Author + information
- Received November 27, 2018
- Revision received January 24, 2019
- Accepted February 22, 2019
- Published online June 12, 2019.
- Yuka Otaki, MDa,
- Julian Betancur, PhDa,
- Tali Sharir, MDb,
- Lien-Hsin Hu, MDa,c,
- Heidi Gransar, MSa,
- Joanna X. Liang, BAa,
- Peyman N. Azadani, MDa,
- Andrew J. Einstein, MD, PhDd,
- Mathews B. Fish, MDe,
- Terrence D. Ruddy, MDf,
- Philipp A. Kaufmann, MDg,
- Albert J. Sinusas, MDh,
- Edward J. Miller, MD, PhDh,
- Timothy M. Bateman, MDi,
- Sharmila Dorbala, MD, MPHj,
- Marcelo Di Carli, MDj,
- Balaji K. Tamarappoo, MDa,
- Guido Germano, PhDa,
- Damini Dey, PhDa,
- Daniel S. Berman, MDa and
- Piotr J. Slomka, PhDa,∗ ()
- aDivision of Nuclear Medicine, Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- bDepartment of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, and Ben Gurion University of the Negev, Beer Sheba, Israel
- cDepartment of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- dDivision of Cardiology, Department of Medicine and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York
- eOregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon
- fDivision of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
- gDepartment of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
- hSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut
- iCardiovascular Imaging Technologies LLC, Kansas City, Missouri
- jDepartment of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women’s Hospital, Boston, Massachusetts
- ↵∗Address for correspondence:
Dr. Piotr Slomka, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Suite A047N, Los Angeles, California 90048.
Objectives This study compared the ability of automated myocardial perfusion imaging analysis to predict major adverse cardiac events (MACE) to that of visual analysis.
Background Quantitative analysis has not been compared with clinical visual analysis in prognostic studies.
Methods A total of 19,495 patients from the multicenter REFINE SPECT (REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT) study (64 ± 12 years of age, 56% males) undergoing stress Tc-99m-labeled single-photon emission computed tomography (SPECT) myocardial perfusion imaging were followed for 4.5 ± 1.7 years for MACE. Perfusion abnormalities were assessed visually and categorized as normal, probably normal, equivocal, or abnormal. Stress total perfusion deficit (TPD), quantified automatically, was categorized as TPD = 0%, TPD >0% to <1%, ≤1% to <3%, ≤3% to <5%, ≤5% to ≤10%, or TPD >10%. MACE consisted of death, nonfatal myocardial infarction, unstable angina, or late revascularization (>90 days). Kaplan-Meier and Cox proportional hazards analyses were performed to test the performance of visual and quantitative assessments in predicting MACE.
Results During follow-up examinations, 2,760 (14.2%) MACE occurred. MACE rates increased with worsening of visual assessments, that is, the rate for normal MACE was 2.0%, 3.2% for probably normal, 4.2% for equivocal, and 7.4% for abnormal (all p < 0.001). MACE rates increased with increasing stress TPD from 1.3% for the TPD category of 0% to 7.8% for the TPD category of >10% (p < 0.0001). The adjusted hazard ratio (HR) for MACE increased even in equivocal assessment (HR: 1.56; 95% confidence interval [CI]: 1.37 to 1.78) and in the TPD category of ≤3% to <5% (HR: 1.74; 95% CI: 1.41 to 2.14; all p < 0.001). The rate of MACE in patients visually assessed as normal still increased from 1.3% (TPD = 0%) to 3.4% (TPD ≥5%) (p < 0.0001).
Conclusions Quantitative analysis allows precise granular risk stratification in comparison to visual reading, even for cases with normal clinical reading.
Supported by U.S. National Institutes of Health/National Heart, Lung, and Blood Institute grant R01HL089765. The content of this paper is the sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Drs. Germano, Berman, and Slomka participate in software royalties for QPS software at Cedars-Sinai Medical Center. Dr. Slomka has received research grant support from Siemens Medical Systems. Dr. Einstein has received research grants from Roche Medical Systems and Canon Medical Systems; and is a consultant for GE Healthcare. Dr. Ruddy has received research grants from GE Healthcare and Advanced Accelerator Applications. Dr. Miller has received grant support from and is a consultant for GE Healthcare. Dr. Dorbala is a consultant for GE Healthcare, Proclara, and Pfizer. Dr. Di Carli has received institutional grants from Spectrum Dynamics and Gilead; and is a consultant for Sanofi and General Electric.
- Received November 27, 2018.
- Revision received January 24, 2019.
- Accepted February 22, 2019.
- 2019 American College of Cardiology Foundation
This article requires a subscription or purchase to view the full text. If you are a subscriber or member, click Login or the Subscribe link (top menu above) to access this article.