Myocardial Blood Flow Quantification Using Stress Cardiac Magnetic Resonance Improves Detection of Coronary Artery Disease

Shuo Wang; Paul Kim; Haonan Wang; Ming-Yen Ng; Andrew E Arai; Amita Singh; Saima Mushtaq; Tsun Hei Sin; Yuko Tada; Elizabeth Hillier; Ruyun Jin; Christian Østergaard Mariager; Michael Salerno; Gianluca Pontone; Javier Urmeneta Ulloa; Ibrahim M Saeed; Hena Patel; Victor Goh; Simon Madsen; Won Yong Kim; Mayil Singram Krishnam; Vicente Martínez de Vega; Alicia M Maceira; Jose V Monmeneu; Aju P Pazhenkottil; Alborz Amir-Khalili; Mitchel Benovoy; Silke Friedrich; Martin A Janich; Matthias G Friedrich; Amit R Patel

doi:10.1016/j.jcmg.2024.07.023

Myocardial Blood Flow Quantification Using Stress Cardiac Magnetic Resonance Improves Detection of Coronary Artery Disease

JACC Cardiovasc Imaging. 2024 Sep 6:S1936-878X(24)00313-9. doi: 10.1016/j.jcmg.2024.07.023. Online ahead of print.

Authors

Shuo Wang¹, Paul Kim², Haonan Wang³, Ming-Yen Ng⁴, Andrew E Arai⁵, Amita Singh⁶, Saima Mushtaq⁷, Tsun Hei Sin⁴, Yuko Tada², Elizabeth Hillier⁸, Ruyun Jin⁹, Christian Østergaard Mariager¹⁰, Michael Salerno¹¹, Gianluca Pontone¹², Javier Urmeneta Ulloa¹³, Ibrahim M Saeed¹⁴, Hena Patel¹⁵, Victor Goh¹⁶, Simon Madsen¹⁰, Won Yong Kim¹⁷, Mayil Singram Krishnam¹⁸, Vicente Martínez de Vega¹³, Alicia M Maceira¹⁹, Jose V Monmeneu¹⁹, Aju P Pazhenkottil²⁰, Alborz Amir-Khalili²¹, Mitchel Benovoy²², Silke Friedrich²², Martin A Janich²³, Matthias G Friedrich²⁴, Amit R Patel²⁵

Affiliations

¹ Division of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, Virginia, USA.
² Division of Medicine, University of California, San Diego, San Diego, California, USA.
³ GE HealthCare, Waukesha, Wisconsin, USA.
⁴ Department of Diagnostic Radiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China.
⁵ Division of Cardiovascular Medicine and Department of Radiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
⁶ Department of Cardiology, Central DuPage Hospital, Winfield, Illinois, USA.
⁷ Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy.
⁸ Department of Medicine and Diagnostic Radiology, McGill University Health Centre, Montreal, Quebec, Canada; University of Alberta, Edmonton, Alberta, Canada.
⁹ Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA.
¹⁰ Department of Nuclear Medicine and PET Center, Aarhus University Hospital, Aarhus, Denmark.
¹¹ Department of Cardiology, Stanford University, Stanford, California, USA.
¹² Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
¹³ Department of Cardiology, Hospital Universitario Quironsalud, Madrid, Spain.
¹⁴ Virginia Heart, Falls Church, Virginia, USA; Inova Schar Heart and Vascular, Fairfax, Virginia, USA.
¹⁵ Department of Medicine, University of Chicago, Chicago, Illinois, USA.
¹⁶ Hong Kong Sanatorium and Hospital, Hong Kong, China.
¹⁷ Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
¹⁸ Department of Diagnostic Radiology, Stanford University, Stanford, California, USA.
¹⁹ Department of Cardiology, Ascires Biomedical Group, Valencia, Spain.
²⁰ Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.
²¹ Circle Cardiovascular Imaging, Calgary, Alberta, Canada.
²² Area19 Medical, Montreal, Quebec, Canada.
²³ GE HealthCare, Munich, Germany.
²⁴ Department of Medicine and Diagnostic Radiology, McGill University Health Centre, Montreal, Quebec, Canada; Research Institute, McGill University Health Centre, Montreal, Quebec, Canada.
²⁵ Division of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, Virginia, USA. Electronic address: [email protected].

PMID: 39297850
DOI: 10.1016/j.jcmg.2024.07.023

Abstract

Background: Myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) using stress cardiovascular magnetic resonance (CMR) have been shown to identify epicardial coronary artery disease. However, comparative analysis between quantitative perfusion and conventional qualitative assessment (QA) remains limited.

Objectives: The aim of this multicenter study was to test the hypothesis that quantitative stress MBF (sMBF) and MPR analysis can identify obstructive coronary artery disease (obCAD) with comparable performance as QA of stress CMR performed by experienced physicians in interpretation.

Methods: The analysis included 127 individuals (mean age 62 ± 16 years, 84 men [67%]) who underwent stress CMR. obCAD was defined as the presence of stenosis ≥50% in the left main coronary artery or ≥70% in a major vessel. Each patient, coronary territory, and myocardial segment was categorized as having either obCAD or no obCAD (noCAD). Global, per coronary territory, and segmental MBF and MPR values were calculated. QA was performed by 4 CMR experts.

Results: At the patient level, global sMBF and MPR were significantly lower in subjects with obCAD than in those with noCAD, with median values of sMBF of 1.5 mL/g/min (Q1-Q3: 1.2-1.8 mL/g/min) vs 2.4 mL/g/min (Q1-Q3: 2.1-2.7 mL/g/min) (P < 0.001) and median values of MPR of 1.3 (Q1-Q3: 1.0-1.6) vs 2.1 (Q1-Q3: 1.6-2.7) (P < 0.001). At the coronary artery level, sMBF and MPR were also significantly lower in vessels with obCAD compared with those with noCAD. Global sMBF and MPR had areas under the curve (AUCs) of 0.90 (95% CI: 0.84-0.96) and 0.86 (95% CI: 0.80-0.93). The AUCs for QA by 4 physicians ranged between 0.69 and 0.88. The AUC for global sMBF and MPR was significantly better than the average AUC for QA.

Conclusions: This study demonstrates that sMBF and MPR using dual-sequence stress CMR can identify obCAD more accurately than qualitative analysis by experienced CMR readers.

Keywords: obstructive coronary artery disease; quantitative perfusion; stress cardiovascular magnetic resonance.