PCI & AimRadial 2018 | FFR-CT - Colin Berry
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PCI & AimRadial 2018 | FFR-CT - Colin Berry
- 1. FFR-CT Prof. Colin Berry Golden Jubilee National Hospital University of Glasgow, UK. AIMRADIAL – Westin Sarasota 29 November 2018
- 2. Disclosures Institutional agreements between the University of Glasgow (employer) and Abbott Vascular, AstraZeneca, Coroventis, DalCor, GSK, HeartFlow, Novartis, Philips and Siemens Healthcare. TSC Member, PRECISION, DECISION Trials
- 3. 1. Principles 2. Clinical studies 3. New methods: CT-FFR FFR-CT
- 4. FFR-CT increases positive predictive value of CTCA Norgaard BL, Taylor CA et al Curr Cardiovasc Imaging Rep 2016 FFR-CT: Non-invasive computation of FFR in all major coronary arteries, personalised models
- 5. FFR-CT vs. CTCA Landmark diagnostic accuracy studies Knappen et al EuroPCR.2018
- 8. Dreissen, Knappen et al PACIFIC, EuroPCR.2018
- 9. PACIFIC Dreissen, Knappen et al PACIFIC, EuroPCR.2018
- 10. PACIFIC Dreissen, Knappen et al PACIFIC, EuroPCR.2018
- 11. PACIFIC - Conclusions Dreissen, Knappen et al PACIFIC, EuroPCR.2018
- 12. NHS Chest Pain Clinic, n = 1400 Primary endpoint: resource utilisation at 9 months including non- invasive cardiac investigations, invasive coronary angiography (ICA), coronary revascularization, hospitalisation for cardiac events and cardiac medications PIs: Curzen, Berry, Hlatky et al The FORECAST Trial: FFR Derived from CTCA in the Assessment and Management of Stable Chest Pain IIS grant
- 13. PRECISION (PI, Dr Pam Douglas) Prospective Randomized Trial of the Optimal Evaluation of Cardiac Symptoms and Revascularization. Stable chest pain, clinic Precision strategy FFR-CT vs. SoC stress test strategy; health outcomes DECISION (PI, Dr Gregg Stone) Decisive evaluation of cardiac ischemia, symptoms and revascularisation FFR-CT vs. invasive angiography; ~ 5000 patients; health outcome trial Upcoming clinical trials
- 14. • Stress – force per unit area, dynes/cm2 • Wall shear stress (WSS) – tangential force per unit area acting at endothelium; related to flow and viscosity • Axial plaque stress (APS) – at the plaque; related to pressure, transmural, ~x50 vs. WSS • Derived from patient-specific computed models • Clinical significance APS – EMERALD study Coronary biomechanics
- 15. FFR-CT: Coronary biomechanics Case example: cardiac arrest survivor Norgaard BL, Taylor CA et al Curr Cardiovasc Imaging Rep 2016
- 16. Novel applications in development – PCI planner Kim KH et al JACC CV Interv 2014 FFR-CT: Guide revascularisation FFR: Stenosis pre Simulation Post-PCI
- 17. Other technologies CT-FFR AIMRADIAL – Westin Sarasota 29 November 2018
- 19. Ko B et al JACC CV Imaging 2017 CT- FFR
- 20. CT- FFR Ko B et al JACC CV Imaging 2017User generated FFR, ~1 hr
- 22. Distribution of FFR Study design 166 consecutive patients CTCA + ICA + FFR
- 24. CT- FFR
- 25. Conclusions 1. FFR-CT, derived from patient-specific CT- based computational models is validated , quality assured, and clinically useful 2. Clinical trials of FFR-CT on-going 3. Novel emerging applications: PCI planner 4. New methods: CT-FFR ( time, ? Accuracy)
- 26. Thank you for your attention
Editor's Notes
- FFR can be derived from coronary CTA image data acquired using standard acquisition protocols without the need for additional imaging, medication, or radiation. FFRCT analysis uses mathematical models of blood flow derived from patient-specific data extracted from coronary CTA images and solved on high-performance computers. Any mathematical model of blood flow in the circulation includes at least 3 elements: first, a description of the anatomic region of interest; second, the mathematical “governing equations” enumerating the physical laws of blood flow within the region of interest; and third, “boundary conditions” to define physiologic relationships between variables at the boundaries of the region of interest [21]. Although the anatomic region of interest and the boundary conditions are unique to each patient and the specific vascular territory, the governing equations describing velocity and pressure are universal and apply in different patients and other arterial beds. The extraction of the patient-specific anatomic model from image data is performed using image processing algorithms [22, 23]. The basic physiologic principles behind FFRCT have previously been described in detail [20••]. The first principle is that the total coronary blood flow (which is proportional to the myocardial oxygen demand) at rest can be quantified from the myocardial mass as assessed by CT. The second principle is that the microcirculatory vascular resistance at rest is inversely proportional to the size of the coronary arteries supplying the myocardium, and thus the caliber of both healthy and diseased vessels adapt to the amount of flow they carry. The third principle states that the vasodilatory response of the coronary microcirculation to adenosine infusion is predictable, allowing computational modeling of the maximal hyperemic state. Integration of these patients-pecific mathematical models of coronary physiology to 3D computational fluid models enable computation of coronary flow and pressure at each point in the coronary tree under hyperemic conditions. Finally, FFRCT is calculated from the ratio of coronary pressure to aortic pressure under simulated maximal hyperemic conditions.