Automated calcium scores collected during myocardial perfusion imaging improve identification of obstructive coronary artery disease.
AP, Angina pectoris
AUC, Area under the curve
CABG, Coronary artery bypass grating
CAC, Coronary artery calcium
CAD, Coronary artery disease
CAG, Coronary angiography
CFR, Coronary flow reserve
CI, Confidence interval
CVD, Cardiovascular disease
Cardiovascular imaging
Coronary artery calcium
Deep learning
FFR, Fractional flow reserve
MBF, Myocardial blood flow
MI, myocardial infraction
MPI, Myocardial perfusion imaging
Myocardial perfusion imaging
NPV, Negative predictive value
OR, Odds ratio
Obstructive coronary artery disease
PCI, Percutaneous coronary intervention
PET/CT, Positron emission tomography/computed tomography
PPV, Positive predictive value
QCA, Quantitative coronary angiography
ROC, Receiver operator characteristic
SD, Standard deviation
SDS, Summed difference score
WMA, Wall motion abnormalities
Journal
International journal of cardiology. Heart & vasculature
ISSN: 2352-9067
Titre abrégé: Int J Cardiol Heart Vasc
Pays: Ireland
ID NLM: 101649525
Informations de publication
Date de publication:
Feb 2020
Feb 2020
Historique:
received:
01
08
2019
revised:
16
10
2019
accepted:
18
10
2019
entrez:
27
11
2019
pubmed:
27
11
2019
medline:
27
11
2019
Statut:
epublish
Résumé
Myocardial perfusion imaging (MPI) is an accurate noninvasive test for patients with suspected obstructive coronary artery disease (CAD) and coronary artery calcium (CAC) score is known to be a powerful predictor of cardiovascular events. Collection of CAC scores simultaneously with MPI is unexplored. We aimed to investigate whether automatically derived CAC scores during myocardial perfusion imaging would further improve the diagnostic accuracy of MPI to detect obstructive CAD. We analyzed 150 consecutive patients without a history of coronary revascularization with suspected obstructive CAD who were referred for 82Rb PET/CT and available coronary angiographic data. Myocardial perfusion was evaluated both semi quantitatively as well as quantitatively according to the European guidelines. CAC scores were automatically derived from the low-dose attenuation correction CT scans using previously developed software based on deep learning. Obstructive CAD was defined as stenosis >70% (or >50% in the left main coronary artery) and/or fractional flow reserve (FFR) ≤0.80. In total 58% of patients had obstructive CAD of which seventy-four percent were male. Addition of CAC scores to MPI and clinical predictors significantly improved the diagnostic accuracy of MPI to detect obstructive CAD. The area under the curve (AUC) increased from 0.87 to 0.91 (p: 0.025). Sensitivity and specificity analysis showed an incremental decrease in false negative tests with our MPI + CAC approach (n = 14 to n = 4), as a consequence an increase in false positive tests was seen (n = 11 to n = 28). CAC scores collected simultaneously with MPI improve the detection of obstructive coronary artery disease in patients without a history of coronary revascularization.
Sections du résumé
BACKGROUND
BACKGROUND
Myocardial perfusion imaging (MPI) is an accurate noninvasive test for patients with suspected obstructive coronary artery disease (CAD) and coronary artery calcium (CAC) score is known to be a powerful predictor of cardiovascular events. Collection of CAC scores simultaneously with MPI is unexplored.
AIM
OBJECTIVE
We aimed to investigate whether automatically derived CAC scores during myocardial perfusion imaging would further improve the diagnostic accuracy of MPI to detect obstructive CAD.
METHODS
METHODS
We analyzed 150 consecutive patients without a history of coronary revascularization with suspected obstructive CAD who were referred for 82Rb PET/CT and available coronary angiographic data. Myocardial perfusion was evaluated both semi quantitatively as well as quantitatively according to the European guidelines. CAC scores were automatically derived from the low-dose attenuation correction CT scans using previously developed software based on deep learning. Obstructive CAD was defined as stenosis >70% (or >50% in the left main coronary artery) and/or fractional flow reserve (FFR) ≤0.80.
RESULTS
RESULTS
In total 58% of patients had obstructive CAD of which seventy-four percent were male. Addition of CAC scores to MPI and clinical predictors significantly improved the diagnostic accuracy of MPI to detect obstructive CAD. The area under the curve (AUC) increased from 0.87 to 0.91 (p: 0.025). Sensitivity and specificity analysis showed an incremental decrease in false negative tests with our MPI + CAC approach (n = 14 to n = 4), as a consequence an increase in false positive tests was seen (n = 11 to n = 28).
CONCLUSION
CONCLUSIONS
CAC scores collected simultaneously with MPI improve the detection of obstructive coronary artery disease in patients without a history of coronary revascularization.
Identifiants
pubmed: 31768415
doi: 10.1016/j.ijcha.2019.100434
pii: S2352-9067(19)30208-8
pii: 100434
pmc: PMC6872848
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100434Informations de copyright
© 2019 Published by Elsevier B.V.
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