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34 Diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1- dimensional) flow modelling
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  1. Kevin Mohee1,
  2. Jonathan Mynard2,
  3. Gauravsingh Dhunnoo3,
  4. Rhodri Davies3,
  5. Perumal Nithiarasu4,
  6. Julian PJ Halcox5,
  7. Daniel R Obaid3
  1. 1Swansea Bay University Health Board
  2. 2Heart Research, Murdoch Childrens Research Institute, Parkville, VIC, 3052, Australia
  3. 3Department of Cardiology, Morriston Hospital, Swansea Bay University Health Board, Swansea, UK
  4. 4Biomedical Engineering Group, Zienkiewicz Centre for Computational Engineering, College of Engin
  5. 5Swansea University Medical School, Swansea, UK

Abstract

Introduction Fractional flow reserve (FFR) improves the assessment of the physiological significance of coronary lesions compared with conventional angiography. However, it requires additional interventional techniques and equipment to perform. Recently proposed virtual functional indices are derived from coronary imaging alone but require complex computational fluid dynamics modelling, which is time-consuming and hence cannot influence immediate clinical management. We tested the diagnostic performance of a virtual FFR (1D-vFFR) using routine angiographic images and a rapidly performed reduced order computational model.

Methods Quantitative coronary angiography (QCA) was performed in 102 vessels (85 patients) with coronary lesions assessed by invasive FFR. A 1D-vFFR for each lesion was created using reduced order (one-dimensional) computational flow modelling based on parameters derived from conventional angiographic images and patient specific estimates of coronary flow. The diagnostic accuracy of 1D-vFFR and QCA derived stenosis was compared against the gold standard of invasive FFR using area under the receiver operator characteristic curve (AUC).

Results QCA revealed the mean coronary stenosis diameter was 44% ± 12% and lesion length 13 ± 7 mm. Once angiographic analysis of the coronary artery had been performed calculation of the 1D-vFFR took less than one minute. Coronary stenosis (QCA) had a significant but weak correlation with FFR (r=-0.2, p= 0.04) and poor diagnostic performance to identify lesions with FFR <0.80 (AUC 0.39, p=0.09), (sensitivity – 58% and specificity – 26% at a QCA stenosis of 50%). In contrast, 1D-vFFR had a better correlation with FFR (r=0.32, p=0.01) and significantly better diagnostic performance (AUC 0.67, p=0.007), (sensitivity – 92% and specificity - 29% at a 1D-vFFR of 0.7).

Conclusions 1D-vFFR improves the determination of functionally significant coronary lesions compared with conventional angiography without requiring a pressure-wire or hyperaemia induction. It is fast enough to influence immediate clinical decision-making but requires further clinical evaluation.

Conflict of Interest None

  • coronary physiology
  • FFR
  • virtual

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