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60 An atlas of computed FFR in common patterns of coronary artery disease
  1. Roberto Newcombe1,
  2. Rebecca Gosling1,
  3. Julian Gunn1,
  4. Andrew Narracott1,
  5. Rodney Hose1,
  6. Paul Morris2,
  7. Patricia Lawford1
  1. 1University of Sheffield
  2. 2Department of Infection, Immunity and Cardiovascular Disease, Univeristy of Sheffield

Abstract

Introduction Fractional flow reserve (FFR) is the gold standard method for assessing the physiological significance of coronary artery lesions. A ‘virtual’ FFR can be computed from angiographic images, using computational fluid dynamics, avoiding the need for a pressure wire. FFR is influenced by several factors including; stenosis severity, length of stenosis, vessel size and myocardial resistance. However, how each of these contribute to the overall FFR is not fully understood. We sought to create a range of 3D geometries, with varying characteristics and determine their corresponding vFFRs, to inform clinicians about the impact upon blood flow caused by commonly encountered disease patterns.

Methods Geometries were created using ANSYS Design Modeler™ that included stenoses of different shape, severity, number and length, within straight and branched models using variations on a basic standard vessel size (a rigid tubular 3.5mm diameter main vessel, 50mm long, with any branches obeying Huo-Kassab’s law). vFFR values were calculated using our in-house VIRTUheart™ workflow. Results were displayed in easy-to-understand pictorial form.

Results 187 geometries were created. The total reduction in cross sectional area had the greatest effect on FFR. All 80% concentric stenoses studied had an FFR of <0.80, regardless of shape, length or number of lesions. However, when geometries with the same stenosis severity were compared, multiple lesions, increased lesion length, smaller vessel diameter and lower myocardial resistance were associated with lower FFR values. Using different diameter laws for our branched geometries, resulted in minimal difference to FFR values. Table 1 shows some examples of key FFR results derived from single vessel geometries. Table 1 shows some examples of key FFR results derived from single vessel geometries. Figure 1 and 2 display the geometries that produced these key vFFR results. Figure 1 displays geometries 1-4 and figure 2 geometries 5-8.

Abstract 60 Table 1

Example of some key FFR results obtained from our straight vessel geometries

Abstract 60 Figure 1

Geometries 1-4 with vFFR values. Produced by the VIRTUheart workflow™ in the Department of Infection, Immunity and Cardiovascular Disease, at the University of Sheffield. (Newcombe et al., 2019).

Abstract 60 Figure 2

Geometries 5-8 with vFFR values. Produced by the VIRTUheart workflow™ in the Department of Infection, Immunity and Cardiovascular Disease, at the University of Sheffield. (Newcombe et al., 2019).

Conclusion vFFR is most affected by stenosis severity. However, changes to lesion shape, length, number and vessel diameter also impact vFFR. This data places these variables into perspective for clinicians when judging the significance of lesions in a diseased vessel.

Conflict of Interest None

  • Fractional Flow Reserve
  • coronary artery disease
  • Computational modelling

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