Eyal Klein1, Robert J H Miller1,2, Tali Sharir3,4, Andrew J Einstein5, Mathews B Fish6, Terrence D Ruddy7, Philipp A Kaufmann8, Albert J Sinusas9, Edward J Miller9, Timothy M Bateman10, Sharmila Dorbala11, Marcelo Di Carli11, Yuka Otaki1, Heidi Gransar1, Joanna X Liang1, Damini Dey1, Daniel S Berman1, Piotr J Slomka12. 1. Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. A047N, Los Angeles, CA, 90048, USA. 2. Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada. 3. Department of Nuclear Cardiology, Assuta Medical Center, Tel Aviv, Israel. 4. Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheba, Israel. 5. Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA. 6. Department of Nuclear Medicine, Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA. 7. Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada. 8. Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland. 9. Department of Internal Medicine, Section of Cardiovascular Medicine, Yale University, New Haven, CT, USA. 10. Cardiovascular Imaging Technologies LLC, Kansas City, MO, USA. 11. Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA. 12. Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. A047N, Los Angeles, CA, 90048, USA. Piotr.Slomka@cshs.org.
Abstract
BACKGROUND: Obese patients constitute a substantial proportion of patients referred for SPECT myocardial perfusion imaging (MPI), presenting a challenge of increased soft tissue attenuation. We investigated whether automated quantitative perfusion analysis can stratify risk among different obesity categories and whether two-view acquisition adds to prognostic assessment. METHODS: Participants were categorized according to body mass index (BMI). SPECT MPI was assessed visually and quantified automatically; combined total perfusion deficit (TPD) was evaluated. Kaplan-Meier and Cox proportional hazard analyses were used to assess major adverse cardiac event (MACE) risk. Prognostic accuracy for MACE was also compared. RESULTS: Patients were classified according to BMI: BMI < 30, 30 ≤ BMI < 35, BMI ≥ 35. In adjusted analysis, each category of increasing stress TPD was associated with increased MACE risk, except for 1% ≤ TPD < 5% and 5% ≤ TPD < 10% in patients with BMI ≥ 35. Compared to visual analysis, single-position stress TPD had higher prognostic accuracy in patients with BMI < 30 (AUC .652 vs .631, P < .001) and 30 ≤ BMI < 35 (AUC .660 vs .636, P = .027). Combined TPD had better discrimination than visual analysis in patients with BMI ≥ 35 (AUC .662 vs .615, P = .003). CONCLUSIONS: Automated quantitative methods for SPECT MPI interpretation provide robust risk stratification in the obese population. Combined stress TPD provides additional prognostic accuracy in patients with more significant obesity.
BACKGROUND: Obese patients constitute a substantial proportion of patients referred for SPECT myocardial perfusion imaging (MPI), presenting a challenge of increased soft tissue attenuation. We investigated whether automated quantitative perfusion analysis can stratify risk among different obesity categories and whether two-view acquisition adds to prognostic assessment. METHODS: Participants were categorized according to body mass index (BMI). SPECT MPI was assessed visually and quantified automatically; combined total perfusion deficit (TPD) was evaluated. Kaplan-Meier and Cox proportional hazard analyses were used to assess major adverse cardiac event (MACE) risk. Prognostic accuracy for MACE was also compared. RESULTS: Patients were classified according to BMI: BMI < 30, 30 ≤ BMI < 35, BMI ≥ 35. In adjusted analysis, each category of increasing stress TPD was associated with increased MACE risk, except for 1% ≤ TPD < 5% and 5% ≤ TPD < 10% in patients with BMI ≥ 35. Compared to visual analysis, single-position stress TPD had higher prognostic accuracy in patients with BMI < 30 (AUC .652 vs .631, P < .001) and 30 ≤ BMI < 35 (AUC .660 vs .636, P = .027). Combined TPD had better discrimination than visual analysis in patients with BMI ≥ 35 (AUC .662 vs .615, P = .003). CONCLUSIONS: Automated quantitative methods for SPECT MPI interpretation provide robust risk stratification in the obese population. Combined stress TPD provides additional prognostic accuracy in patients with more significant obesity.
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