Integrating Perfusion with AI-derived Coronary Calcium on CT attenuation scans to improve selection of low-risk studies for stress-only SPECT MPI.
Authors
Affiliations (12)
Affiliations (12)
- Departments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Cardiac Sciences, University of Calgary, Calgary AB, Canada.
- Departments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Departments of Cardiology and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Departments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
- Division of Cardiology, Mount Sinai Morningside Hospital, New York, NY, USA.
- Departments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA.
- Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, N Y, USA.
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
- Cardiovascular Imaging Technologies LLC, Kansas City, MO, USA.
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland.
- Departments of Cardiology and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Departments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: [email protected].
Abstract
In many contemporary laboratories a completely normal stress perfusion SPECT-MPI is required for rest imaging cancelation. We hypothesized that an artificial intelligence (AI)-derived CAC score of 0 from computed tomography attenuation correction (CTAC) scans obtained during hybrid SPECT/CT, may identify additional patients at low risk of MACE who could be selected for stress-only imaging. Patients without known coronary artery disease who underwent SPECT/CT MPI and had stress total perfusion deficit (TPD) <5% were included. Stress TPD was categorized as no abnormality (stress TPD 0%) or minimal abnormality (stress TPD 1-4%). CAC was automatically quantified from the CTAC scans. We evaluated associations with major adverse cardiovascular events (MACE). In total, 6,884 patients (49.4% males and median age 63 years) were included. Of these, 9.7% experienced MACE (15% non-fatal MI, 2.7% unstable angina, 38.5% coronary revascularization and 43.8% deaths). Compared to patients with TPD 0%, those with TPD 1-4% and CAC 0 had lower MACE risk (hazard ratio [HR] 0.58; 95% confidence interval [CI] 0.45-0.76), while those with TPD 1-4% and CAC score>0 had a higher MACE risk (HR 1.90; 95%CI 1.56-2.30). Compared to canceling rest scans only in patients with normal perfusion (TPD 0%), by canceling rest scans in patients with CAC 0, more than twice as many rest scans (55% vs 25%) could be cancelled. Using AI-derived CAC 0 on CT scans with hybrid SPECT/CT in patients with a stress TPD<5% can double the proportion of patients in whom stress-only procedures could be safely performed.