Quantification of myocardial blood flow using stress cardiac magnetic resonance for the detection of coronary artery disease
Notice bibliographique
Résumé
Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): GE Healthcare. Background Myocardial blood flow (MBF) analysis using stress cardiac magnetic resonance (CMR) has been shown to detect obstructive coronary artery disease (CAD); however, evaluation of its diagnostic performance has primarily been limited to single-center studies. AQUA-MBF (Assessment of QUAntitative MBF using stress CMR) is an international study with the goal of assessing the diagnostic performance of stress MBF for the detection of CAD. In this study, we aim to determine how stress MBF assessment compared against visual analysis (VA) of stress CMR images for the detection of CAD. Methods. 144 individuals (89 (62%) men, age 62±16 years, 97 (68%) hypertension, 54 (38%) diabetes, 92 (64%) hyperlipidemia) from 9 centers who underwent dual sequence stress CMR (1.5T or 3.0T GE Healthcare) and also had either a coronary computed tomography angiography (CTA, n=31), invasive coronary angiogram (ICA, n=95), or low pre-test probability for CAD (n=18) were included. CAD was defined as the presence of: (1) a stenosis ≥50% in the left main coronary artery or ≥70% in the 1 major vessel based on ICA or CTA or (2) an invasive fractional flow reserve (FFR) ≤ 0.8. Absence of obstructive coronary disease (NOCAD) was defined as a no history of myocardial infarction and stenosis <50% by ICA or CTA, 50–70% stenosis with FFR>0.8, or a young individual with no cardiac risk factors. Myocardial perfusion imaging was performed during first pass perfusion of a gadolinium-based contrast agent following the administration of adenosine or regadenoson with a low-resolution image acquired to assess the arterial input function and 2–3 short axis slices acquired to assess myocardial perfusion. VA was performed by 2 experienced cardiologists who assigned a grade of 1–5 based on the probability a study was abnormal. Stress MBF values were determined for each of the 16 myocardial segments using Fermi deconvolution (CircleCVI). The global stress MBF was calculated as the average value of two segments with the lowest values from each of the three coronary artery territories. Unpaired t-test was used to compare stress MBF values between CAD vs NOCAD. Receiver-operating characteristics curves were used to determine diagnostic performance. Results. 60 patients had CAD (20: 1-vessel, 26: 2-vessel, and 14: 3-vessel) (Figure 1A) while 84 had NOCAD (Figure 1B). The global stress MBF in CAD was lower than in NOCAD (1.63±0.52ml/g/min vs 2.41±0.68ml/g/min, p<0.0001) (Figure 2A). Stress MBF had a higher AUC than VA (reader 1 – 0.83 vs 0.73, p = 0.07: reader 2–0.83 vs 0.71, p = 0.02). The optimal stress MBF cut-off value for detecting CAD was 2.05ml/g/min (Figure 2B). Conclusions. In this multicenter study, we show that global stress MBF reported as a single value can identify patients with CAD at least as accurately as VA performed by physicians experienced in the interpretation of stress CMR images.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 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) | 0,000 | 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 tête enseignante, 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 ».