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12/17/16 Cardiac CT, Nuclear Cardiology, and Cardiac MRI DISCLOSURE - PDF document

Oct 04, 2023 •261 likes •639 views

12/17/16 Cardiac CT, Nuclear Cardiology, and Cardiac MRI DISCLOSURE Roles in the Era of Value-based Imaging Daniel S. Berman, M.D. declares the following relationships: Daniel S. Berman, MD Director, Cardiac Imaging Consultant:

  1. 12/17/16 Cardiac CT, Nuclear Cardiology, and Cardiac MRI DISCLOSURE Roles in the Era of Value-based Imaging Daniel S. Berman, M.D. declares the following relationships: Daniel S. Berman, MD Director, Cardiac Imaging Consultant: Molecular-Dynamics Cedars-Sinai Heart Institute Royalties: Cedars-Sinai Medical Center Professor of Medicine Cedars-Sinai Medical Center Research grant: HeartFlow UCSF December 2016 Value-based Cardiac Imaging in CAD • The exciting: –Technology: always improving • The realistic: – Someone is paying for it all – Proven value will be required for its use 1

  2. 12/17/16 Value-based Cardiac Imaging in CAD Value-based Cardiac Imaging in CAD Does the test • The exciting: result in –Technology: always improving improved outcomes or • The realistic: reduce costs? – Someone is paying for it all – Proven value will be required for its use Does the test PREDICT risk? Automatic Quantitative Analysis of SPECT/PET Value-based Cardiac Imaging in CAD • Technologic developments • Applications – Prevention – Acute coronary syndromes 4D M-SPECT – Stable ischemic heart disease Cedars-Sinai Emory Cardiac – Heart failure QGS/QPS/QPET Tool Box • Challenges 2

  3. 12/17/16 Prediction of MACE with SPECT-MPI Prediction of MACE with SPECT-MPI Machine Learning with Nuclear and Clinical Variables Machine Learning with Nuclear and Clinical Variables Otaki…Slomka, et al Otaki…Slomka, et al ASNC 2016 Automated quantitative assessment with machine learning: ASNC 2016 Superior to expert reading alone and will become routine N=2818 N=2818 Mean f/u: 3.0±0.8 years Otaki…Slomka, et al Mean f/u: 3.0±0.8 years Otaki…Slomka, et al MACE: 12% ASNC 2016 MACE: 12% ASNC 2016 CZT Cardiac SPECT Systems SPECT/CT CZT Detectors No PMT • GE sensitivity spatial resolution energy resolution smaller size GE Siemens Philips <1 mSV • Attenuation correction • Coronary artery calcium scoring Einstein JNM 2014 3

  4. 12/17/16 PET/CT Cameras SPECT/CT GE Siemens • GE GE Siemens Philips Philips • Attenuation correction • Coronary artery calcium scoring PET/CT and SPECT/CT PET/CT and SPECT/CT Combining anatomy with function: CAC+ MPI Combining anatomy with function: CAC+ MPI V GFADS STRESS REST REST CAC 0 LAD + LCX stenosis: CABG 4

  5. 12/17/16 Absolute MBF Assessment With Rb-82 PET PET/CT and SPECT/CT Prognostic Value Over Regional Perfusion Combining anatomy with function: CAC+ MPI V GFADS 50 LV GFADS LV VOI RV GFADS 40 RV VOI MYO GFADS 30 MYO VOI 20 REST 10 0 0 20 40 60 80 100 120 Adding CAC to PET/SPECT: Increases diagnostic certainty seconds Detects subclinical atherosclerosis El Fakhri, Sitek, et al. J. Nucl. Med 2005, Brigham & Women’s Hospital; LAD + LCX stenosis: CABG Ziadi et al. JACC 2011 Coronary Flow Reserve (CFR) Predicts Mortality Coronary Flow Reserve (CFR) Predicts Mortality Independent of Perfusion Defects Independent of Perfusion Defects N= 2,783 N= 2,783 CD= 137 CD= 137 Annualized Mortality 12% Annualized Mortality 12% P<0.0001 P<0.0001 10% 10% 8% 8% 6% 6% 4% 4% Lower Tertile Lower Tertile 2% 2% Middle Tertile Middle Tertile 0% 0% Upper Tertile Upper Tertile ≥ 10% ≥ 10% CFR: Adds prognostic information to perfusion defect 1-9% 1-9% 0% 0% Increases certainty; identifies diffuse disease ≥ 10% 1-9% 0% ≥ 10% 1-9% 0% (n) (n) (n) (n) (n) (n) Upper Tertile 2.4% (119) 0.3% (195) 0.1% (614) Upper Tertile 2.4% (119) 0.3% (195) 0.1% (614) Middle Tertile 4.4% (217) 4.0% (202) 1.1% (509) Middle Tertile 4.4% (217) 4.0% (202) 1.1% (509) Lower Tertile 10.2% (416) 6.0% (190) 3.6% (321) Lower Tertile 10.2% (416) 6.0% (190) 3.6% (321) Murthy VL, et al . Circulation 2011 Murthy VL, et al . Circulation 2011 5

  6. 12/17/16 F-18 Sodium Fluoride PET Identifies Ruptured and Nuclear Imaging Targets for Vulnerable Plaque High-Risk Coronary Plaques • 40 AMI • 93% uptake in culprit plaque at ICA • 40 Stable angina • 45% uptake in plaques with high risk features (IVUS) Joshi, Dweck…Newby Lancet 2013 Courtesy: Andreas Kjaer Beautiful Images Value-based Cardiac Imaging in CAD Cardiac CT Who needs them? • Instrumentation/software Will the results affect – Full coverage single beat managment/outcome? – Higher temporal resolution 66 ms – Lower radiation <1 mSv – Model based iterative reconstruction S. Achenbach, MDbb 6

  7. 12/17/16 Positive remodeling (+), Soft plaque (+), Value-based Cardiac Imaging in CAD Fibrous plaque (+), Calcification (-) Cardiac CT ACS • Instrumentation/software – Full coverage single beat – Higher temporal resolution 66 ms – Lower radiation <1 mSv – Model based iterative reconstruction • Assessments – Plaque – Perfusion – Flow LAD Motoyama et al. JACC 2007;50:319-26 Positive remodeling (+), Soft plaque (+), % of Patients Subsequently Having ACS Fibrous plaque (+), Calcification (-) Adverse Plaque Features on CCTA ACS 25 Adverse features (F): 20 positive remodeling 15 low-attenuation plaques % with events 10 5 0 nl 0 1 F 2 F (0/167) F(4/20) (1/27) (10/45) Adverse Plaque Features: positive remodeling, 1,059 pts with CCTA followed up for 27 ± 10 mo LAD low attenuation plaque ACS developed in 15 patients. None had >75% stenosis in the culprit lesion at time of CCTA Motoyama et al. JACC 2007;50:319-26 Motoyama et al. JACC 2009;54:49-57 7

  8. 12/17/16 APFs on CCTA Predict Ischemia High risk plaque Features: Predict ACS HRP and Significant Stenosis: Complementary 81% stenosis 70% stenosis N=3,158; 88 ACS in mean f/u 3.9 ± 2.1 years HRP: positive remodeling or low attenuation plaque; SS: ≥70% stenosis Shmilovich, Cheng, et al., Atherosclerosis 2011 Motoyama, et al JACC 2015 CTA plaque assessment predicts regional PET flow Plaque Features on CCTA Add to Stenosis in Prediction of Impaired PET MFR True positive rate Composite Score 0.83 (0.79-0.91) Stenosis 0.66 (0.57-0.76) p = 0.005* ML of Clinical, stenosis,and plaque variables • 51 patients with coronary CTA and rest-stress 13 N-ammonia PET • Ischemia automatically derived from PET. Plaque analysis by Autoplaq 153 vessels; 51 patients • False positive rate Dey et al Circulation Cardiovascular Imaging, 2015 Noncalcified Plaque Burden • Dey, et al Circ CV Imaging 2015 Collaboration with Dr. Erick Alexanderson, Mexico City Strongest Plaque Variable Dey et al Circulation Cardiovascular Imaging, 2015 8

  9. 12/17/16 Autoplaq: Automated method for quantitative plaque characterization Serial Quantitative Coronary Plaque Assessment • % Diameter Stenosis • % Area Stenosis • Reproducible, quantitative of global plaque burden • NCP, CP, total plaque volume/burden • Assessing response to therapy • Low-density NCP plaque volume/burden • Patient managment and clinical trials • % NCP/Total plaque Volume • CAC not useful for this purpose • % Aggregate plaque volume • Could extend application of CCTA to • Remodeling index asymptomatic patients • Contrast density difference • Minimum luminal area, lesion length Dey et al JCCT 2009, Dey et al JCCT 2014, Diaz Zamudio et al Radiology 2015 CT Perfusion Predicts Ischemia: CTP + CTA CT Perfusion Predicts Ischemia: CTP + CTA CORE320: 64-year-old male with chest pain CORE320: 64-year-old male with chest pain Rochitte C E et al. Eur Heart J 2014 Rochitte C E et al. Eur Heart J 2014 9

  10. 12/17/16 Non-Invasive FFR CT Non-Invasive FFR CT From typically acquired CCTA From typically acquired CCTA • • • Computational fluid dynamics • Computational fluid dynamics • Stenosis • Stenosis • Vessel volume after lesion • Vessel volume after lesion • Myocardial mass distal to • Myocardial mass distal to lesion lesion No additional acquisition, radiation No additional acquisition, radiation • • • No modification to imaging • No modification to imaging protocols protocols No administration of medications No administration of medications • • • Limitations: Must send to HeartFlow • • Significant added expense Source : Min JK et al. J Cardiovasc Comput Tomogr 2011, Min JK et al. Am J Cardiol 2012; Min JK et al. J Cardiovasc Comput Tomogr. Source : Min JK et al. J Cardiovasc Comput Tomogr 2011, Min JK et al. Am J Cardiol 2012; Min JK et al. J Cardiovasc Comput Tomogr. 2012; Grunau GL et al. Curr Cardiol Report; Min JK et al. JAMA 2012; Koo et al. J Am Coll Cardiol 2012 2012; Grunau GL et al. Curr Cardiol Report; Min JK et al. JAMA 2012; Koo et al. J Am Coll Cardiol 2012 FFR CT for Lesion-Specific Ischemia NXT Per-Vessel: FFR CT vs FFR FFR CT CT ICA and FFR FFR CT <.80 N=484 CT >50 ICA ≥50 Case 1 FFR 0.65 FFR CT 0.62 LAD stenosis = Lesion-specific ischemia = Lesion-specific ischemia FFR CT ICA and FFR CT Case 2 FFR CT diagnostic accuracy superior to both CT and ICA stenosis FFR 0.86 FFR CT 0.87 RCA stenosis = No ischemia = No ischemia Norgaard et al JACC 201 10

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