial coronary artery disease such as anomalous coronary arteries that course between the aorta and pulmonary artery, myocardial bridging and coronary vasospasm may also contribute to MPI results suggesting the presence of ischemia in the presence of normal coronary arteries on coronary angiography. Additional causes of reversible perfusion defects on MPI in the presence of a normal coronary angiogram are intraventricular conduction disturbances. The existence of reversible perfusion defects in the anteroseptal region in most of the patients suffering from left bundle branch block (LBBB) on MPI following physical exercise as stressor is well documented. As the observed reduced septal uptake of both 201Tl and 99mTc-sestamibi/tetrofosmin in LBBB reflects coronary autoregulation in response to lower oxygen demands, not surprisingly, dipyridamole which uniformly exploits flow reserve, has proven more accurate for the diagnosis of coronary artery disease (CAD) in patients suffering from LBBB. Although patients with a permanent ventricular pacemaker have a similar conduction abnormality as patients presenting with a LBBB, most of the defects found on MPI imaging in this patient population (in up to 78% of patients with a normal coronary angiogram that area continuously paced) are localized in the inferoposterior (71%), apical (50%) and inferoseptal (28%) wall; coronary flow velocities in the left anterior descending (LAD) and dominant coronary artery and coronary flow reserve are also significantly lower when compared to a control group. Contrary to what is seen in LBBB patients, dipyridamole stress does not significantly reduce the incidence of abnormalities found but limits the defects to the inferior wall. Furthermore, the frequency of abnormalities found on MPI increases over time with right ventricular outflow tract pacing. Previous histologic studies have shown that microvessel disease is often accompanied by a slow-flow phenomenon reflecting decreased resting flow velocity. Thus, not surprisingly, MPI reversible abnormalities in the presence of a normal coronary angiogram have been reported in a wide variety of diseases characterized by microvessel disease such as diabetes, systemic lupus erythematosus, Behçet's disease and metabolic syndrome. In these patients, low adiponectin and high lipoprotein(a) levels are found which are known to be associated with endothelial dysfunction, atherosclerosis and coronary artery disease. Furthermore, in these patients, limited available data suggest that reversible perfusion defects on MPI confer a significantly poorer prognosis both in terms of hard event rate (MI and cardiac death) and total event rate (MI, cardiac death or late revascularization). It is thus suggested that MPI could discriminate patients with a more severe prognosis. Finally, physical training in patients with primary microvascular angina appears to be associated with reduction of myocardial perfusion abnormalities.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».