Quantitative myocardial perfusion in coronary artery disease: A perfusion mapping study.


Journal

Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850

Informations de publication

Date de publication:
09 2019
Historique:
received: 13 12 2018
revised: 11 01 2019
accepted: 11 01 2019
pubmed: 27 1 2019
medline: 22 10 2020
entrez: 27 1 2019
Statut: ppublish

Résumé

Cardiac MR stress perfusion remains a qualitative technique in clinical practice due to technical and postprocessing challenges. However, automated inline perfusion mapping now permits myocardial blood flow (MBF, ml/g/min) quantification on-the-fly without user input. To investigate the diagnostic performance of this novel technique in detecting occlusive coronary artery disease (CAD) in patients scheduled to undergo coronary angiography. Prospective, observational. Fifty patients with suspected CAD and 24 healthy volunteers. 1.5T. SEQUENCE: "Dual" sequence multislice 2D saturation recovery. All patients underwent cardiac MR with perfusion mapping and invasive coronary angiography; the healthy volunteers had MR with perfusion mapping alone. Comparison between numerical variables was performed using an independent t-test. Receiver operator characteristic (ROC) curves were generated for transmyocardial, endocardial stress MBF, and myocardial perfusion reserve (MPR, the stress:rest MBF ratio) to diagnose severe (>70%) stenoses as measured by 3D quantitative coronary angiography (QCA). ROC curves were compared by the method of DeLong et al. RESULTS: Compared with volunteers, patients had lower stress MBF and MPR even in vessels with <50% stenosis (2.00 vs. 3.08 ml/g/min, respectively). As stenosis severity increased (<50%, 50-70%, >70%), MBF and MPR decreased. To diagnose occlusive (>70%) CAD, endocardial and transmyocardial stress MBF were superior to MPR (area under the curve 0.92 [95% CI 0.86-0.97] vs. 0.90 [95% CI 0.84-0.95] and 0.80 [95% CI 0.72-0.87], respectively). An endocardial threshold of 1.31 ml/g/min provided a per-coronary artery sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 90%, 82%, 50%, and 98%, with a per-patient diagnostic performance of 100%, 66%, 57%, and 100%, respectively. Perfusion mapping can diagnose occlusive CAD with high accuracy and, in particular, high sensitivity and NPV make it a potential "rule-out" test. 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:756-762.

Sections du résumé

BACKGROUND
Cardiac MR stress perfusion remains a qualitative technique in clinical practice due to technical and postprocessing challenges. However, automated inline perfusion mapping now permits myocardial blood flow (MBF, ml/g/min) quantification on-the-fly without user input.
PURPOSE
To investigate the diagnostic performance of this novel technique in detecting occlusive coronary artery disease (CAD) in patients scheduled to undergo coronary angiography.
STUDY TYPE
Prospective, observational.
SUBJECTS
Fifty patients with suspected CAD and 24 healthy volunteers.
FIELD STRENGTH
1.5T. SEQUENCE: "Dual" sequence multislice 2D saturation recovery.
ASSESSMENT
All patients underwent cardiac MR with perfusion mapping and invasive coronary angiography; the healthy volunteers had MR with perfusion mapping alone.
STATISTICAL TESTS
Comparison between numerical variables was performed using an independent t-test. Receiver operator characteristic (ROC) curves were generated for transmyocardial, endocardial stress MBF, and myocardial perfusion reserve (MPR, the stress:rest MBF ratio) to diagnose severe (>70%) stenoses as measured by 3D quantitative coronary angiography (QCA). ROC curves were compared by the method of DeLong et al. RESULTS: Compared with volunteers, patients had lower stress MBF and MPR even in vessels with <50% stenosis (2.00 vs. 3.08 ml/g/min, respectively). As stenosis severity increased (<50%, 50-70%, >70%), MBF and MPR decreased. To diagnose occlusive (>70%) CAD, endocardial and transmyocardial stress MBF were superior to MPR (area under the curve 0.92 [95% CI 0.86-0.97] vs. 0.90 [95% CI 0.84-0.95] and 0.80 [95% CI 0.72-0.87], respectively). An endocardial threshold of 1.31 ml/g/min provided a per-coronary artery sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 90%, 82%, 50%, and 98%, with a per-patient diagnostic performance of 100%, 66%, 57%, and 100%, respectively.
DATA CONCLUSION
Perfusion mapping can diagnose occlusive CAD with high accuracy and, in particular, high sensitivity and NPV make it a potential "rule-out" test.
LEVEL OF EVIDENCE
1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:756-762.

Identifiants

pubmed: 30684288
doi: 10.1002/jmri.26668
pmc: PMC6767569
doi:

Substances chimiques

Contrast Media 0
Gadolinium AU0V1LM3JT

Types de publication

Journal Article Observational Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

756-762

Subventions

Organisme : British Heart Foundation
ID : FS/17/34/32901
Pays : United Kingdom
Organisme : British Heart Foundation
ID : FS/18/83/34025
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RG/16/1/32092
Pays : United Kingdom

Informations de copyright

© 2019 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

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Auteurs

Kristopher D Knott (KD)

University College London, Institute of Cardiovascular Science, London, UK.
Barts Heart Centre, St Bartholomew's Hospital, London, UK.

Claudia Camaioni (C)

Barts Heart Centre, St Bartholomew's Hospital, London, UK.

Anantharaman Ramasamy (A)

University College London, Institute of Cardiovascular Science, London, UK.
Barts Heart Centre, St Bartholomew's Hospital, London, UK.

Joao A Augusto (JA)

University College London, Institute of Cardiovascular Science, London, UK.
Barts Heart Centre, St Bartholomew's Hospital, London, UK.

Anish N Bhuva (AN)

University College London, Institute of Cardiovascular Science, London, UK.
Barts Heart Centre, St Bartholomew's Hospital, London, UK.

Hui Xue (H)

National Heart, Lung, and Blood Institute, National Institutes of Health, DHHS, Bethesda, Maryland, USA.

Charlotte Manisty (C)

University College London, Institute of Cardiovascular Science, London, UK.
Barts Heart Centre, St Bartholomew's Hospital, London, UK.

Rebecca K Hughes (RK)

University College London, Institute of Cardiovascular Science, London, UK.
Barts Heart Centre, St Bartholomew's Hospital, London, UK.

Louise A E Brown (LAE)

Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.

Rajiv Amersey (R)

Barts Heart Centre, St Bartholomew's Hospital, London, UK.

Christos Bourantas (C)

University College London, Institute of Cardiovascular Science, London, UK.
Barts Heart Centre, St Bartholomew's Hospital, London, UK.

Peter Kellman (P)

National Heart, Lung, and Blood Institute, National Institutes of Health, DHHS, Bethesda, Maryland, USA.

Sven Plein (S)

Department of Biomedical Imaging Science, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.

James C Moon (JC)

University College London, Institute of Cardiovascular Science, London, UK.
Barts Heart Centre, St Bartholomew's Hospital, London, UK.

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